Next Generation Data Analytics: Data as a Strategic Currency

Transcription

1 Next Generation Data Analytics: Data as a Strategic Currency July 2014 Joel P. Fishbein, Jr BMO Capital Markets Corp. [email protected] (212) Brett Fodero BMO Capital Markets Corp. [email protected] (212) Edward Parker BMO Capital Markets Corp. [email protected] (212) Refer to pages for Important Disclosures, including Analyst s Certification. For Important Disclosures on the stocks discussed in this report, please go to

2 BMO Capital Markets A member of BMO Financial Group 2 July 17, 2014

3 BMO Capital Markets Table of Contents...5 Stock Positioning: we like Tableau...6 Industry Overview...8 Big Data: Cutting Through the Noise...13 Big Data Spending Will Initially Be Focused on Backend Enabling Technologies...22 Operational Intelligence: Emerging Market Expanding From Machine to Relational Data Sets...27 Business Intelligence and Analytics: Decentralization...29 Performance and Sentiment...39 Company Comparables...41 Billion-Dollar Private Company Valuations...43 Key Private Security Companies to Watch...44 Models...98 Glossary Tableau Software Investment Drivers Company Background Market Backdrop Balance Sheet and Capital Allocation Current Outlook Valuation Risks Financial Models Qlik Technologies Investment Drivers Company Background Market Backdrop Balance Sheet and Capital Allocation Current Outlook Valuation Risks Financial Models Splunk Details & Analysis Industry Backdrop Product Pricing: Near-Term Noise Offset by Long-Term Accretion Distribution: Capacity Constraints a Hurdle and Opportunity Operational Intelligence Market Current Outlook Balance Sheet Valuation Risks Financial Models A member of BMO Financial Group 3 July 17, 2014

4 BMO Capital Markets A member of BMO Financial Group 4 July 17, 2014

5 Industry Rating: Market Perform July 17, 2014 Joel P. Fishbein, Jr BMO Capital Markets Corp. Brett Fodero / Edward Parker BMO Capital Markets Corp / [email protected]/[email protected] Next Generation Data Analytics: Data as a Strategic Currency Organizations have spent tens of billions of dollars over the years to analyze their structured data sources. Moreover, the continued rise of the digital economy is expected to lead to 50x growth in digital data creation from 2010 to Despite organizations investments, only 0.05% of digital data created is being captured for use in the real time reporting and analytics process. The growth in computational power has now reached the point where it is becoming feasible to gather, store, and analyze huge troves of data in useful ways that can unlock value. Data has become a currency to create better products and services, make R&D more productive, establish new business models, improve the quality of customer interactions, and create a competitive advantage to take market share. This avalanche of data has given rise to the much hyped concept of Big Data, which has helped elevate business intelligence and analytics again as the number one IT spending priority, as measured by Gartner. Technologies such as Hadoop, NoSQL and in-memory databases, and cloud computing can now be utilized to enable underlying infrastructure to analyze data through a multitude of new emerging Business Intelligence applications. Still, organizations are in the process of separating hype from reality in an effort to adapt and take advantage of the Big Data trend. Doing so requires a major commitment in terms of significant investments in people, data management infrastructure, and related consumption applications. Moreover, it is leading to a fundamental shift in thinking about data use and decision making, not just from an IT department perspective, but also from a management and operational perspective. As such, budgets available for analytics are being found in many different places, from IT, which controls the data, to individual lines of business. Summary "Big Data" has become a ubiquitous marketing term that, at its core, refers to datasets whose size is beyond the ability of traditional IT assets, processes, and applications to cost effectively capture, store, manage, and analyze "Structured" data (traditional business data), "Unstructured" data (web-based data, files, media), and "Machine-generated" data (machine logs). Big Data is evolutionary to traditional data analytics infrastructure, and Gartner expects that through 2020, more than 90% of Big Data implementations will augment not replace existing data warehouse and business intelligence (BI) deployments. Near term, much of the focus of Big Data will be on backend systems and processes, which is a necessary step for enabling front-end applications and analysis. The Operational Intelligence market is expanding from operational to business analytics. The $15 billion BI market is transitioning to new user driven requirements resulting in share losses for all traditional BI vendors by emerging vendors such as Tableau and Qlik. We are Outperform rated on Tableau (DATA) and Market Perform rated on both Qlik (QLIK) and Splunk (SPLK). Page 5 July 17, 2014

6 Stock Positioning: we like Tableau We are initiating coverage of the Data Analytics sector with an Outperform rating on Tableau (DATA) and Market Perform ratings on both Qlik (QLIK) and Splunk (SPLK). Tableau (DATA - Outperform) - We believe Tableau is one of the best positioned companies in the analytics space with highly differentiated offerings that will experience sustained growth by democratizing data. Tableau has the most upside of the analytics vendors, and we see current out year top line consensus estimates as potentially 10-20% conservative. Shares have pulled in significantly off their high and the multiple has compressed to be in line with peers, which we believe the stock can hold given what we see as the potential for +40% for the next few years in an upside case. We see a favorable setup for the shares. Qlik (QLIK Market Perform) - Qlik has been an early pioneer leading the shift to next generation data discovery Business Intelligence tools, and customer loyalty is solid. A history of mixed execution continues to be an overhang on the shares, and we are concerned about the effects of the QlikView.Next launch in the short and medium term. While we are positive on the technology improvements, we are concerned about the impact plans for dual product and pricing models could have on customer adoption and sales cycles. Additionally, anticipation for QlikView.Next has been building for the better part of two years, and we are cautious on the potential pause ahead of the release slated for 2H14. Our estimates are below the Street's. We look to evaluate sales execution and customer adoption around the pending QlikView.Next product cycle. Splunk (SPLK Market Perform) - Splunk is in the midst of transitioning from being a pioneer in the nascent Operational Intelligence market into a broader data analytics platform. We view FY2015 as a transitional year. Near term we are concerned that the shift from transactional to more enterprise agreements could weigh on sales cycles until a repeatable pricing model is developed. Consensus estimates appear attainable, but we don t see material upside likely keeping shares range bound despite our view that competition concerns are overblown and market opportunity significant. Shares are trading at a premium multiple, and we see an unfavorable setup. Exhibit 1 provides a scorecard of our analytics coverage universe. Recognizing the limitations in assigning weights/scores, we view this as a framework for providing investors a quick reference sheet to illustrate relative (not absolute) positioning of fundamentals and valuations. Our scorecard is based on a weighted relative rankings (5 is most favorable while 1 is the least favorable) that takes into account Fundamental (Technology, Total Addressable Market, Competitive Strength, Execution) and Valuation (Growth, Margin Leverage, Upside to Consensus, Relative Valuation) factors. Page 6 July 17, 2014

7 Exhibit 1. Analytics Coverage Scorecard FUNDAMENTAL Competitive SCORECARD Variables Technology TAM Strength Execution Total Notes: Weight 20% 30% 20% 30% 100% Tableau Software Inc DATA Differentiated through Live Query Engine; VizQL best in class. - Expanding addressable market for BI. Increased competition long term. - Solid management team and execution. Splunk Inc SPLK Differentiated technology; under distribution its biggest constraint. - Leader in greenfield Operational Intelligence market, and expanding into more competitive broader data analytics. - Seasoned management team with good track record for execution. Qlik Technologies Inc QLIK Early leader in Data Discovery market; QlikView.Next addressing deeper Business Intelligence use cases. - More competitive against traditional BI tools and business analysts. - Execution historically mixed; concerned over upcoming product cycle. VALUATION Margin Upside to Relative SCORECARD Variables Growth Leverage Consensus Valuation Total Notes: Weight 30% 15% 20% 35% 100% Tableau Software Inc DATA We see current out year consensus estimates potentially 10-20% conservative, and should our upside case play out we expect top line upside to drop to the bottom line. Splunk Inc SPLK Consensus estimates appear attainable but we don t see material upside. Near term we are concerned that the shift from transactional to more enterprise agreements could weigh on sales cycles until a repeatable pricing model is developed. Qlik Technologies Inc QLIK Concerned over the effects of the QlikView.Next launch on execution and growth in the short and medium term. S&M expenses are high (>50% revenue) relative to growth (<20%) and the company needs to re-accelerate growth in order to expand margins. TOTAL AVERAGE FY1 FY2 SCORECARD Variables Rating EV/Sales EV/Sales Average Notes: Tableau Software Inc DATA Outperform 9.1x 6.7x We see a favorable set up for shares. - Tableau has the most upside of the analytics vendors, and we believe the stock can hold a peer average multiple given what we see as the potential for +40% for the next few years in an upside case. Splunk Inc Qlik Technologies Inc SPLK QLIK Market Perform Market Perform Source: BMO Capital Markets Research, Thomson Reuters. 11.1x 8.4x x 2.6x Unfavorable setup with a premium multiple in our view. - Near term slower than expected growth likely keeps shares range bound despite our view that competition concerns are overblown. - Balanced risk reward. More attractive at ~2.0x. - While we are positive on the technology improvements we are concerned over the impact plans for dual product and pricing models could have on customer adoption and sales cycles Page 7 July 17, 2014

8 Industry Overview Corporate data holds insights that can drive competitive advantages and the collection and analysis of data in organizations is not a new phenomenon. Data warehouses have been in use since the 1980s, Business Intelligence tools have been deployed since the 1990s, and organizations have spent tens of billions of dollars (~$15 billion in Business Intelligence spending in FY15 forecast) to analyze their structured data sources. However, despite this investment, only 0.05% of digital data created is being captured for use in the real time reporting and analytics process. Today, the game is changing. The growth in computational power has now reached the point where it is now becoming feasible to gather, store, and analyze huge troves of data in useful ways that can unlock value. Moreover, the continued rise of the digital economy is creating opportunities to tap into new sources of data generated outside of the confines of the enterprise sensors, transactions, GPS trackers, medical and legal records, videos, and electronic payments are all generating significant amounts of digital information that hold potentially valuable insight. This machine-generated data is projected to increase 15x by 2020, representing 40% of the digital data universe. Data has become a currency to create better products and services, make R&D more productive, establish new business models, improve the quality of customer interactions, and create a competitive advantage to take market share. According to McKinsey, the effects of these improvements will be felt the greatest in retail, manufacturing, healthcare, and government services, with as estimated $610 billion in annual productivity gains and cost savings. Specifically, $325 billion in incremental annual GDP could be driven from big data analytics in retail and manufacturing. Exhibit 2. 50x Growth in Digital Creation in Zettabyte (ZB) Digital Data Created Machine generated data is a key driver in the growth of the world s data which is projected to increase 15x by 2020 (representing 40% of the digital universe) Source: BMO Capital Markets Research, IDC Page 8 July 17, 2014

9 This avalanche of data has given rise to the much hyped concept of Big Data, which has helped elevate business intelligence and analytics again as the number one IT spending priority, as measured by Gartner. Still, organizations are still in the process of separating hype from reality in an effort to adapt and take advantage of the Big Data trend. Doing so requires a major commitment in terms of significant investments in people, data management infrastructure, and related consumption applications. Moreover, it is leading to a fundamental shift in thinking about data use and decision making, not just from an IT department perspective, but also from a management and operational perspective. As such, budgets available for analytics are being found in many different places, from IT, which controls the data, to individual lines of business. Exhibit 3. Analytics and Business Intelligence a Top Priority for CIOs Source: Gartner. Key Drivers A Boom in Digital Creation As we think about Big Data and the new crop of tools and capabilities required to make sense of it, we consider some key drivers. IDC estimates that only 0.5% of the world s data is being analyzed, and 3% is being tagged. IDC projects that the digital universe will reach 40 zettabytes (ZB) by 2020, resulting in 50x growth from the beginning of 2010, equivalent to 5,247 GB per person worldwide. Machine-generated data is a key driver in the growth of the world s data which is projected to increase 15x by 2020 (representing 40% of the digital universe). The McKinsey Global Institute estimates that data volume has been growing 40% per year, and will grow 44 times this rate between 2009 and According to Gartner, unstructured data doubles every three months and seven million Web pages are added every day. IDC estimates that 32% of the world s total data assets reside in the U.S. Page 9 July 17, 2014

10 The computing power of the average desktop computer has increased by 75 times from 2000 to MGI research has estimated that by 2018, the U.S. will face a shortage of up to 190,000 data scientists with advanced training in statistics and machine learning and this specialty requires years of study. Ninety percent of the world s data was created in the past two years; 80% of this is unstructured. The average time users spend online in the U.S. has increased from an average of 5.2 hours a week in 2001 to 19.6 hours in In 2012, Internet users generated 4 exabytes of data, fed by more than one billion computers and one billion smartphones. Over 50% of internet connections are things. In 2011 there were 15 billion-plus permanent and 50 billion-plus intermittent and that is expected to rise by 2020 to 30 billion-plus permanent and more than 200 billion intermittent. Exhibit 4. Number of Connected Nodes to Grow at a +35% CAGR Source: BMO Capital Markets Research, McKinsey. Key Industry Points The term Big Data has become a ubiquitous marketing term so any discussion must start with a definition. At its core, Big Data refers to datasets whose size is beyond the ability of traditional IT assets, processes, and applications to cost effectively capture, store, manage, and Page 10 July 17, 2014

11 analyze. The premise of Big Data is that businesses can extract business decision making insight from transactional or structured data (traditional business data), unstructured data (web-based data, files, media), and machine-generated data (machine logs),with the goal of affecting business outcomes positively. Big Data enabled by the emergence of multiple trends. The rise of Big Data has created the need for new tools that are optimized to capture, aggregate, manage, protect and analyze data at scale. Traditional enterprise IT systems RDBMS, scale-up storage and computing, traditional ETL are simply not equipped to cost effectively manage and leverage Big Data. Two major technology trends currently enable Big Data Cloud Computing and Hadoop/NoSQL by providing the underlying structure and capacity to execute analysis of massive data sets in a parallel/distributed fashion. Big Data is evolutionary to traditional data management infrastructure, and Gartner expects that through 2020, more than 90% of big data implementations will augment not replace existing data warehouse and business intelligence deployments. Big Data spending will initially be focused on backend enabling technologies. Big Data is still very much evolving, in part because of the ongoing maturation/evolution of open-source projects, the massive inertia of existing data architectures, and ongoing confusion over Big Data. Furthermore, as we discuss, Big Data is not a discrete solution or technology but a multitude of technologies under a large umbrella. In this way, Big Data spending in many ways draws parallels to security spending a multitude of problems and challenges that require a wide variety of different solutions. Currently, IT is often a bottleneck for broader adoption due to internal politics and the lack of a cohesive data management initiative. These factors in concert will provide a modest headwind to overall BI and analytics software spending growth over the next several years. Near term, much of the focus of Big Data will be on backend systems and processes, which is a necessary step for enabling front-end Big Data applications. Market growth rates reflect these trends. For example, in 2013, spending on RDBMS, Data Integration Tools, and Data Quality Tools grew at 7%, 9%, and 14%, respectively, faster than the 7% spending growth on Business Intelligence applications. We expect this trend to continue through 2017, at which time spending on front-end application will likely accelerate. Hadoop/NoSQL, also instrumental in enabling next generation predictive analytics applications, will also see faster growth (~35%) than the Business Intelligence/Data Discovery market (~25%). Operational Intelligence market expanding from operational to business analytics. Despite the significant investment in analytics, only 0.05% of digital data created is being captured for use in the real time reporting and analytics process. Operational Intelligence (OI) solutions run query analysis against machine logs, live feeds and event data to deliver real-time visibility and insight into business and IT operations, enabling people to make better, faster decisions. Contrary to Business Intelligence, which is data centric, OI is primarily activity-centric. By leveraging machine data, Operational Intelligence vendors can touch and monetizes more forms of data than can the emerging Data Discovery BI vendors, which are more focused on new delivery mechanisms to extend traditional relational BI data sets. Given the broad use cases associated with OI, budget for related solutions is spread across infrastructure, security, operations, and analytics spending. This represents a challenge for OI vendors. The market for the core machine log management market is nascent but has substantial potential with machine-generated data projected to increase 15x by 2020 (from 2010). Pricing per GB capacity naturally reduces with scale and given the capacity-based model of most vendors, we believe the opportunity to be worth multiple billions of dollars. Splunk is the only pure play investment in this trend. Page 11 July 17, 2014

12 Business Intelligence and Analytics market transitioning to new user driven requirements to unlock the value of data. The $15 billion dollar business intelligence market is going through a significant transition, driven by evolving business user requirements and enabled by advances in in-memory and data discovery/visualization technology. This is illustrated in Gartner s market share data, which shows traditional BI vendors commanding 64% of the market but only growing 4.5%, below the market growth rate of 7.9%. In suit, the BI market is shifting from rearwardlooking centralized reporting of the past to forward-looking decentralized near-real time predictive analysis. Legacy BI tools have not lived up to their promises, particularly around ROI, as consolidation in the space (IBM/Cognos, Oracle/Hyperion, SAP/BusinessObjects) has not reduced complexity and has in fact slowed innovation. A new breed of vendors such as Tableau and Qlik has commoditized traditional reporting/query tools. The main three data discovery vendors Qlik, Tableau, and Tibco Spotfire control ~75% of the ~$1 billion market; however, the data discovery market accounts for only ~8% of the total BI market. Importantly, the consumerization of BI technology is in some cases shifting the end user from IT analysts to business users, which in effect is expanding the market opportunity. Gartner predicts that, by 2014, 40% of BI purchasing will be business-led rather than IT led. With that said, we don t view the market as a zero-sum game for incumbents that are coming to market with competitive tools, which will lead to an increasingly competitive battle for customer wallet share. Gartner expects that less than 25% of enterprises will fully replace their existing BI solutions. What about Cloud Business Intelligence? We expect cloud BI to emerge as a growing trend in the years to come. To date, cloud-based delivery has not been a popular option, representing less than 5% of overall BI spending today. However, the market is growing, up 42% in 2013, and we expect this trend to continue. The primary driver for increasing cloud deployment is that the percentage of data creation occurring off-premise, beyond the firewall, is growing. As this broad secular trend continues, data gravity will pull more workloads, services, and applications, including BI, into the cloud where the data is created and residing. Trust has been and continues to be a hurdle to cloud adoption, but there are indications that this is changing. Forty-five percent of Gartner s recent Magic Quadrant survey respondents noted a willingness to put mission-critical BI in the cloud, compared to thirty three percent in As with most applications, the promise of increased collaboration with customers and partners and mobility are drivers of Cloud BI. Over time, we expect the proliferation of connected devices (the Internet of Things ) and continued growth in cloud services (SaaS, PaaS, IaaS) will create more cloud-based data and drive the adoption of cloud BI solutions. Cloud Business Intelligence vendors including Birst, GoodData, Looker, Domo, Adaptive Insights have all garnered significant amounts of venture funding. Billion-dollar private company valuations. A tremendous amount of capital has been committed by venture and strategic investors within the big data ecosystem. According to CB insights, $4.9 billion was invested in Big Data companies in , and $3.6 billion in 2013 alone. By our count, more than 35 companies have raised over $50 million and more than 10 have raised over $100 million. We expect that several of these companies will make their way to the public markets, with M&A being the primary exit for the sector. Companies have staked their positions and the question is, which emerge as the leaders and which investments do not pan out, given the competitive nature of the business. Page 12 July 17, 2014

13 Big Data: Cutting Through the Noise The term Big Data has become a ubiquitous marketing term so any discussion must start with a definition. At its core, Big Data refers to datasets whose size is beyond the ability of traditional IT assets, processes, and applications to cost effectively capture, store, manage, and analyze. The premise of Big Data is that businesses can extract business decision-making insight from transactional or structured data (traditional business data), unstructured data (web-based data, files, media), and machine-generated data (machine logs), with the goal of affecting business outcomes positively. The characteristics of data in today s world are changing in several dimensions, referred to as the three Vs, which require a shift from computational to data-intensive technologies. Volume (records, transactions, tables, files). Jet engine, website interactions, automated trading, bioinformatics. Velocity (batch, near-time, real-time, streams). Online ad serving, customer scoring probabilities, streaming data. Variety (structured, unstructured, semi-structured, mixed). RFID, sensors, mobile payments, in-vehicle tracking, social streams. Exhibit 5. Opportunities Lie in Insight Discovery and Decision Making Source: Gartner (May 2012). Penetration rates remain low, with less than 10% of companies deploying a Big Data solution. Penetration Remains Low Penetration remains low, with 8% of organizations surveyed having deployed a Big Data solution, but continuing to rise. Sixty-four percent of organizations are investing or planning to invest in Big Data technology, compared with 58% in 2012, based on a Gartner Survey. It is our view that organizations with deep business intelligence and data management experience will be able to adopt Big Data in the near term as the underlying technology of Big Data is complex and requires both people and IT business intelligence know-how. Page 13 July 17, 2014

14 Exhibit 6. Big Data Investments Increasing Source: Gartner (September 2013). Not a Discrete Market Big Data is less about technology itself but more about the real-time use of data and the application of insights; we do not believe that Big Data represents a discrete market segment in itself that one can easily size. In fact, according to Gartner, by 2020, Big Data spending will become almost indistinguishable from traditional IT spending. Big Data is in its early stages and will touch many aspects of IT and the way companies do business. These technologies are not really replacing incumbents such as business intelligence, relational database management systems, and enterprise data warehouses. Instead, they supplement traditional information management and analytics. Why Now? Only recently have enabling technologies been developed to collect, store, and analyze large structured/unstructured data sets efficiently. One of the most prominent trends is the sustained reduction in the cost of computing power and digital storage, which has been instrumental in both driving digital data creation and providing the capabilities to capture and analyze this newly created data. Big Data also brings capabilities to analyze new types of data that were previously unavailable to enterprises due to the nature of the data itself (unstructured data) and/or the volume of data (sensor feeds). The Internet of Things exploits a wide range of technologies that enable refinements to current business models and open up entirely new business opportunities. Machine data and digital exhaust are two types of data that hold significant amounts of value that until recently have fallen by the wayside because a traditional RDBMS isn t optimized to manage data on this scale. Page 14 July 17, 2014

15 Machine data is one of the fastest growing, most complex (lacks standardized formatting) and most valuable (contains a categorical record) segments of Big Data, and is projected to increase 15x by 2020, representing 40% of the digital data universe, according to IDC. Machine data affects almost every industry and business unit, encompassing GPS, RFID, Hypervisor, Web Servers, , Messaging Clickstreams, Mobile, Telephony, IVR, Databases, Sensors, Telematics, Storage, Servers, Security Devices, Desktops, temperature, alarms, alerts, etc. that generate massive streams of data, in an array of unpredictable formats that are difficult to process and analyze by traditional methods or in a timely manner. The continued rise of the digital economy is creating a trail of digital exhaust unstructured information/data that is a byproduct of the online activities of internet users. Examples include Amazon/eBay transactions, shipping records, Twitter feeds, LinkedIn data, YouTube views, Facebook interactions, and general web page views. This data had previously been considered insignificant because value could not be extracted through traditional OLAP systems and was typically disregarded. However, with systems based on newer data management paradigms such as NoSQL, examining this data is now possible and can provide insight into user and customer behavior and preferences. Additionally, traditional transactional business data (financial records, business transaction records, s, contract data, customer service calls, etc.) remains a substantial opportunity because we don t believe the majority of business data created is being captured for use in the real time reporting and analytics process. This dark data is typically stored during the course of normal business activity but isn t utilized for analytics or monetization. This type of data is currently one of the top Big Data use cases because the data is already in-house and underutilized, and bringing it into the analytics process has lower initial startup costs versus analyzing machine data and digital exhaust. Exhibit 7. Traditional Data Types Still Lead Use Cases for Big Data Analysis Source: BMO Capital Markets Research, Gartner Page 15 July 17, 2014

16 Evolving Use Cases: Focus on Insight Discovery and Decision Making Big Data is in its early stages of development relative to its potential. Over time, Big Data will touch many aspects of IT and influence how companies do business. The early adopters believe that adopting Big Data will put them at a competitive advantage over their peers. Ultimately, Big Data s goal is to become brain-like, with the ability to produce real-time actionable insights based on correlation of data from multiple sources, regardless of their complexity. Insight discovery (45%) and decision making (43%) are the biggest opportunities for Big Data. This is illustrated in a recent survey ranking the top priorities for Big Data, as seen in Exhibit 10 below: enhancing customer experience, process efficiency, new products/business models, and more targeted marketing. Using data to gain insight into customer behavior is growing more vital, especially in sectors such as retail, financial services, information, and manufacturing, all of which have highly competitive consumer-facing businesses. Exhibit 8. Top Priorities for Big Data Source: Gartner (September 2013). Dark Data Use Cases Some examples of areas where dark data can provide competitive insight include the following: Pricing. Pricing transparency by product can enable more targeted price setting based on customer loyalty or price sensitivity, which can improve customer lifetime value. Demandware, NetSuite, and ChannelAdvisor are examples of applications that play in this area. Oracle s Customer Experience cloud helps customer vary pricing on airline tickets targeted at individual persons. Campaign lead generation. The identification of leads that are most likely to result in incremental sales. Application providers are utilizing analytics tools to provide this functionality inside of their marketing clouds. Pentaho Business Analytics integrates into ExactTarget s on-demand interactive marketing platform to power its Report Builder application. This allows ExactTarget to create more relevant and targeted cross-channel marketing campaigns across , mobile, social media, and the Web. ExactTarget can now Page 16 July 17, 2014

17 offer an integrated data analysis and reporting solution that allows marketers to explore interactive marketing metrics in real time. Exhibit 9. Big Data in Retail Source: BMO Capital Markets Research, McKinsey. Unstructured Data Use Cases Customer experience. Knowing customers' preferences at whatever location they are around the world can help improve a consumer s experience with a brand. Combining a batch processing method (Hadoop) to identify at-risk customers with real-time data (in-memory), such as call center interactions, new purchase history, or usage activity, allows enterprises to be proactive with retention efforts. For example, NetFlix uses DataStax NoSQL database to help power its personalized recommendation engine. Discount targeting. Using location or transactional spending data to offer discount coupons redeemable at the nearest store. Retailers have been recording transactions for decades. Now they can gain new insights from radio-frequency identification chips embedded within products, location-based smartphone tracking, in-store customer behavior analysis captured on video and via sensors, and customer online searches and reviews. Macys reduced the time to price items from 27 hours to about one hour and reduced hardware costs 70% through a combination of Hadoop, R, Impala, SAS, Vertica, and Tableau. Web targeting. Using browsing history to target a web site visitor with relevant advertising. Adobe s Test and Target and content management systems focus on this. Oracle recently acquired BlueKai to enrich the data it can analyze. Streamlining processes. In-manufacturing analytics can streamline R&D, production, and supply-chain management. Most modern equipment generates data that can be analyzed in real time on the production floor to enhance productivity. Advanced demand forecasting gives manufacturers the tools to take real-time inputs from their supplier customers (location of trucks, mileage, weather conditions) to more accurately estimate the quantity of demand but also exactly where products will be needed. For example, almost every part of a new Boeing commercial airliner records information and in some cases sends continuous streams of data about its status to GE, one of three major jet engine manufacturers, which has said Page 17 July 17, 2014

18 that, in a single flight, one of its turbofans generates half a terabyte of data, and it is using this data to predict maintenance. Salesforce.com improved application troubleshooting time by 96% and improved application performance for their customers through implementing Splunk. Credit line management. Learning the right credit line to maximize profitability and losses for a consumer. Fraud prevention. By matching the location of a mobile phone with a credit for debit transaction Exhibit Common Hadoop Use Cases and Data Types Source: Gartner, Hortonworks. Big Data Enabled by the Emergence of Multiple Trends The rise of Big Data has created the need for new tools that are optimized to capture, aggregate, manage, protect, and analyze data at scale. Traditional enterprise IT systems RDBMS, scale-up storage and computing, traditional ETL are simply not equipped to cost effectively manage and leverage Big Data.. Two major technology trends currently enabling Big Data Cloud Computing and Hadoop/NoSQL provide the underlying structure and capacity to execute analysis of massive data sets in a parallel/distributed fashion. Page 18 July 17, 2014

19 Exhibit 11. Technologies Used to Derive Value From Big Data Source: Gartner (September 2013). Cloud Computing: Enabling Access to Scalable Computing and Storage Resources Cloud Computing infrastructure allows users to access scalable and elastic computing and storage resources as needed and expand it rapidly to the enormous scale required to process big data sets and run complicated mathematical models. Services can be deployed without setting up the underlying infrastructure internally, enabling the creation of new companies specializing in data services. For Example, Bristol Myers Squibb is conducting clinical trials on AWS and now bills only 22% of billable hours used on its prior infrastructure. It has been able to save 98% of the time to conduct clinical trial simulations (1.2 hours vs 60 hours), reduce the total cost of a study to $250,000 from $700,000, reduce the number of subjects to 40 from 60 in some cases, and speed time to market for new drugs by running thousands of trials rather than hundreds in the same time period. Hadoop and NoSQL: Data Management Tools and Systems Open-source solutions such as Hadoop and NoSQL will play a crucial role, as they are cheaper and better optimized than proprietary stacks that lack the flexibility required to process unstructured data (blogs, audio, video, research articles published in academic journals, and handwritten medical records) at scale. The key commonality between the two is leveraging commodity hardware and open source software to become a low-cost complement to traditional enterprise data infrastructure (relational databases and data warehouses, etc.). Hadoop and NoSQL are still relatively immature from an enterprise perspective and have yet to take meaningful share from RDBMS or data warehouse vendors. We are seeing the beginnings of some displacement activity, particularly in the data warehousing and ETL markets, and we expect this could accelerate as these open-source technologies become more mature from a security and management perspective. Page 19 July 17, 2014

20 Additionally, the lack of analytics tools and applications that leverage Big Data sets has been a barrier to adoption, but progress is being made on this front. With the release of Hunk, Splunk has begun selling directly to data analysts and architects, bringing it into competition with specialist BI tools for analyzing data in Hadoop such as Datameer, Plaforma, Karmasphere. The introduction of YARN in Hadoop2 and vendors emerging to provide analytics on top of Hadoop should reduce this barrier. Hadoop is an open source processing and storage framework for very large data sets (structured or unstructured) across a distributed network. It is composed of two core components: HDFS (Hadoop distributed file system) and MapReduce. HDFS allows users to store large amounts of data across servers and MapReduce is a framework for processing that data at scale. Emerging vendors such as Cloudera and Hortonworks have developed commercial software distributions of the Hadoop framework targeted at enterprise customers. Cloudera s Enterprise Data Hub competes directly with large players including IBM (IBM, Market Perform, by Keith Bachman), Oracle, SAS and Teradata (TDC, Market Perform, by Keith Bachman). Hortonworks takes the opposite approach of embracing the data ecosystem and partnering with these large players to enable them access to data that lands in Hadoop. Hadoop is used for Extract/Transform/Load (ETL) processes, data reservoir (data aggregation, cleansing, and auditing), data enrichment (the combining of public and enterprise data for insights), and advanced analytics (machine learning, statistics, correlations, and trend analysis). Currently, standalone Hadoop has limited capabilities for generating insight from data, and is typically run in conjunction with traditional information management and processing technologies. The recent evolution to Hadoop 2 introduces YARN, a component that generalizes the compute layer to execute not just MapReduce style but other application frameworks. As such, developers can now build applications directly within Hadoop. Additionally, Hadoop 2 allows for the consolidation of other non-hadoop clusters (such as HPC or virtualization clusters) with Hadoop clusters. Over time, we expect Hadoop to move further up the stack to encompass alternative data processing frameworks through other projects (HBase, Storm, Spark, Graph) to expand the number of services built around Hadoop. Cloudera for example, has a commercial distribution of the HBase NoSQL database, called Impala, that is geared toward large-scale storing of sparse data. Increasingly SQL on Hadoop is being considered, and companies are emerging to take advantage of this. Security concerns have been a slight hindrance to mainstream adoption. But progress is being made here as well. For example, Hortonworks recently acquired XA Secure and Cloudera recently acquired Gazzang, both of which provide centralized policy management, fine-grain access control, and encryption management. Page 20 July 17, 2014

21 Exhibit 12. Hadoop 2 Enables Developers to Build Applications Directly Within Hadoop Source: Hortonworks. Another major enabling technology in Big Data is Open Source NoSQL databases. NoSQL, contrary to traditional relational databases, is designed to handle large volumes of structured, semi-structured, and unstructured data (personal user information, geo location data, social graphs, user-generated content, machine logging data, and sensor-generated data) without a predefined schema (e.g., first name, last name, ID number). NoSQL databases perform better at scale with much lower costs by distributing workloads across commodity servers in parallel. We believe that in the future, the majority of new enterprise applications will use one or more types of NoSQL databases. The market opportunity for databases with these characteristics is thus larger than the core relational market because it addresses a larger set of data. That said, not all data is going to move into NoSQL databases, as relational databases are still better suited for complex transactions (OLTP). We believe that less than 10% of NoSQL database implementations are replacements of traditional RDBMS. Further, some management challenges associated with NoSQL DBMS arise from the lack of standardization across multiple different implementations of NoSQL. For example, MongoDB is extremely successful in lighter application type use cases, while DataStax competes more in higher scale enterprise. Page 21 July 17, 2014

22 Exhibit 13. Scalability and Flexibility the Top Motivations for Using NoSQL Source: Gartner (February 2014). Big Data Spending Will Initially Be Focused on Backend Enabling Technologies Big Data is still very much evolving, in part because of the ongoing maturation/evolution of opensource projects, the massive inertia of existing data architectures, and ongoing confusion over Big Data. Furthermore, as discussed, Big Data is not a discrete solution or technology but a multitude of technologies under a large umbrella. In this way, Big Data spending in many ways draws parallels to security spending a multitude of problems and challenges that require a wide variety of different solutions. These factors in concert will provide a modest headwind to overall BI and analytics software spending growth over the next several years. Near term, much of the focus of Big Data will be on backend systems and processes, which is a necessary step for enabling frontend Big Data applications. Marked growth rates reflect these trends. For example, in 2013, spending on RDBMS, Data Integration Tools, and Data Quality Tools grew at 7%, 9%, and 14%, respectively, faster than the 7% spending growth on Business Intelligence applications. We expect this trend to continue through 2017, at which time spending on front-end application will likely accelerate. Hadoop/NoSQL, also instrumental in enabling next generation predictive analytics applications, will also see faster growth (~35%) than the Business Intelligence/Data Discovery market (~25%). Page 22 July 17, 2014

23 Exhibit 14. Investment Dollars Still Focused on the Data Management Layer Market Size Growth CAGR Vendors Analytics Business Intelligence $ 14,055 $ 18, % 7.3% IBM, ORCL, SAP, MicroStrategy, SAS Data Discovery $ 1,091 $ 2, % 27.3% Tableau, Qlik Data Management Relational DBMS (RDBMS) $ 27,927 $ 39, % 8.9% Oracle, IBM, Microsoft Data Integration Tools $ 2,241 $ 3, % 9.9% Informatica, IBM, Tibco Data Quality Tools $ 1,091 $ 1, % 15.6% Informatica, IBM, SAS, SAP Big Data Enablers Hadoop $ 495 $ 1, % 35.1% Cloudera, Hortonworks NoSQL $ 525 $ 1, % 36.5% MongoDB, MarkLogic, DataStax, Couchbase Source: BMO Capital Markets Research, Gartner, Wikibon. Big Data Is Complementary to Existing Data Management Systems Batch not real time. As noted above, a lot new systems are enterprise ready, not commercialized. There is existing investment in old systems. And transaction business data still has unlocked value that can be extracted from RDBS. Big Data is evolutionary to traditional data management infrastructure, and unlike previous use cases, the promise of Big Data technology uncovers insights from multiple connected data sources using multiple different technologies. However, in considering the rise of Big Data architectures, it is important to recognize that these new data management paradigms are often complementary to the old guard. Hadoop/NoSQL has immense advantages of managing extremely large data sets but is unable to provide the performance of traditional OLAP systems and is still very much a batch process and looks to be so over the course of the next several years. Additionally, as noted above, open source systems do not have the level of maturity for many mainstream enterprise use cases. And, in addition to the massive inertia of existing data management systems within enterprise IT, we believe there is significant untapped potential in terms of analyzing business transactional data via traditional OLAP systems. In Gartner inquiries, the number of organizations asking about replacing traditional data warehouses is dropping significantly, from 17%, 13%, 7%, to 3% between 2010 and Traditional business intelligence, data warehousing, and online transaction processing solutions still have a role to play in the enterprise. A Look at the Old The demarcation between transactional workloads is ubiquitous in enterprise computing due to the dramatic differences inherent in how systems must be optimized. Having two databases one transactional (OLTP) and one analytical (OLAP) and the associated burden of moving data back and forth has been deemed as an acceptable compromise due to the speed and agility afforded by optimized systems. Corporate data is typically generated by multiple independent business support systems and must be aggregated and processed (by data integration software from Oracle, IBM, or Informatica) before being loaded onto a disk in the form of tables and multi-dimensional cubes (OLAP) in a data warehouse, where it is available for use by Business Intelligence Page 23 July 17, 2014

24 applications. This ETL process can take anywhere from a few hours to weeks to complete, meaning analysis is constantly being done on stale data. There are immediate benefits to an in-memory approach combining OLTP and OLAP into a single system in-memory which can be 10-20x faster than traditional disk-based OLAP. Eliminating redundant data stores and complicated ETL processes results in significant benefits from an IT perspective, providing substantial improvements in business agility without the management burden of constructing two separate systems and managing data integration between the two. There are also substantial business benefits because the increased performance of these systems enables analytics applications more direct access to the physical data store and creates a real time aspect to analytics and reporting. This agility and speed enables more precise analytics capabilities to be built into business processes, which in turn support new kinds of business decision making that was not possible in a world when analytics and business intelligence were grounded in a rearward-looking batch processes. SAP s HANA has taken a leadership position with in-memory technology, but its competitive lead has narrowed. Originally targeted at analytics applications, SAP was an early proponent of in-memory based systems, an approach that has been validated as the world has begun to converge on this vision. SAP s first in-memory system, Business Warehouse Accelerator (BWA), was introduced several years ago to address the massive pain points experienced by disk-based OLAP queries. The combination of BWA and the acquired Business Objects has failed to materialize due to integration issues. HANA has gone from a fairly specific product targeted at supporting analytical application toward a broad platform designed to underpin the entirety of SAP s and a large number of third-party applications, whether they are provided on-premise, as a hosted service, or as a true cloud offering. Oracle combined OLAP and OLTP with its Exadata product several years ago, has been offering in-the Exalytics in-memory system since 2011, announced Exadata in-memory machine in 2012, offers the TimesTen In-Memory Database standalone, and now offers fully integrated in-memory capabilities with its new Database 12c. Exhibit 15. Solutions Vary Based on Specific Requirements in Processing and Data Source: BMO Capital Markets Research, Open Data Center Alliance. Page 24 July 17, 2014

25 Combining the Old With the New The logical data warehouse (LDW) is emerging as the target architecture for combining data warehousing and Big Data technologies. Gartner expects that through 2020, more than 90% of Big Data implementations will augment, not replace, existing data warehouse and business intelligence deployments. We see a growing need for Big Data integration specialists such as Pentaho and Talend, which are sometimes being deployed to off-load certain less frequently used data into Hadoop for less time sensitive batch processing. Exhibit 16. Next Generation Data Management Infrastructure Data Sources Data Warehouse Applications ERP Reporting CRM SCM Files E T L S t a g i n g Data Management Systems Data Mart OLAP Cube Dashboards Excel Data Discovery Web Logs E mail NoSQL Custom Applications Sensors Machine Data Enterprise Applications Social Media Source: BMO Capital Markets Research. Traditional Big Data Architecture Deployments combining structured data with new data types are growing, and data warehouse vendors are adapting to meet evolving requirements. Leading organizations are currently pursuing a hybrid of the LDW and traditional implementation approaches. Building data marts remains an organizational data priority followed closely by the use of NoSQL or MapReduce as an ETL preprocessor based on a survey by Gartner. This approach lends itself to a more service-oriented approach to data, opening data up to multiple applications (data to endpoints) and deploying data marts for specific end users for report building (endpoints to data). Our research suggests that organizations are introducing Hadoop into their environments for processing and storage that then feeds into a NoSQL database for building new custom applications or for existing enterprise applications (i.e., Hortonworks' partnerships with Splunk and Tableau or Cloudera s partnership with Qlik) in parallel with existing data warehouses. Over time these approaches will converge into the LDW as traditional BI reporting from data marts becomes increasingly commoditized and Page 25 July 17, 2014

26 Exhibit 17. Data Vendor Landscape will at some point be more deeply integrated into an organization s modern data management framework. Database Platform Enterprise Data Warehouse Big Data Analytics Platfoms/Tools Business Intelligence Platfoms/Tools Database Platform Relational OLAP Data Warehouse: Software based Hadoop: Business Intelligence Oracle 12c IBM Netezza IBM InfoSphere BigInsights Actuate Birt Microsoft SQL Oracle ExaData Operational Intelligence: IBM Cognos IBM DB2 Teradata CommVault Microsoft SAP Sybase SAP BW Splunk MicroStrategy Database Platform in memory: Next Generation Data Warehouse: Integration: Oracle Hyperion SAP Hana HP Vertica Informatica SAP Business Objects Microsoft SQL Oracle Big Data Appliance PRIVATE Tibco Jaspersoft IBM SolidDB Teradata Aster Discovery Platform Software based Hadoop: Business Intelligence Data Discovery Oracle 12c In memory/timesten SAP Hana Cloudera Datawatch PRIVATE PRIVATE Hortonworks Qliktech NoSQL Column: Next Generation Data Warehouse: MapR Tableau DataStax Cassandra 1010 Data Pivotal HD Tibco Spotfire Hbase Actian ParAccel Operational Intelligence: PRIVATE Sqrrl Accumulo Kognito Sumologic Business Intelligence NoSQL Document: Pivotal Greenplum Loggly Alteryx Couchbase LogRhythm Information Builders Marklogic Search Platforms Pentaho MongoDB (fmr. 10Gen) Elasticsearch SAS NoSQL Key Value Stores: LucidWorks Business Intelligence Cloud CPM Basho Technologies Riak Integration Adaptive Insights Dynamo Mulesoft Analapan Redis Pentaho Tidemark NoSQL Graph: Talend Business Intelligence Cloud Platform Allegro Analytic Platforms 1010 Data Neo4J Appistry Birst In Memory: Attivio GoodData Aerospike Ayasdi Business Intelligence Data Discovery MemSQL Hadapt ClearStory Data Pivotal GemFire Mu Sigma Domo VoltDB Opera Solutions Logi Analytics PostgreSQL: Palantir Salient Management Company EnterpriseDB Panorama Software PivotLink Business Intelligence Data Discovery Hadoop Datameer Platfora Karmasphere Source: BMO Research As such, it s not surprising that incumbent vendors are providing multiple data strategies (RDBMS, Hadoop, NoSQL) under a hybrid approach by adopting open-source components within their solutions. Examples thus far include Oracle embedding Cloudera in its Big Data Machine, the Microsoft Azure and Hortonworks partnership, and the Teradata Unified Data Architecture. Next generation data database solutions, such as those from 1010 Data, Actian, Cloudera, DataStax, MongoDB, MarkLogic, SAP HANA, Pivotal Greenplum, HP Vertica, and Amazon RedShift are emerging to challenge incumbents with a Big Data first mentality. Over time, Platform-as-a-Service could also be considered an alternative to on-premise data warehouses. GoodData is pursuing a strategy similar to this with its Open Analytics Platform. We view this shift as a positive for Hortonworks strategy of enabling Hadoop for existing data warehouses. Page 26 July 17, 2014

27 Exhibit 18. NoSQL and MapReduce (Hadoop) are Complementary Enabling Technologies For Modern Data Architecture Source: BMO Capital Markets Research, Gartner (February 2013). We think that traditional transactional business data, or Dark Data, remains a substantial opportunity for incumbent vendors because we do not believe the majority of business data created is being captured for use in the real time reporting and analytics process. We suspect that many enterprises integrate three or fewer data sources to support data warehousing or business intelligence systems, which we believe to be well below the number of sources that can be successfully integrated. The value derived from business analytics increases significantly the more independent data sources are integrated for comparison. Low data integration rates suggest plenty of opportunity for data warehousing, which are the underlying engines that store, organize, and ensure continuity of data. In-memory RDBMS technology is a key enabler of unlocking both existing data inside of organizations and supplementing Hadoop by supporting ad hoc analytics and real-time analytics processing. Inserting results of Hadoop/MapReduce into an in-memory data store allows business users to explore data without the delay of batch processes or relying on IT to produce reports. For example, social data could be analyzed in real time in an in-memory database to monitor the progress of a marketing campaign or be processed in Hadoop to find insights related to supply chain forecasting. Operational Intelligence: Emerging Market Expanding From Machine to Relational Data Sets Despite the significant investment in analytics only 0.05% of digital data created is being captured for use in the real time reporting and analytics process. Operational Intelligence (OI) is a form of real-time dynamic, business analytics that delivers visibility and insight into business operations. OI solutions run query analysis against machine logs, live feeds, and event data to deliver realtime visibility and insight into business and IT operations, enabling people to make better, faster decisions. OI automates processes for responding to events by using business rules and incoming event information. Contrary to Business Intelligence, which is data centric, OI is primarily activity-centric. Page 27 July 17, 2014

28 By leveraging machine data, Operational Intelligence vendors can touch and monetize more forms of data than can the emerging Data Discovery BI vendors, which are more focused on new delivery mechanisms to extend traditional relational BI data sets. Given the broad use cases associated with OI, budget for related solutions is spread across infrastructure, security, operations, and analytics spending. This represents a challenge for OI vendors. The market for the core machine log management market is nascent but has substantial potential; IDC projects that the digital universe will reach 40 zettabytes (ZB) by 2020, with machine-generated data expected to represent 40% of that. Pricing per GB capacity naturally drops with scale and, given the capacity-based model of most vendors, we believe the opportunity to be worth multiple billions of dollars. At SplunkLive NY, in mid-may 2014, Splunk demonstrated a business analysis use case by monitoring retail and webstore sales of iphone units in real time. The demonstration showed how real time sales data could be retrieved through dashboards and pivot interfaces in order to optimize marketing and promotional campaigns. Most organizations have not been analyzing log files, and the ones that have rely primarily on homegrown solutions. Accordingly, we consider the majority of the market to be greenfield and a major opportunity for emerging OI vendors such as Splunk and SumoLogic. There has been investor focus regarding the competitive nature of this market but, given its nascence, we believe there is ample runway for all emerging OI vendors and that competition primarily comes in the form of displacing homegrown solutions as well as lack of knowledge regarding the potential of this technology. As use cases expand beyond security and basic IT and operations into broader business analytics we expect competition to increase. We believe that OI is only in the very earliest stages of starting to capture some of the ~$15 billion spent annually on business intelligence software. The market for OI is in early innings but the following metrics suggest that the market is poised for very strong growth: Splunk grew billings 46% in its recent quarter. SumoLogic s recently raised $30 million in funding bring its total to more than $75 million. VMware has stated that 75% of IT organizations are not doing log management in any real sense. Significant M&A activity in the log and event management space: HP acquired ArcSight, Tibco acquired LogLogic, Solarwinds acquired TriGeo, IBM acquired Q1-Labs, among others Splunk is positioning its platform as the Data Fabric that collects data from anywhere to perform real time operational intelligence. Splunk believes that search will be the de facto data query language standard, eliminating the need to structure data in various schema and therefore obviating the ETL/ data warehouse/mdm model. Splunk collects and indexes all the streaming data from IT systems and technology devices in real time in tens of thousands of sources in various formats and types defining the data schema at a read, not a write time. Over the past year, Splunk has gradually expanded from providing on-premise solutions focusing on machine log data toward a hybrid cloud architecture that includes connectors to SQL databases (DB Connect) and Hadoop (Hadoop Connect, HadoopOps, Hunk). Enterprise 6 has been well received since its 3Q13 release, with several ease of use enhancements including pivot interfaces and dashboards, designed to encourage broader enterprise adoption. As the product expands within organization from IT to new areas such as data analysts and architects, Splunk will encounter more competition from vendors in the Business Intelligence and Web Analytics space. Page 28 July 17, 2014

29 Time will tell the extent to which Splunk is able to expand its customer reach outside of IT operations/security and into the realm of data analysts and business users. In the core IT Operations and Application management space we see cloud-based vendors as the biggest competitive hurdle but not in a zero sum scenario. SumoLogic, commonly cited as the biggest emerging competitor to Splunk, mostly differentiates around its pure cloud-based delivery, which enables it to effectively manage capacity/pricing as well as offer by provide insights around infrastructure by benchmarking between clients. Its current focus is around Application availability/performance and Security in focused verticals. We have heard of existing Splunk customers implementing SumoLogic; however, we think it s important to keep in mind that Sumo has roughly 300 customers versus Splunk s more than 7,400. Over time we do believe the cloud/subscription model will be preferred by some customers as a way to predominantly manage the growth in capacity relative to price. Business Intelligence and Analytics: Decentralization The $15 billion dollar business intelligence market is going through a significant transition, driven by evolving business user requirements and enabled by advances in in-memory and data discovery/visualization technology. This is illustrated in Gartner s market share data, which shows traditional BI vendors commanding 64% of the market but growing only 4.5%, below the market growth rate of 7.9%. In suit, the BI market is shifting from rearward-looking centralized reporting of the past to forward-looking decentralized near-real time predictive analysis. Underpinning this transition is the explosion of Big Data, which holds valuable insight for companies willing to invest in technologies to capture and analyze it, thereby forcing other companies to invest lest they lose their competitive edge. Legacy BI tools have not lived up to their promises, particularly around ROI, as consolidation in the space (IBM/Cognos, Oracle/Hyperion, and SAP/BusinessObjects) has not reduced complexity and has in fact slowed innovation. A new breed of vendors such as Tableau and Qlik has commoditized traditional reporting/query tools. Importantly, the consumerization of BI technology is in some cases shifting the end user from IT analysts to business users, which in effect is expanding the market opportunity. While this new breed of data discovery vendors is out in front at the moment, incumbents are coming to market with competitive tools, which will lead to an increasingly competitive battle for customer wallet share. Incumbents are attempting to stem customer losses by adding visualization tools to traditional offerings while data discovery vendors will broaden their data management capabilities to address traditional business analysts requirements, leading to a collision course as products and capabilities begin to converge With that said, we don t view the market as a zero-sum game for incumbents. Gartner expects that less than 25% of enterprises will fully replace their existing BI solutions. Large organizations will likely settle on multiple platforms, ranging from full enterprise BI suites, to BI embedded into applications and lightweight desktop self-service BI tools for business users. Data discovery vendors are growing market share, although it is unclear whether the BI incumbents (organically, or through acquisition) or the data discovery specialists will ultimately win out. Page 29 July 17, 2014

30 Exhibit 19. Business Intelligence Market Share All Incumbents Losing Share Market Share Business Intelligence ($M) Revs '13 Share '12 Share '13 y/y growth Total $14, % CAGR 7.3% Top 3 share 50% 48% SAP $3, % 21.3% 5.3% Oracle $1, % 13.9% 2.1% IBM $1, % 12.7% 4.9% SAS $1, % 11.8% 6.0% Microsoft $1, % 9.6% 15.9% Qliktech $ % 3.0% 20.1% MicroStrategy $ % 2.9% 4.9% FICO $ % 2.6% 8.8% Tableau $ % 1.5% 80.5% Information Builders $ % 1.3% 0.3% Other vendors $2, % 19.4% 10.5% Gain Loss Source: Gartner. The Shift to Data Discovery Data Discovery is increasingly taking over as the next-generation BI architecture. Garter expects that by 2015, the majority of BI vendors will make data discovery their primary BI platform offering, shifting BI emphasis from reporting-centric to analysis-centric. As discussed in the above sections, traditional BI reporting tools depend on extracting data from a data warehouse and have been largely confined to reporting yesterday s news in static reports or preconfigured dashboards. Most business users are not exposed to this information and rely on IT for reporting. Moreover, implementing these systems takes months and maintenance can cost 3-5x the cost of a BI application. Data discovery tools offer an intuitive interface, which makes the application accessible to many more users, enabling them to explore data, conduct rapid prototyping, and create proprietary data structures to store and model data from disparate sources. Business users themselves, unskilled in traditional business intelligence and data analytics, are able to create, modify, mash up, and share their data, helping them to make better-informed decisions. Based on reported results and our industry conversations, these data discovery deployments are beginning to move from small groups within companies to larger organizations and business units. Currently, IT is often a bottleneck for broader adoption due to internal politics and the lack of a cohesive data management initiative. This is perhaps the single biggest barrier to data discovery adoption and will help protect traditional BI players. Looking ahead, no single vendor is addressing business user ease of use and IT driven enterprise requirements, and as data discovery deployments grow and use cases become more complex this will emphasize the need for Page 30 July 17, 2014

31 governance. This is the impetus of QlikView Next, expected later this year. Over time, searchbased data discovery will ultimately drive mainstream adoption of data discovery platforms, similar to the way the web browser brought about ubiquitous use of the internet. Exhibit 20. Traditional BI Platforms vs. Data Discovery Platforms Traditional BI Platforms Data Discovery Platforms Key Buyers IT-driven Business-driven Main Sellers Approach Megavendors, large independents Top-down, IT-modeled (semantic layers), query existing repositories Fast-growing independents Bottom-up, business-usermapped (mashup), move data into dedicated repository User Interface Report/KPI dashboard/grid Visualization/interactive dashboard Use Case Monitoring, reporting Analysis Deployment Consultants Users Source: Gartner. What s Enabling Data Discovery? The key underlying driver enabling the emergence of data discovery technology is advances in computing, specifically in the area of in-memory. Traditionally, OLAP systems accessed data stored on hard disk drives, which due to inherent limitations of electromechanical disks, experienced latency and delay. Queries typically could take hours. Storing or caching large amounts of data in-memory was cost prohibitive. However, the shift to 64 bit systems and the sustained reduction in memory prices has at last enabled the building of information systems that leverage memory versus disk in OLAP applications. As a result, analytical query times can be reduced from hours to minutes, or even seconds. This shift to 64-bit systems has marked the inflection point for vendors like Qlik and Tableau, and in-memory is a core technology component of the SAP HANA and Oracle Exalytics vision. In-memory will be an important piece of the overall next generation analytics space. Most solutions in the market max out at up to a billion rows of data and deal mostly with structured data. Machine-generated data creates billions of rows of data, which necessitates other types of processors, such as parallelization and Hadoop. What About Cloud BI? We expect cloud BI to emerge as a growing trend in the years to come. To date, cloud-based delivery has not been a popular option, representing less than 5% of overall BI spending today. The market is growing, however, up 42% in 2013, and we expect this trend to continue. The primary driver for increasing cloud deployment is that the percentage of data creation occurring off-premise, beyond the firewall, is growing. As this broad secular trend continues, data gravity will pull more workloads, services, and applications, including BI, into the cloud where the data is created and residing. Trust has been and continues to be a hurdle to cloud adoption, but there are Page 31 July 17, 2014

32 indications that this is changing. Forty-five percent of Gartner s recent Magic Quadrant survey respondents noted a willingness to put mission-critical BI in the cloud, compared to 33% in As with most applications, the promise of increased collaboration with customers and partners and mobility are drivers of Cloud BI. Over time, we expect the proliferation of connected devices (the Internet of Things ) and continued growth in cloud services (SaaS, PaaS, IaaS) will create more cloud based data and drive the adoption of cloud BI solutions. Cloud Business Intelligence vendors including Birst, GoodData, Looker, Domo, Adaptive Insights have all garnered significant amounts of venture funding. GoodData s focus on building an end-to-end system from visual interface to customer data management to perform analytics-asa-service is gaining good market traction. As IT becomes more heterogeneous we expect platforms like this to become more pervasive, and help drive cloud adoption. Established vendors such as MicroStrategy, Tableau, and Tibco/Jaspersoft also offer cloud BI products. Business Intelligence Competitive Positioning Market share in BI continues to be concentrated, but sources of innovation are more diverse. According to Information Week, much of the activity in the BI market has been dominated by emerging BI vendors focusing on experimenting with open source technology, producing a diverse set of solutions, marking a trend away from standardization. In 2012, 30% of those surveyed had standardized on a small handful of BI tools, falling from 47% in This reversal has occurred despite massive consolidation in the BI space last decade (SAP/Business Objects, Oracle/Hyperion, and IBM/Cognos). Today, traditional BI vendors command ~64% of the market but grew on less than 5% on average, below the overall market growth rate of 8%. After consolidating the market, the large IT vendors were largely focused on integrating acquired BI solutions into their broader software and infrastructure product portfolio, which generally resulted in underinvestment. As a result, we believe these legacy BI tools are considered old and lacking in modern functionality. Emerging data discovery vendors, by contrast, have led with innovative solutions, which is rejuvenating the marketplace and leading to growth 3x that of traditional BI platforms. In summary, the consumerization of IT is resulting in a shift in the use of and the buying of BI and related services away from IT and toward individual business users and managers. Data discovery vendors continue to organize their go-to-market strategies around this trend and we expect traditional BI vendors to increasingly pivot away from IT to attack this new opportunity. This is consistent with Gartner s prediction that, by 2014, 40% of BI purchasing will be businessled rather than IT led. Page 32 July 17, 2014

33 Exhibit 21. Data Discovery Vendors Gaining Mindshare Source: BMO Capital Markets Research, Forrester. Products from large vendors such as IBM, Oracle, Microsoft, and SAP are currently the most popular BI choices, with Tableau and Qlik ranking among the top five vendors mentioned in the BI-related inquiries at Forrester. This is consistent with data from Gartner that suggests that best of breed solutions are growing mindshare. Page 33 July 17, 2014

34 Exhibit 22. Standardization on Mega Vendors Is Dropping, With Most Survey Respondents Preferring to Adopt Best of Breed Source: BMO Capital Markets Research, Gartner. We believe traditional BI applications from incumbent vendors will continue to lose share as standard reporting becomes commoditized and is embedded across the application stack. As noted above, in-memory and new data processing frameworks are leading to new application architectures, allowing vendors to offer solutions that let users embed analytics deeper into the business process. Incumbents are responding by focusing on the benefits of offering an end-toend stack while leveraging their massive installed bases, as evidenced by SAP s HANA strategy, Oracle s embedding of analytics within its Fusion Applications, and Microsoft s positioning of SharePoint as a delivery mechanism for BI. SaaS vendors like Salesforce.com, Workday, and Demandware are in the early stages of creating Big Data solutions that incorporate third-party data into their own cloud-based data and delivery business insights. Data discovery is one of the main disruptors of the incumbent vendors market dominance in the near to medium term, although IT concerns over duplicate data sets remains a potential roadblock. We have heard customers express reluctance around the perception of having to add yet another silo on top of existing systems, since newer data discovery tools often create another copy of corporate data in the process of performing analytics. The main three data discovery vendors Qlik, Tableau, and Tibco Spotfire control ~75% of the ~$1 billion market; however, the data discovery market accounts for only ~8% of the total BI market. These vendors are moving beyond their original target business user customer and are attacking the enterprise more broadly Page 34 July 17, 2014

35 by adding features that challenge larger vendors in terms of scalability, security, rich metadata, internationalization, and application programming interfaces (APIs), among other industrialstrength enterprise features. The key to the evolution of these vendors is how they approach and manage data governance and other issues sensitive to enterprise buyers. While the three vendors all offer data discovery tools with strong visualization capabilities, they differ in important ways. Qlik. Qlik s products are targeted toward business users with strong visualization attributes. Qlik s solutions are based on proprietary systems that extract data from existing data warehouses into in-memory systems. Qlik offers a data discovery option to connect directly to other data sources. The company is at the front end of a major product cycle with a full release of QlikView.Next (QV.N) slated for later this year. QlikView11(QV.11) is expected to be the primary driver of business in 2014, according to management. Adoption for the move to QV.N is expected to be gradual, with some users staying on QV11, some upgrading, and others choosing QV.N for net new transformational use cases or users. We believe QV.N is a transformative release from a product and a consumption perspective that should expand the company s addressable market by bridging the gap between data discovery and complex BI platforms. The core of QV.11 and prior versions is its data discovery capabilities; QV.N., however, is designed to give users immediate insights while providing IT professionals the enterprise manageability and governance they require. Further, the release of the QV.N API extends use cases of the core QV application to very specific guided analytic experiences. Uneven execution remains the overarching issue at Qlik; however, we re positive on the solution. Tibco. Tibco Spotfire is geared more toward the high end of the market. Like Qlik, Spotfire leverages in-memory and uses connectors to hook into existing data warehouses. Tibco has had execution issues with Spotfire and has been losing share. We hear positive anecdotes on the product s ease of setup and use. Tibco released version 6.5 of Spotfire in 2Q, which includes a personal desktop addition, new simple-to-use mapping and staff capabilities, expanded data connectivity, especially for big data sources like Hadoop clusters, and enhanced usability features. Tibco also recently acquired Jaspersoft, which is strong in embedded BI applications. Tableau. Tableau has a distinct architectural approach with its Visual Query Language (VizQL) Live Query Engine, which allows users to instantaneously connect to large volumes of data in existing databases to leverage investments in existing data platforms. Direct query access has been a strength of the platform since the product's inception. To complement Tableau s Live Query Engine, its Hybrid Data Architecture In-Memory Data Engine enables users to import large amounts of data into its in-memory database for customers that want to analyze data that is not already captured in existing databases (such as text files, spreadsheets, and logs) for those seeking the performance capabilities offered through in-memory. The company released its 8.1 release in 4Q13; version 8.2 was released in 2Q14, and Tableau 9.0 is expected to be released in 1H15. The key feature in the 8.2 release is Mac support. Established vendors are attempting to add data discovery features onto existing offerings through organic development or acquisition. Capabilities are thus far in general weaker than those offered by pure play vendors, but this may be sufficient to protect their existing installed base. IBM (IBM, Market Perform, by Keith Bachman). IBM offers a complete range of enterprisegrade BI, performance management and advanced analytics platform capabilities, complemented by a deep services organization. The IBM Cognos BI platform handles some Page 35 July 17, 2014

36 of the largest deployments in the world, and in 2013, IBM significantly simplified the Cognos licensing model, reducing 28 SKUs to only two. IBM offers data discovery through its IBM Cognos Insight product. IBM has been acquiring domain-specific experience in packaged analytic applications including Algorithmics (credit and operations risk analytics), DemandTec (merchandising analytics), Emptoris (spend analytics), and Varicent (sales performance management). Infor. Infor Business Intelligence is part of an end-to-end platform that encompasses BI and performance management offerings, both based on the MIS Alea product (also called MIS DecisionWare) acquired from Systems Union in Infor is investing significantly to enhance the attractiveness of its platform both inside and outside Infor's ERP installed base. Infor BI is used primarily for reporting. Infor BI 10x includes an in-memory multidimensional OLAP database; other offerings include Web frontends, such as Infor BI Dashboards/Motion Dashboards for data presentation and analysis, a feature-rich Microsoft Excel-based interface, a data integration tool, and a modeling tool. Infor BI Planning and Infor BI Consolidation offer data modeling and reporting, in support of a standard planning and financial consolidation process for a diversity of industries and domains. Infor Intelligent Open Network (ION), Business Vault, and Workspace integrate BI content for Infor and non- Infor ERP customers. Infor BI is a consideration by organizations looking for an integrated BI and performance management solution. Microsoft. Microsoft has done a good job delivering a combination of business user capabilities with an enterprise-capable platform. The Microsoft data platform takes advantage of Office, Azure, and SQL Server. For data discovery, Microsoft hopes to tap its ~1 billion Office users and leverage enterprise licensing agreements to drive competitive pricing. Microsoft offers Power BI for Office 365, a cloud-based, self-service business intelligence solution with natural language capability and Power Query for Excel, which makes it easier for generalists to perform data discovery. The Azure HDInsight for elastic Hadoop is a managed cloud-based Hadoop service through a partnership with Hortonworks. This integrates with Power BI and Excel. The Microsoft Azure Intelligent Systems Service will help customers embrace the Internet of Things and securely connect to, manage, and capture machine-generated data from sensors and devices, regardless of operating system. SQL Server 2014 is the first time in-memory is built into every SQL workload, enabling non- OLTP to take advantage of in-memory without changing workloads. The Analytics Platform System (APS) combines the best of Microsoft s SQL database and Hadoop technology in one low-cost offering, leveraging partnerships with Dell and HP. PolyBase brings structured and unstructured data together in a data warehouse appliance. MicroStrategy. MicroStrategy remains an industry benchmark in large BI deployments running on top of large enterprise data warehouses, and it is often viewed as a go-to vendor when enterprise requirements are complex. Functionality is one of the main reasons for selecting MicroStrategy; however, time to create reports is among the highest and MicroStrategy lacks user-friendly and comprehensive advanced analytic capabilities, as well as unstructured data support. In late 2013, MicroStrategy released an updated version of its visual data discovery engine. A scalable multiterabyte in-memory engine is also being developed in cooperation with Facebook, for release in This could differentiate MicroStrategy from direct competitors in the data discovery market. Microstrategy has done a good job releasing cloud products and driving adoption in its installed base Page 36 July 17, 2014

37 Oracle. Oracle s BI and analytics technologies include Oracle BI, Oracle Exalytics In- Memory Machine, and Oracle Endeca Information Discovery. Customers choose Oracle for its stack integration; the company offers more than 80 prebuilt analytic applications for Oracle E-Business Suite, PeopleSoft, JD Edwards, Siebel and other enterprise applications, including industry-specific packaged analytic applications. Most Oracle BI deployments support traditional BI reporting and dashboarding. Oracle purchased Endeca to add searchbased data discovery. Endeca has good functionality, but integration and growth have slowed since the acquisition and the solution now lags pure play competitors. Oracle BI on Exalytics uses the Oracle TimesTen In-Memory RDBMS for performance optimization. Oracle plays from a position of strength as a result of its dominance in database and applications, but execution around data integration has been uneven. SAP. SAP customers use SAP BusinessObjects BI primarily for reporting. SAP has been investing heavily in SAP Lumira to establish a presence in the data discovery market. SAP is used to embed BI content, but is not widely used by customers to embed advanced analytic content, although the introduction of HANA is designed to change this. The company has had issues integrating BusinessObjects with legacy solutions such as Business Warehouse Accelerator (BWA). Classic BusinessObjects is now being deemphasized as a front end for BWA. BusinessObjects is being bundled with HANA in an effort to drive the BWA installed base toward HANA adoption. SAP is positioning HANA as the strategic database and inmemory platform that will enable much of the advanced analytics functionality delivered by Lumira, Predictive Analysis, and KXEN. SAS. SAS tools have been used primarily by power users, data scientists, and IT-centric BI developers. It s considered an expensive system that takes time to deliver reports, but has strong roots in advanced analytics. In 2012, SAS released Visual Analytics, a business useroriented data discovery and BI platform targeted at less technical and analytically sophisticated users. The product is strong but is only relevant for SAS shops. The product uses SAS's LASR Analytic Server, an in-memory server for large-scale data analysis. Importantly, SAS has made the strategic decision to make Visual Analytics its "go forward" BI platform and the front-end reporting and analysis tool for all its analytic applications, which should unify SAS front-end tools, defend its installed base against stand-alone data discovery vendors, and address both business user and business analysts. Business Intelligence Spending A key trend is that data discovery vendors are actually expanding the addressable market for BI applications by creating products that are usable, and appeal to and empower individual business users. Formerly, classic BI was confined to the IT department and generally required specific skill sets. The benefits of these new vendors business models are faster sales cycles and deployments, expansion into SMBs and departmental organizations, viral usage, and try before you buy selling. As a result, spending on data discovery is now three times faster than that of the traditional BI market. We believe that this accelerating growth in spending is largely incremental to the traditional BI market, but over time we expect it will come at the expense of traditional BI vendors (IBM, SAP, ORCL, SAS), especially as buying centers (IT vs. departmental) converge. While there is significant opportunity for multiple vendors in the emerging data discovery market, we think Tableau is best positioned to outgrow the competition. Tableau is actually gaining share at the expense of Qliktech and Tibco/Spotfire in the market for data discovery tools. Tableau Page 37 July 17, 2014

38 gained 11points of market share over the past two years, matching the combined declines of Qliktech (7 points) and Tibco/Spotfire (4 points). Emerging cloud vendors are also in a position to take advantage of this shift over time. We take a top-down and bottom-up approach to sizing the Business Intelligence Market. Gartner estimates the total BI market at $14.05 billion today, growing at a 7.3% CAGR to $18.60 billion by Our bottom-up customer sensitivity analysis yields a potential $25 billion market opportunity. Exhibit 23. Addressable Market and Market Share Analysis CAGR BI Platforms $ 8,442 $ 8,968 $ 9,571 $ 10,180 $ 10,828 $ 11, % CPM Suites $ 2,629 $ 2,827 $ 3,049 $ 3,285 $ 3,538 $ 3, % Analytic Applications and Performance Management $ 2,059 $ 2,261 $ 2,482 $ 2,728 $ 2,998 $ 3, % Total Busienss Intelligence Market $ 13,131 $ 14,055 $ 15,101 $ 16,193 $ 17,365 $ 18, % y o y 6.8% 7.0% 7.4% 7.2% 7.2% 7.1% y/y (traditional) 5.3% 5.6% 5.8% 5.2% 4.9% 4.4% y/y (contribution from Data Discovery) 1.5% 1.4% 1.6% 2.0% 2.3% 2.8% Data Discovery $ 853 $ 1,091 $ 1,395 $ 1,784 $ 2,266 $ 2, % y o y 34.8% 27.9% 27.9% 27.9% 27.0% 26.5% % Total Business Intelligence Market 6.5% 7.8% 9.2% 11.0% 13.0% 15.4% Data Discovery Market Share Tableau License +Maintenance Revenue $ 118 $ 213 $328 $446 $597 $ % QlikTechLicense +Maintenance Revenue $ 359 $ 431 $497 $574 $660 $ % Tibco Spotfire License +Maintenance Revenue $ 163 $ 171 $ 180 $ 189 $ 199 $ % Other $ 213 $ 275 $ 391 $ 575 $ 810 $ 1, % % Data Discovery Tableau 14% 20% 24% 25% 26% 27% QlikTech 42% 40% 36% 32% 29% 26% Tibco Spotfire 19% 16% 13% 11% 9% 7% Other 25% 25% 28% 32% 36% 39% Source: Gartner "High-Tech Tuesday Webinar: Collision of Data Discovery and Business Intelligence Will Cause Destruction", September 2013; Gartner "Forecast: Enterprise Software Markets, Worldwide, , 4Q13 Update"; BMO Capital Markets estimates. Estimates vary as to the penetration of Business Intelligence tools used by the 615 million information workers globally (Forrester). In its S-1, Tableau cites data from Forrester that assumes ~105 million, or 17%, of information workers globally use BI tools today. This is consistent with a February 2014 statement by Eron Kelly, general manager of SQL Server Marketing at Microsoft: Maybe 10-20% of employees today use BI tools on any given day. Using these assumptions yields an average revenue per user of BI tools today of $134/user per year given Gartner s estimated market size of $14.05 billion. Expanding the addressable market to business users and into different buying centers has and continues to expand the addressable market for BI tools. Again, utilizing data from Tableau s S-1, citing Forrester estimates, ~363 million, or 59%, of information workers globally use spreadsheets today. This is equivalent to roughly a third of the estimated 1 billion Microsoft Office users worldwide. Based on these assumptions, we estimate the incremental addressable market to be 258 million users. Assuming a $134/user ASP and 33% penetration, this yields an incremental $11 billion opportunity. These estimates are illustrated in Exhibit 26 below. Page 38 July 17, 2014

39 Exhibit 24. Incremental Market Opportunity Selling to Business Outside of IT Incremtal Market Opportunity for Busienss Users (M) Incremental Users est. 258,000, M spreadsheet users - 105M BI users= 258M ASP user Market Size ($M) $ 114 $ 124 $ 134 $ 144 $ 154 Penetration 3.2% $ 927 $ 1,009 $ 1,090 $ 1,171 $ 1, % $ 2,396 $ 2,606 $ 2,817 $ 3,027 $ 3, % $ 3,865 $ 4,204 $ 4,544 $ 4,883 $ 5, % $ 5,333 $ 5,802 $ 6,270 $ 6,739 $ 7, % $ 6,802 $ 7,400 $ 7,997 $ 8,594 $ 9, % $ 8,271 $ 8,997 $ 9,724 $ 10,450 $ 11, % $ 9,740 $ 10,595 $ 11,451 $ 12,306 $ 13,161 Source: BMO Capital Markets Research, Gartner, Forrester. Incremental revenue from business users could drive the total addressable market opportunity to roughly $25 billion, which would yield a total user penetration of ~30%. Coincidentally, this is consistent with a poll from Gartner s 2012 Business Intelligence Summit in which respondents reported that a mean of 31% of users had access to analytics tools. Exhibit 25. Addressable Market Opportunity Adressable Market (M) Total Information Workers 615,000,000 Forrester 2013 estimate ASP user Market Size ($M) $ 114 $ 124 $ 134 $ 144 $ 154 Penetration 17.1% $ 11,955 $ 13,005 $ 14,055 $ 15,105 $ 16, % $ 13,424 $ 14,603 $ 15,782 $ 16,961 $ 18, % $ 14,893 $ 16,201 $ 17,509 $ 18,817 $ 20, % $ 16,361 $ 17,798 $ 19,235 $ 20,672 $ 22, % $ 17,830 $ 19,396 $ 20,962 $ 22,528 $ 24, % $ 19,299 $ 20,994 $ 22,689 $ 24,384 $ 26, % $ 20,768 $ 22,592 $ 24,416 $ 26,240 $ 28,064 Source: BMO Capital Markets Research, Gartner, Forrester. Performance and Sentiment We believe we remain in the early innings of rewriting the data architecture. Big Data is still evolving, in part because of the ongoing maturation/evolution open-source projects, which act as enablers, and confusion over Big Data will hamper overall spending on BI and analytics software for the next several years. Meaning much of Big Data focus is still on the enabling data management layer. Over the past 12 months we have seen sentiment to gain exposure to the scarce number of growth companies exposed to the Big Data trend reach euphoria, and sharply reverse a sentiment shift, in which investors pared back exposure to high-multiple growth names. Page 39 July 17, 2014

40 Exhibit 26. One-Year Relative Performance yr Relative Performance DATA SPLK QLIK RUT-E Source: BMO Capital Markets Research and Thomson. The median infrastructure company is trading 28.4% off its 52-week high. Exhibit 27. Absolute and Relative Performance Price Absolute Performance Relative vs S&P Week % of Week % of 52 Company Ticker 7/16/14 1 Month 3 Month 12 Month 2 Year 3 Year 5 Year 1 Month 3 Month 12 Month 2 Year 3 Year 5 Year High Week High Low Week Low Actuate Corp BIRT $ % 21.5% 37.8% 29.1% 27.4% 6.4% 2.9% 27.4% 55.5% 74.8% 77.3% 116.1% $ % $ % Ca Inc CA $ % 6.7% 3.7% 10.3% 28.7% 56.8% 4.1% 12.7% 21.4% 35.5% 21.3% 53.0% $ % $ % Citrix Systems Inc CTXS $ % 13.8% 4.3% 16.7% 16.8% 83.5% 2.8% 7.8% 22.1% 62.5% 66.8% 26.2% $ % $ % Commvault Systems Inc CVLT $ % 24.4% 38.9% 19.3% 10.0% 186.6% 3.3% 30.4% 56.6% 26.4% 39.9% 76.8% $ % $ % Tableau Software Inc DATA $ % 4.6% 10.6% N/A N/A N/A 9.7% 10.6% 7.1% N/A N/A N/A $ % $ % Datawatch Corp DWCH $ % 6.1% 30.3% 4.6% 141.7% 706.6% 9.2% 12.1% 48.0% 41.2% 91.8% 596.9% $ % $ % Informatica Corp INFA $ % 8.6% 12.0% 15.9% 36.5% 84.3% 7.5% 14.5% 29.7% 29.9% 86.5% 25.4% $ % $ % Microsoft Corp MSFT $ % 5.1% 17.0% 44.2% 58.5% 73.7% 0.5% 0.9% 0.7% 1.6% 8.6% 36.1% $ % $ % Microstrategy Inc MSTR $ % 36.0% 44.8% 19.9% 17.6% 160.2% 4.8% 30.1% 27.1% 25.8% 67.5% 50.5% $ % $ % Oracle Corp ORCL $ % 1.0% 26.7% 37.3% 26.3% 87.3% 5.7% 4.9% 9.0% 8.4% 23.6% 22.4% $ % $ % Qlik Technologies Inc QLIK $ % 15.9% 31.9% 17.9% 29.4% N/A 10.6% 21.8% 49.6% 27.9% 79.3% N/A $ % $ % Red Hat Inc RHT $ % 7.4% 9.7% 4.7% 25.6% 160.7% 2.5% 1.5% 8.0% 41.1% 24.3% 51.0% $ % $ % Splunk Inc SPLK $ % 28.4% 7.1% 63.1% N/A N/A 7.5% 34.4% 24.8% 17.4% N/A N/A $ % $ % Solarwinds Inc SWI $ % 8.8% 12.1% 6.0% 60.4% 128.1% 5.3% 14.7% 29.8% 51.7% 10.5% 18.3% $ % $ % Tibco Software Inc TIBX $ % 2.2% 19.8% 28.8% 31.1% 144.3% 10.4% 8.2% 37.5% 74.6% 81.0% 34.5% $ % $ % Vmware Inc VMW $ % 7.3% 31.7% 12.3% 7.6% 216.3% 2.2% 13.3% 13.9% 33.4% 57.6% 106.5% $ % $ % Varonis Systems Inc VRNS $ % 15.8% N/A N/A N/A N/A 7.4% 21.8% N/A N/A N/A N/A $ % $ % Verint Systems Inc VRNT $ % 7.2% 31.1% 67.7% 36.3% 377.4% 5.1% 1.2% 13.3% 21.9% 13.6% 267.7% $ % $ % Verisign Inc VRSN $ % 1.4% 9.7% 15.3% 48.2% 153.8% 4.2% 7.4% 8.0% 30.5% 1.8% 44.0% $ % $ % Average 3.4% 4.3% 0.9% 14.8% 16.8% 174.2% 5.2% 10.2% 18.6% 31.0% 33.1% 64.5% 71.6% 121.1% Source: BMO Capital Markets and Thomson. Since the end of 4/25/14, TIBX, DATA, and VRNT have been the heaviest shorted infrastructure names, in the infrastructure space. While heavy short covering has taken place in CTXS, CVLT, and CA. Page 40 July 17, 2014

41 Exhibit 28. Short Interest Snapshot Short Interest As of: % Change From: 3 Month Avg Days Infrastructure Software Symbol 4/25/2014 5/25/2014 6/25/2014 4/25 6/25 5/25 6/25 Volume to Cover Actuate Corp BIRT 977,499 1,309,107 1,391, % 6.3% 585, Ca Inc CA 6,913,013 5,445,396 4,621, % 15.1% 2,705, Citrix Systems Inc CTXS 4,312,564 23,572,014 18,199, % 22.8% 3,025, Commvault Systems Inc CVLT 6,419,778 4,350,216 3,592, % 17.4% 843, Tableau Software Inc DATA 2,241,272 3,303,459 4,823, % 46.0% 1,947, Datawatch Corp DWCH 412, , , % 2.2% 191, Informatica Corp INFA 1,377,444 2,256,752 2,348, % 4.1% 888, Microsoft Corp MSFT 90,644,190 79,466,801 82,583, % 3.9% 28,252, Microstrategy Inc MSTR 285, , , % 2.6% 105, Oracle Corp ORCL 38,500,302 34,943,513 36,831, % 5.4% 13,660, Qlik Technologies Inc QLIK 9,993,674 11,002,760 10,827, % 1.6% 1,717, Red Hat Inc RHT 3,596,746 3,503,555 3,587, % 2.4% 1,468, Splunk Inc SPLK 4,962,244 7,110,892 6,801, % 4.4% 3,908, Solarwinds Inc SWI 2,603,010 2,813,406 3,013, % 7.1% 789, Tibco Software Inc TIBX 2,017,958 2,032,334 3,012, % 48.2% 2,840, Vmware Inc VMW 17,477,203 16,688,318 17,464, % 4.7% 1,475, Varonis Systems Inc VRNS 244, , , % 19.7% 158, Verint Systems Inc VRNT 933,697 1,024,159 1,319, % 28.8% 475, Verisign Inc VRSN 20,056,366 20,035,781 22,922, % 14.4% 1,259, Source: BMO Capital Markets and Thomson Company Comparables Comparables for analytics vendors are currently based on EV/Sales due to their growth characteristics and low levels of profitability. EV/FCF becomes a secondary valuation metric as companies reach scale. EV/Sales multiples for growth names have come in. Relative to our coverage universe we believe Tableau can hold its current peer average multiple given what we see as the potential for 40%-plus for the next few years in an upside case. Slunk has an unfavorable setup with a premium multiple in our view. Qlik has a balanced risk reward, and we d be more constructive at ~2.0x. Page 41 July 17, 2014

42 Exhibit 29. Comparables ($ in millions, except per share) Recent Market EV/SALES EV/FCF P/E Rev. Growth Company Name Ticker Rating Price Cap Infrastructure Software Actuate Corp BIRT NC $ 4.45 $ x 1.5x NA NA NM NM 25.5% 2.1% Ca Inc CA NC $ $12, x 2.5x NA NA 11.4x 10.9x 2.2% 0.9% Citrix Systems Inc CTXS Market Perform $ $11, x 3.2x 15.6x 14.4x 20.6x 17.4x 9.0% 9.3% Commvault Systems Inc CVLT Outperform $ $2, x 2.5x 22.2x 11.6x 24.8x 21.5x 15.0% 14.1% Tableau Software Inc DATA Outperform $ $3, x 6.7x NM NM NM NM 53.7% 35.8% Datawatch Corp DWCH NC $ $ x 1.6x NA NA NM NM 19.7% 25.1% Informatica Corp INFA NC $ $3, x 2.6x NA NA 20.7x 18.1x 11.9% 11.9% Microsoft Corp MSFT NC $ $368, x 3.0x NA NA 15.4x 14.1x 11.2% 18.3% Microstrategy Inc MSTR NC $ $1, x 1.9x NA NA NM NM 6.2% 7.0% Oracle Corp ORCL Outperform $ $183, x 4.0x 11.7x 11.2x 12.6x 11.8x 5.5% 4.5% Qlik Technologies Inc QLIK Market Perform $ $1, x 2.6x NM NM NM NM 15.9% 15.5% Red Hat Inc RHT Outperform $ $10, x 4.5x 17.0x 14.0x 35.9x 30.8x 15.6% 13.0% Splunk Inc SPLK Market Perform $ $5, x 8.4x NM NM NM NM 35.8% 32.9% Solarwinds Inc SWI NC $ $2, x 5.5x NA NA 23.2x 19.9x 24.2% 18.2% Tibco Software Inc TIBX NC $ $3, x 2.7x NA NA 22.8x 19.5x 2.5% 6.3% Vmware Inc VMW Market Perform $ $40, x 5.1x NA NA 26.7x 22.2x 15.7% 15.8% Varonis Systems Inc VRNS NC $ $ x 3.4x NA NA NM NM 30.8% 26.4% Verint Systems Inc VRNT NC $ $2, x 2.8x NA NA 14.2x 13.1x 25.4% 9.0% Verisign Inc VRSN NC $ $7, x 6.6x NA NA 18.8x 16.6x 4.9% 5.1% Average 4.4x 3.7x 16.6x 12.8x 20.6x 18.0x 14.5% 14.0% (Fishbein - CTXS, CVLT, DATA, ORCL, QLIK, RHT, SPLK) (Bachman - VMW) (NC = Not covered. Thomson data for not covered companies) Estimates reflect latest complete and forward fiscal years Source: BMO Capital Markets and Thomson. Exhibit 30. Comparables: Margins and Growth ($ in millions, except per share) Rev. Growth EPS Growth FCF Growth Gross Margin EBIT Margin FCF Margins Company Name Ticker '13 '14E '14 '15E '13 '14E '14 '15E '13 '14E '14 '15E 2014E 2015E 2014E 2015E 2014E 2015E Infrastructure Software Actuate Corp BIRT 26% 2% NA NA NA NA NA NA NA NA NA NA Ca Inc CA 2% 1% NA 4% NA NA NA NA NA NA NA NA Citrix Systems Inc CTXS 9% 9% 2% 18% 5% 8% 84% 84% 22% 23% 22% 22% Commvault Systems Inc CVLT 15% 14% 1% 16% 68% 92% 87% 87% 23% 25% 13% 22% Tableau Software Inc DATA 54% 36% NA NA NA NA 91% 90% 0% 2% 0% 5% Datawatch Corp DWCH 20% 25% NA NA NA NA NA NA NA NA NA NA Informatica Corp INFA 12% 12% 24% 14% NA NA NA NA NA NA NA NA Microsoft Corp MSFT 11% 18% 6% 9% NA NA NA NA NA NA NA NA Microstrategy Inc MSTR 6% 7% NA NA NA NA NA NA NA NA NA NA Oracle Corp ORCL 6% 5% 12% 6% 1% 5% 95% 95% 93% 93% 36% 36% Qlik Technologies Inc QLIK 16% 16% 11% 48% 4% 90% 87% 87% 6% 8% 3% 5% Red Hat Inc RHT 16% 13% 3% 16% 16% 21% 86% 87% 23% 24% 30% 32% Splunk Inc SPLK 36% 33% NA NA 8% 36% 90% 90% 0% 2% 17% 17% Solarwinds Inc SWI 24% 18% 21% 17% NA NA NA NA NA NA NA NA Tibco Software Inc TIBX 3% 6% 27% 17% NA NA NA NA NA NA NA NA Vmware Inc VMW 16% 16% NA NA NA NA NA NA NA NA NA NA Verint Systems Inc VRNT 25% 9% 158% 9% NA NA NA NA NA NA NA NA Verisign Inc VRSN 5% 5% 94% 13% NA NA NA NA NA NA NA NA Average 14% 13% 26% 16% 15% 42% 89% 89% 24% 25% 17% 20% Source: BMO Capital Markets and Thomson Page 42 July 17, 2014

43 Billion-Dollar Private Company Valuations A tremendous amount of capital has been committed by venture and strategic investors within the big data ecosystem. By our count more than 35 companies have raised over $50 million and more than 10 have raised over $100 million. We expect that several of these companies will make their way to the public markets with M&A being the primary exit for the sector. The companies have staked their positions, and the question is, which will emerge as leaders and which investments will not pan out, given how competitive the space appears. According to CB insights, $4.9 billion was invested in Big Data Companies in , and $3.6 billion in 2013 alone. The most active venture firms in Big Data are Lightspeed Venture partners, Sequoia Capital, New Enterprise Associates, Khosla Ventures, IA Ventures, General Catalyst Partners, Accel Partners, Battery Ventures, Andreessen Horowitz, and Greylock Partners. Exhibit 31. Big Data Companies With $100 Million-Plus in Venture Funding $1,000 Venture Funding ($M) $800 $600 $400 $200 $0 *Cloudera partial secondary offering Source: BMO Capital Markets Research. Page 43 July 17, 2014

44 Key Private Security Companies to Watch Below we have listed the private companies that we believe will be the ones to watch in the coming years. They have been selected based on a variety of criteria, including market positioning, targeted market, product offering, customer base, and management. In this continually evolving environment, we believe investors should keep these select companies on their radar screens, as they have the potential to gain an increasing presence in the marketplace. During the enormous effort in making this report, we are sure to have missed giving credit to many companies and/or technologies either by design, in order to make the report easy to digest, or by mistake, due to the sheer volume of information that we had to process. Page 44 July 17, 2014

45 1010data Company Description 1010data is leading provider of big data discovery and data sharing solutions. It is used by hundreds of the world s largest retail, manufacturing, telecom, and financial services enterprises because of its proven ability to deliver actionable insight from very large amounts of data more quickly, easily and inexpensively than any other solution. The company has over 600 customers, thousands of users, and 16 trillion rows of data under management. Key Products 1010data Big Data Discovery Unifies data and analytics on its platform allowing business users to perform analysis on data in the same place as it is stored. 1010data Analytical Platform as a Service (APaaS) A powerful analytical database providing Data discovery, advanced analytics and predictive modeling capabilities; User interfaces for browser-based and mobile dashboarding, reporting, and analysis; Rapid, sophisticated data integration capabilities to connect to any traditional or big data source; Enterprise-grade security, reliability, and interoperability; Cost-effective, on-demand performance and storage scalability 1010data Advanced Analytics - Includes a powerful array of advanced, built-in analytic functions including Statistics, Predictive modeling and forecasting, Machine learning, etc. 1010data Data Sharing & Monetization Enables organizations to securely share data and analytics with partners, retaining full control over access, permissions, and usage rights granted to every member of other organizations. 1010data Industry-Specific Applications Vertical applications include Financial Services, Gaming & Hospitality, Healthcare, Retail & CPG, Telecom, Government Key Employees Sandy Steier- CEO and Co-founder Joel Kaplan- Chairman, CTO and Co-founder Greg Munves - EVP and Chief Revenue Officer Dr. Adam Jacobs- Chief Scientist T.C. Fleming - CFO Key Investors Norwest Venture Partners Page 45 July 17, 2014

46 Actian Corporation Company Description Actian Corporation enables organizations to transform big data into business value with data management solutions to transact, analyze, and take automated action across their business operations. Its next generation Actian Analytics Platform software delivers extreme performance, scalability, and agility on off-the-shelf hardware, overcoming key technical and economic barriers to broad adoption of big data. Key Products Actian Analytics Platform is purpose built to accelerate analytics from simple analytics like historical reporting to more sophisticated discovery analytics on Hadoop. Actian offers three purpose-built Operational Databases Ingres 10S, PSQL, and Versant. Actian also offers OpenROAD a database-centric, object-oriented, 4GL application development tool for developing and deploying mission-critical, n-tier business applications against databases such as Ingres, Microsoft SQL Server, Oracle, and DB2 UDB. Key Employees Steve Shine - CEO and President Steven Springsteel - CFO Mike Hoskins - CTO Mark Milani SVP, Product Engineering Ashish Gupta - CMO and SVP, Business Development Key Investors Garnett & Helfrich Capital Page 46 July 17, 2014

47 Adaptive Insights Company Description Adaptive Insights is a leader in cloud-based business and financial analytics solutions for companies and nonprofits of all sizes. The company s software as a service (SaaS) platform allows finance and management teams to work together to plan, monitor, report on, and analyze financial and operational performance. Adaptive Insights is used by over 2,000 organizations worldwide. Its customers and partners are in 85 countries worldwide. Partners include Armanino, Intacct, IntuitiveTek, Plex Systems, SAP, and NetSuite, which offers a specialized version of Adaptive Insights as the NetSuite Financial Planning Module. Key Products Adaptive Planning provides comprehensive budgeting, planning, and forecasting. Adaptive Consolidation cut time and resources spent closing and reporting. Adaptive Discovery helps users visually uncover insights into financial, sales, and operational performance. Adaptive Reporting provides comprehensive financial, management, board and transactional reporting, all available through an easy to use drag-and-drop report builder. Adaptive Integration provides a comprehensive range of connectors to leading ERP and CRM data sources. Key Employees John Herr CEO Robert S. Hull Founder, President David Pefley CFO Neil Thomas SVP of Worldwide Sales Greg Schneider SVP, Marketing Maurizio Gianola SVP, Engineering & Hosted Operations Amy Reichanadter SVP, People and Human Resources Carolee Gearhart VP of Global Channels & Business Development Keli Forsman VP, Professional Services Connie DeWitt VP, Product Management Key Investors Bessemer Venture Partners (BVP) Norwest Venture Partners (NVP) Royal Bank of Canada (RBC) ONSET Ventures Monitor Ventures Cardinal Venture Capital. Page 47 July 17, 2014

48 Alert Logic Company Description Alert Logic, is a leading provider of Security-as-a-Service solutions for the cloud, provides solutions to secure the application and infrastructure stack. Alert Logic solutions include day-to-day management of security infrastructure, security experts translating complex data into actionable insight, and flexible deployment options to address customer security needs in any computing environment. Alert Logic partners with over half of the largest cloud and hosting service providers to provide Security-as-a-Service solutions for business application deployments for over 2,500 enterprises. The company is on a $56 million dollar revenue run rate, grew 35% in 1Q14, and is tracking ahead of the company s plan to reach a $60 million runrate by the end of Q Key Products Threat Manager s managed intrusion detection and vulnerability scanning services provide ongoing insights into the threats and vulnerabilities affecting your environment. Log Manager collects and normalizes log data from your entire infrastructure and presents it in a single view, within our intuitive web interface that includes 100+ pre-built reports and powerful analytical tools. Web Security Manager provides two levels of protection: signature-based protection against known attacks and positive protection against unknown attacks by only allowing permitted actions. ScanWatch services from Alert Logic gives managed scanning that s easy to implement and use, helping identify and correct vulnerabilities before they are exploited. Alert Logic s ActiveWatch services solve the shelfware problem by offering fully managed intrusion detection, vulnerability scanning and web application firewall solutions. LogReview service provides daily analyst review of log data, meeting the most time- and resource-consuming requirement of PCI DSS. Key Employees Gray Hall - Chairman & CEO Paul Marvin CFO Greg Davis SVP of Worldwide Sales Ben Matheson - CMO Dave Colesante SVP of Platform & Technologies Services & CTO Marty McGuffin SVP of Operations Misha Govshteyn - Chief Strategy Officer & Co-Founder Key Investors Welsh Carson Page 48 July 17, 2014

49 Anaplan Company Description Anaplan is disrupting the world of business planning and execution. Its mission is to change the way companies around the world align people and plans to market opportunities. The core of the Anaplan platform is its patented HyperBlock Architecture, an in-memory modeling and calculation engine. Anaplan s cloud-based platform allows companies to continuously align people, plans and spend to market opportunities. Key Products Anaplan for Sales - Drives sales productivity and effectiveness by aligning all the moving parts of the process target setting, territory, quota, rep coverage, overlays, channel partners, commissions, forecasting, etc.. Anaplan for Operations - Drives forward thinking, data driven planning with integrated operational plans combining plans with management metrics, executive reporting, and corporate reporting. Anaplan for Finance - Rapidly deploy financial models linked to a continuous stream of operational data to monitor and optimize critical drivers across the enterprise. Key Employees Frederic Laluyaux - President & CEO Michael Gould - CTO Alan Priest - CFO Doug Smith - EVP Key Investors DFJ Growth Brookside Capital Coatue Management Sands Capital Management Workday Granite Ventures Meritech Capital Partners salesforce.com Shasta Ventures Page 49 July 17, 2014

50 Applied Predictive Technologies (APT) Company Description Applied Predictive Technologies (APT) is a cloud-based predictive analytics software company. APT s Test & Learn software is revolutionizing the way Global 2000 companies harness their big data to accurately measure the profit impact of pricing, marketing, merchandising, operations, and capital initiatives, tailoring investments in these areas to maximize ROI. Key Products APT s Test & Learn solution enables executives to accurately measure the profit impact of pricing, marketing, merchandising, operations, and capital initiatives, tailoring ongoing investments in these areas to maximize ROI. Key Employees Anthony Bruce - CEO Jim Manzi Chairman Scott Setrakian - Managing Director Patrick O'Reilly - President & COO Jeff Babka - CFO Andrew Fedorchek - CTO Key Investors Accel/KKR Goldman Sachs Page 50 July 17, 2014

51 Ayasdi Company Description Ayasdi automatically discovers and operationalizes insights from complex datasets. Founded in 2008 after a decade of DARPA and NSF funded research at Stanford, the Ayasdi Platform executes and merges the results of hundreds of machine learning algorithms using Topological Data Analysis (TDA), enabling users to explore their data within interactive and intuitive applications. Key Products The Ayasdi Platform enables data scientists and domain experts to explore their data within interactive and intuitive applications, empowering any user to derive operational value from complex data. Utilizing Topological Data Analysis (TDA), Ayasdi unifies best-of-breed machine learning approaches into a common framework without the need to write algorithms, queries or models. Key Employees Gurjeet Singh - Co-Founder & CEO Gunnar Carlsson- Co-Founder Harlan Sexton - Co-Founder and Vice President, Research Gary Hagmueller - CFO Patrick Rogers - CMO Key Investors Khosla Ventures Institutional Venture Partners GE Ventures Citi Ventures FLOODGATE Page 51 July 17, 2014

52 Attivio Company Description Attivio makes information meaningful, accessible, and actionable. Its patented Active Intelligence Engine (AIE) brings together information from any source or format and enriches it to expose the relationships, patterns, and insights that are hidden within. Its clients use the platform as the basis for informationdriven solutions that dramatically improve the speed, certainty, and effectiveness of key business decisions. Key Products Attivio s Active Intelligence Engine (AIE) allows companies to access, analyze, and act upon all relevant information, with complete 360-degree insight into any topic. Key Employees Ali I. Riaz - CEO and a Co-founder Sid Probstein - CTO and a co-founder Alan Cooke - CFO and General Counsel Stephen Baker - COO Tom Stephens - SVP of Strategy and Business Development Tom Higgins - SVP of Global Operations Matt brings - SVP of channels and alliances Gopal Nagarajan SVP of Client Delivery Will Johnson - Chief Architect and co-founder Martin Serrano- Co-founder and Chief Architect Rik Tamm-Daniels - VP of Technology and co-founder Key Investors Oak Investment Partners General Electric Pension Trust Tenth Avenue Holdings Page 52 July 17, 2014

53 Appistry Company Description Appistry makes genomics data easier for researchers and clinicians to act on. Appistry CloudDx provides cost-effective access to established, laboratory developed tests so that clinically relevant genomics-based information can be usable by any physician, anywhere. Appistry also empowers researchers by providing world-class bioinformatics tools, cloud services, and software that streamline the analysis of next-generation sequencing data and provide easy scale for moving researchdeveloped pipelines into production. Key Products Genomics Research Tools - Appistry s commercial tool suites provide the most accurate and flexible tools for analyzing whole genomes, exomes, and tumor-normal pair data and annotating genetic variation. Clinical Genomics Implementation - Ayrris organizes NGS tools, data, and workflows while providing behind-the-scenes, big-data capabilities for developing, managing, and executing complex analytics at scale. Genomically Enhanced Medicine - Appistry CloudDx provides a single point of access to established laboratory developed tests (LDTs) developed by premiere hospitals and medical research centers for a range of diseases including cancer, heart disease, and pediatric conditions. Key Employees Kevin Haar - President & Chief Executive Officer Trevor Heritage - VP of Corporate Development and Strategy Rich Mazzarella, Ph.D. - Chief Scientific Officer Jean Roberson - CFO Mark Lien - SVP Michael Groner - VP of Engineering and Chief Architect Heath Moylan - VP of Cloud Operations Patrick Geritz - VP of North American Sales Key Investors exome Capital Stuart Mill Venture Partners Bush O Donnell & Co. Page 53 July 17, 2014

54 Birst Company Description Birst is an enterprise-caliber business intelligence platform in the cloud. Birst is engineered with an automated data warehouse and rich, visual analytics, to give meaning to data all types and sizes. Key Products Birst provides a fully integrated designed for Software-as-a- Service (SaaS) cloud delivery and on-premise deployment as a software appliance. Its platform combines all the essential components to develop, deploy, and maintain enterprise-caliber reporting, dashboarding, visualization, and data discovery solutions. All components share a single unified logical layer of metadata and consistent end user experience. Key Employees Jay Larson CEO Brad Peters - Chairman and Chief Product Officer Paul Staelin - Chief Customer Officer and Co-founder Rick Spickelmier - CTO Mike Brown - VP of Finance & Operations Southard Jones - VP of Product Strategy Ryan Ried - VP of Worldwide Sales Junaid Saiyed - VP of Product Management & Engineering Wynn White - VP of Marketing Sharon Gordon - VP of Alliances and Technology Partnerships David Gray - VP of International Key Investors Hummer Winblad Venture Partners DAG Ventures Sequoia Capital Northgate Capital Page 54 July 17, 2014

55 Clarabridge Company Description Clarabridge is the leading provider of intelligent customer experience management (CEM) solutions for the world s top brands. Intelligent customer experience by Clarabridge, or Clarabridge ICE was built specifically to enable businesses to listen, analyze, and act on the voice of the customer intelligently. As the premier provider of CEM, Clarabridge enables Global 1,000 businesses to intelligently listen to, analyze, operationalize, and measure multi-source customer feedback, through intelligent sentiment and text analytics. Insights extracted through Clarabridge enable organizations to create a universal understanding of their customers, partners, and employees; make actionable business decisions with measurable ROI, and collaborate on those decisions both internally with stakeholders and externally with customers. Key Products Clarabridge Intelligence Platform - Clarabridge Intelligence Platform is the brains behind Clarabridge ICE. It comprises VOC Data Engines, algorithms, Clarabridge Connect, and templates. Clarabridge Analyze - Clarabridge Analyze makes CEM faster by automating and refining the process of evaluating customer feedback. Clarabridge Act - Clarabridge Act gives companies that are serious about customer centricity a solution to empower business users, store managers, call center staff, and social care agents to improve loyalty and increase SAT scores. Key Employees Sid Banerjee- CEO and Co-Founder Lenny Nash General Manager and Co-founder Bas Brukx - CFO Key Investors Summit Partners General Catalyst Partners Page 55 July 17, 2014

56 ClearStory Data Company Description ClearStory Data's solution offers a new way for business users to easily discover, analyze, and consume data at scale from corporate, web and premium data sources for combined and upto-date insights. Data sources include relational databases, Hadoop, Web and social application interfaces, and third-party data providers. ClearStory Data's platform modernizes data analysis by introducing a new user model for big data analysis. The platform simplifies access to disparate data sources, automatically manages data harmonization, and enables interactive analysis at scale. With ClearStory's solution, organizations can easily converge data from corporate and thirdparty sources to make business decisions faster and across distributed teams in ways never before possible. Key Products Underlying the ClearStory Application, is an integrated Platform that s a scalable, distributed processing system that understands data sources and speeds access and diverse data convergence. The ClearStory Platform gathers top-down signals of a user s analysis intent and matches that intent dynamically to one or more data sets registered in the Platform. Flexibility in converged data execution is enabled through Intelligent Data Harmonization across internal and external data sources (structured and un-structured data sources) registered in the ClearStory Data catalog. Key Employees Sharmila Mulligan CEO and Founder Vaibhav Nivargi - Founder Key Investors Andreessen Horowitz Google Ventures Khosla Ventures Kleiner Perkins Caufield & Byers DAG Ventures Page 56 July 17, 2014

57 Cloudera Company Description Cloudera offers a unified platform for big data, an enterprise data hub built on Apache Hadoop. Cloudera offers enterprises one place to store, process, and analyze all their data, empowering them to extend the value of existing investments while enabling fundamental new ways to derive value from their data. Cloudera offers software for business critical data challenges, such as storage, access, management, analysis, security and search. Key Products Cloudera Enterprise helps organizations become informationdriven by leveraging the best of the open source community with the enterprise capabilities for Apache Hadoop. Cloudera Enterprise includes CDH, the world s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from its team of Hadoop developers and experts. Cloudera Enterprise also offers support for several advanced components that extend and complement the value of Apache Hadoop including HBase, Impala, Cloudera Search, Spark, Cloudera Navigator. Key Employees Tom Reilly - CEO Mike Olson - Chief Strategy Officer and Co-founder Kirk Dunn - COO Jim Frankola CFO Amr Awadallah, Ph.D. CTO and Co-founder Doug Cutting - Chief Architect Jeff Hammerbacher - Chief Scientist and Co-founder Key Investors Intel Google Ventures T. Rowe Price Accel Partners Ignition Partners Greylock Partners Meritech Capital Partners In-Q-Tel MSD Capital Page 57 July 17, 2014

58 Clustrix Company Description Clustrix is the leading scale-out SQL database, engineered for the cloud. Clustrix provides a simple SQL database that enables applications to scale to unlimited users, transactions and data, while eliminating database sharding and automating fault tolerance. Clustrix software is in production both as an appliance and on Amazon Web Services with more than one trillion transactions per month running through Clustrix databases worldwide. Key Products ClustrixDB is a distributed SQL database built for large-scale and fast-growing applications. ClustrixDB is designed to run massive transaction volume and fast real-time analytics, both at the same time. Designed for the cloud, ClustrixDB offers built-in high availability and is largely self-managing. Key Employees Don Listwin - CEO and Chairman Sergei Tsarev - Co-Founder and CTO Mike Azevedo - Vice President of Sales Tony Barbagallo - CMO Key Investors HighBar Partners Sequoia Capital U.S. Venture Partners ATA Ventures Y Combinator Page 58 July 17, 2014

59 Couchbase Company Description Couchbase provides a high performing and scalable NoSQL database. It includes a shared nothing architecture, a single nodetype, a built in caching layer, true auto-sharding and a NoSQL mobile offering. Couchbase Server and all Couchbase Mobile products are open source projects. Key Products Couchbase Server, is a packaged version of Couchbase technology that s available in Community and Enterprise Editions. Couchbase Mobile, a complete NoSQL mobile solution comprised of Couchbase Server, Couchbase Sync Gateway, and Couchbase Lite. Key Employees Bob Wiederhold - President and CEO Rahim Yaseen - SVP, Product Development Sujan Jain - SVP & Chief Financial Officer Key Investors Accel Partners Adams Street Partners Ignition Partners Mayfield Fund North Bridge Venture Partners WestSummit Page 59 July 17, 2014

60 Datameer Company Description Datameer is the only end-to-end big data analytics application purpose-built for Hadoop that enables the fastest time from raw data to new insights. Its mission is to simplify the complexity of big data analytics and empower everyone to make data-driven decisions in minutes, not months. They believe you shouldn t need a data scientist or multiple, technical tools to model, integrate, cleanse, prepare, analyze, and visualize your data. Datameer is the one-stop-shop for getting all data into Hadoop, analyzing that data, and visualizing your results. Key Products Datameer is a single application for data analytics providing data integration, dynamic data management, and self-service analytics. The product comes in three versions Personal, Workgroup, and Enterprise. Key Employees Stefan Groschupf - CEO Frank Henze - VP of Product Management Peter Voss - CTO Azita Martin - CMO Key Investors Citi Ventures Kleiner Perkins Caufield & Byers Next World Capital Redpoint Ventures Software AG Workday Page 60 July 17, 2014

61 Databricks Company Description Databricks was founded out of the UC Berkeley AMPLab by the creators of Apache Spark. Databricks builds software, centered around Apache Spark and Shark for analyzing and extracting value from data. Key Products Databricks Cloud provides a fully managed service for big data, running 100% open source Apache Spark. Key Employees Ion Stoica CEO Matei Zaharia - CTO Key Investors Andreessen Horowitz NEA Page 61 July 17, 2014

62 DataSift Company Description DataSift Inc. is a leading social data platform with more than 1,000 customers in 40+ countries. DataSift is driving a revolution in corporate decision-making by making it easy for applications to incorporate the use of real time and historical Social data intelligently and easily into data analysis. DataSift powers hundreds of applications across a wide variety of industries including retail, telco, high tech, consumer packaged goods and agencies. Key Products The DataSift platform enables companies to easily aggregate, filter and extract useful data from the billions of public social conversations on Twitter, other leading social networks and millions of other sources. It provides products for developers, agencies, and enterprises. Key Employees Rob Bailey CEO Nick Halstead Founder and CTO Steven Pease CFO and SVP Operations Tim Barker - Chief Product Officer Jason Rose SVP of Marketing Ming Wu VP of Client Solutions Lorenzo Alberton Chief Technical Architect Andrew Jackson VP of Engineering Tim Budden Director of Data Science Jon Oelman VP of Business Development Key Investors Insight Venture Partners Scale Venture Partners Upfront Ventures IA Ventures Page 62 July 17, 2014

63 DataStax Company Description DataStax is a leading distributed database management system, based on commercially supported Apache Cassandra the open source NoSQL database technology to more than 500 customers in 45 countries. Key Products DataStax Enterprise, built on Cassandra, provides the massive scalability, continuous availability and enhanced data security today s sophisticated big data applications demand. With inmemory computing capabilities, enterprise-level security, fast and powerful integrated analytics and enterprise search, visual management, and expert support, DataStax Enterprise is a leading distributed database choice for online applications that require fast performance with no downtime. Key Employees Billy Bosworth - CEO Jonathan Ellis CTO and Co-founder Matt Pfeil - Chief Customer Officer and Co-founder Dennis Wolf - CFO John Schweitzer - EVP of Worldwide Operations Tony Kavanagh - CMO Martin Van Ryswyk - EVP of Engineering Key Investors Lightspeed Venture Partners Meritech Capital Crosslink Capital Scale Venture Partners DFJ Growth Next World Capital Page 63 July 17, 2014

64 Delphix Company Description Delphix is a market leader in Agile Data, which helps industry leading companies around the world deliver critical application projects on time and on quality. Delphix enables Agile Data Management through intelligent software that eliminates redundant infrastructure and slow processes. Key Products The Delphix Agile Data Platform delivers the right data to the right team at the right time. By virtualizing the entire application stack (binaries, files, and databases) Delphix quickly delivers full environments for development, testing, and reporting needs. Delphix overcomes organization, process, and location bottlenecks with a comprehensive data delivery platform. Delphix drives improved utilization of hardware and software systems for application projects. The Delphix Compliance Engine accelerates report delivery while shedding associated costs by offloading all phases of compliance reporting to on-demand, space-efficient virtual copies. Delphix Modernization Engine mitigates risk in application rationalization and modernization projects. Key Employees Jedidiah Yueh - President and CEO Kevin Mosher - EVP, Worldwide Field Operations Adam Leventhal - CTO Key Investors In-Q-Tel Lightspeed Venture Partners Battery Ventures Greylock Partners Summit Partners Jafco Ventures Page 64 July 17, 2014

65 Domo Company Description Domo is a cloud-based executive management platform that gives users direct, real-time access to all the business information they care about, all in one place. Key Products Domo brings business and its data together in one intuitive platform. Domo allows users to quickly and easily connect to any source of data, no matter what it is or where it lives, with the ability to discover, mash up, visualize and present data any. Key Employees Josh James - Founder, CEO & Chairman of the Board Chris Harrington President Daren Thayne - CTO Heather Zynczak - CMO Steve Wellen - COO Matt Belkin - Chief Strategic Solutions Officer Catherine Wong - SVP of Engineering Key Investors Benchmark Fidelity Investments Founders Fund GGV Capital Greylock Partners IVP salesforce.com TPG Growth T. Rowe Price WPP Zetta Venture Partners Page 65 July 17, 2014

66 Elasticsearch Company Description Elasticsearch is on a mission to make massive amounts of data usable for businesses everywhere by delivering the world s most advanced search and analytics engine. Used by thousands of enterprises in virtually every industry today, Elasticsearch, Inc. provides production support, development support and training for the full ELK stack. Elasticsearch, Inc. was founded in 2012 by the people behind the Elasticsearch and Apache Lucene open source projects. Since its initial release, Elasticsearch has more than 8 million cumulative downloads. Key Products The Elasticsearch ELK stack comprises Elasticsearch, Logstash and Kibana has become one of the most popular and rapidly-growing open source solutions in the market. Built and supported by the engineers behind each of these open source projects, the Elasticsearch ELK stack makes searching and analyzing data, even log files, easy. Key Employees Steven Schuurman, CEO Shay Banon - Co-founder and CTO Nick White - CFO Jen Grant - CMO Key Investors Benchmark Capital Index Ventures NEA Page 66 July 17, 2014

67 EnterpriseDB Company Description EnterpriseDB is the leading provider of enterprise-class products and services based on PostgreSQL, the world's most advanced open source database. EnterpriseDB also employs a number of industry thought leaders and more of the PostgreSQL open source community's core team than any other company, providing customers unparalleled expertise in our software subscriptions, 24x7 support, consulting, and training services. More than 2,500 enterprises, governments and other organizations worldwide use EnterpriseDB software, support, training, and professional services to integrate open source software into their existing data infrastructures. Key Products Postgres Plus Standard Server is an enterprise ready version of community PostgreSQL complete with enterprise modules for performance and scalability while our database compatibility enhancements in Postgres Plus Advanced Server enable seamless migrations from Oracle that save up to 90% of the cost. Key Employees Ed Boyajian - President and CEO Keith Alsheimer - CMO Marc Linster SVP, Products and Services Radhika Samant - CFO Sean Doherty- SVP, Global Sales and Business Development Key Investors Charles River Ventures Translink Capital Valhalla Partners Volition Capital RedHat IBM Page 67 July 17, 2014

68 GoodData Company Description GoodData, the a leader in end-to-end cloud analytics, enabling more than 40,000 companies to store, combine, analyze and visualize data to quickly answer business-critical questions. GoodData s mission is to help companies become all data enterprises: companies that gain a competitive advantage by leveraging all of the data available to them through advanced analytics. Key Products GoodData Open Analytics Platform - helps companies manage and analyze that data in one seamless, interactive environment. Powered By GoodData - lets users embed GoodData into existing applications or create new applications. GoodData BI Dashboards - on-demand business intelligence tools and dashboards prebuilt for specific business units or applications. Key Employees Roman Stanek - CEO and Founder Radovan Janecek - SVP, R&D & Operations Michael Gear - SVP, Field Operations Divya Ghatak - Chief People Officer Ran Van Riper - SVP, Global Services Zdenek Svoboda - VP, Platform Jeff Morris - VP, Product Marketing Vishal Save - VP, Finance & Business Operations Key Investors Andreessen Horowitz Fidelity Growth Partners General Catalyst Partners Next World Capital Tenaya Capital TOTVS SA Windcrest Partners Page 68 July 17, 2014

69 Guavus Company Description Guavus is a big data analytics company for service providers and enterprise customers. The company offers an operational intelligence platform integrated with a suite of decisioning applications for network planning and operations, marketing, security, and customer care. The company counts 4 of the top 5 mobile network operators, 3 of the top 5 Internet Backbone providers, as well as 80% of cable MSOs in North America as customers. It currently analyzes more than 50% of all US mobile data traffic and processes more than 2.5 petabytes of data per day. Key Products The Guavus Reflex Platform is capable of creating actionable information from widely distributed, high volume data streams in near real-time. The resulting live analytics are powerful business metrics generated from responsive querying over huge volumes of high cardinality data. Reflex uses highly optimized computational algorithms and machine learning to distill actionable insights from very large datasets. Key Employees Anukool Lakhina - CEO & Founder Mike Staiger - CFO Ty Nam EVP, Sales Roy Singh - CTO Eric Carr - VP, Core Systems Group François de Repentigny - VP, Marketing Anita Gupta - VP, Human Resources & Operations Key Investors Artiman Ventures Sofinnova Ventures Intel Capital Investor Growth Capital (IGC) QuestMark Partners Goldman Sachs TransLink Capital Page 69 July 17, 2014

70 Hortonworks Company Description Hortonworks develops, distributes, and supports the only 100% open source Apache Hadoop data platform. Its team comprises the largest contingent of builders and architects within the Hadoop ecosystem who represent and lead the broader enterprise requirements within these communities. The Hortonworks Data Platform provides an open platform that deeply integrates with existing IT investments and upon which enterprises can build and deploy Hadoop-based applications. Key Products Hortonworks Data Platform enables Enterprise Hadoop: the full suite of essential Hadoop capabilities that are required by the enterprise and that serve as the functional definition of any data platform technology. This comprehensive set of capabilities is aligned to the following functional areas: Data management, data access, data governance and integration, security, and operations. HDP provides the broadest range of deployment options for Hadoop: from Windows server or Linux to virtualized cloud deployments. Key Employees Rob Bearden CEO Herb Cunitz President Scott Davidson CFO Ari Zilka CTO Shaun Connolly VP Corporate Strategy Mitch Ferguson VP Business Development Tim Hall VP Product Management David McJannet - VP Marketing Bob Page - VP Partner Product Management Dan Bradford - VP Finance Andy Knosp VP Professional Services Greg Pavlik VP Engineering Jeff Miller VP North American Sales Jamie Engesser VP Solutions Engineering Barry Duplantis VP Customer Success Key Investors Tenaya Capital Dragoneer Investment Group Benchmark Index Ventures Yahoo! Dragoneer Investment Group Passport Capital BlackRock Page 70 July 17, 2014

71 Host Analytics www hostanalytics.com Company Description Host Analytics is a leader in cloud-based financial applications for planning, consolidation, reporting, and analytics. Host Analytics enterprise performance management (EPM) customers benefit from improved business agility, improved security, and lower overall cost compared with legacy on-premises alternatives. World-class companies like NEC, Burlington Coat Factory, and Crocs trust Host Analytics to power their strategic financial processes. Key Products Host Analytics EPM Suite - Designed for planning, close, reporting, and analytics. Planning Cloud - Streamline planning, budgeting, and forecasting. Close Management Cloud - Lower audit and compliance costs because of better controls. Reporting Cloud - Professional-grade formatting with powerful ad hoc analysis. Analytics Cloud - Deliver insights that inform strategy setting, align the organization, and measure progress in real time. Key Employees Dave Kellogg CEO Ian Charles - CFO Lance Walter - CMO Ron Baden - VP of Services Mark Bauer - VP of Product Management Gary Hanna - VP of Sales Alison Holmlund - VP of Customers for Life Kulo Rajasekaran - VP of Engineering Andy Ross- VP of Alliances Key Investors Trident Capital StarVest Partners Advanced Technology Ventures Next World Capital Page 71 July 17, 2014

72 Logi Analytics Company Description Logi Analytics enables enterprises to raise their corporate IQ by delivering on the promise of analytics everywhere. By providing both a data discovery solution and a business information platform, Logi Analytics helps its customers address a broad range of use cases, enabling employees from the corner office to the factory floor to be more informed, make better decisions and drive corporate performance. With more than 1,200 customers worldwide, Logi Analytics meets the analytics needs of organizations ranging from small businesses to Global 2000 enterprises. Key Products Logi Info - A complete BI platform for developers to create customized, Web-based dashboards, reports, and analysis applications powered by Logi Analytics' elemental approach that eliminates the need to write code. Logi Info comes with pre-built elements and super elements that deliver application-style enduser capabilities. Logi Vision A data discovery application that is designed to give all business users in an organization the ability to analyze, visualize and socialize insights. Decision-making is accomplished through a recommendations approach that delivers best practices in data profiling and data visualization to make analysis easier for non-technical users. Logi Ad Hoc - A self-service Web application that enables business users to create, manage, and share dashboards, reports, and analyses. Key Employees Brett Jackson - President and CEO Arman Eshraghi - CTO and Founder Adrian D. Muniz - CFO Steven Schneider - Chief Product Officer Hari Srihari - SVP of Products & Solutions Dave Ploger - VP of Research & Development Brian Brinkmann - VP of Products Kevin Greene - VP of Business Development Ben Mathew - VP of Sales, North America Simon Ryan - Director of EMEA Key Investors Updata Partners GroTech Ventures Summit Partners LLR Partners Page 72 July 17, 2014

73 LogRhythm Company Description LogRhythm is an independent security intelligence company. The company s patented and award-winning Security Intelligence Platform, unifying next-gen SIEM, log management, file integrity monitoring, network forensics, and host forensics, allows organizations around the globe to detect and respond to breaches and the most sophisticated cyber. LogRhythm also provides compliance automation and assurance as well as IT predictive intelligence to Global 2000 organizations, government agencies and mid-sized businesses worldwide. Key Products LogRhythm is an enterprise-class platform that seamlessly combines SIEM, log management, file integrity monitoring and machine analytics, with host and network forensics, in a unified security intelligence platform. LogRhythm delivers: Next generation SIEM and log management Independent host forensics and file integrity monitoring Network forensics with application ID and full packet capture State-of-the art machine analytics Advanced correlation and pattern recognition Multi-dimensional user/host/network behavior anomaly detection Rapid, intelligent search Large data set analysis via visual analytics, pivot, and drill down Workflow enabled automatic response via LogRhythm's SmartResponse Integrated case management Key Employees Andy Grolnick - President & CEO Chris Petersen - CTO Founder Phillip Villella - Ph.D. Chief Scientist & Founder Mark Vellequette - CFO Mike Reagan - CMO Bill Smith - SVP of Worldwide Field Operations Ross Brewer - VP and Managing Director of International Markets David Anthony - VP of Customer Care Matt Winter - VP of Corporate & Business Development Chris Brazdziunas - VP of Software Engineering Nancy Reynolds - VP Americas Channel Sales Jorda (Jody) Cire, CPA - VP of Finance & Accounting Key Investors Adams Street Partners Siemens Venture Capital Grotech Ventures Croghan Investments Access Venture Partners High Country Venture Page 73 July 17, 2014

74 Lotame Company Description Lotame helps publishers, marketers and agencies drive Maximum Audience Impact by maximizing the way they collect, unify, protect and activate audience data. Leveraging its DMP as a central audience management engine, Lotame helps users monetize data, gain greater targeting efficiency, and drive higher campaign performance and revenue. Key Products DMP for Marketers - Lotame's unifying DMP provides advertisers and brands with a comprehensive understanding of the behaviors and actions of valuable audiences. DMP for Publishers - Using the Lotame DMP, publishers can better understand and activate site visitors, maximizing the value of every consumer who visits a site. Mobile Data Management Platform - Lotame offers a mobile data management platform and a suite of mobile DMP products Lotame Syndicate - Powers the seamless exchange of second-party data between participating Lotame customers. Lotame Syndicate creates a rich data ecosystem you can use to expand your use of audience data. Lotame Insights - Helps customers monitor the performance of your campaigns and understand how to drive actionable results. Lotame Optimizer - Takes the guesswork out of creating optimized campaigns by applying sophisticated learning and algorithms to large amounts of data. Lotame s Smart Data solution - gives publishers and marketers with 100% declared and demonstrated audience data to reach specific audiences across the web. Key Employees Andy Monfried - Founder and CEO Jeremy Pinkham - CTO Adam Lehman - President and COO Kevin Kohn - Chief Revenue Officer Mike Sullivan - CFO Eric Hastings - EVP of Technology Omar Abdala - Chief Data Scientist Key Investors Battery Ventures Emergence Capital Hillcrest Management, LLC Pinnacle Ventures Page 74 July 17, 2014

75 MapR www. MapR.com Company Description MapR provides an enterprise-grade platform that supports a broad set of mission-critical and real-time production uses. MapR brings dependability, ease-of-use and world-record speed to Hadoop, NoSQL, database and streaming applications in one unified distribution for Hadoop. MapR is used by more than 500 customers across financial services, government, healthcare, manufacturing, media, retail, and telecommunications as well as by leading Global 2000 and Web 2.0 companies. Amazon, Cisco, Google, and HP are part of the broad MapR partner ecosystem. Key Products MapR Distribution for Apache Hadoop provides organizations with an enterprise-grade distributed data platform to reliably store and process big data. MapR packages a broad set of Apache open source ecosystem projects enabling batch, interactive, or real-time applications. The data platform and the projects are all tied together through an advanced management console to monitor and manage the entire system. Key Employees John Schroeder - CEO and Co-founder M.C. Srivas - CTO and Co-founder Dan Atler CFO Jack Norris CMO Ted Dunning Chief Application Architect Steve Fitz SVP, Worldwide Field Operation Pinaki Mukerji SVP, Engineering Key Investors Google Capital Lightspeed Venture Partners Mayfield Fund NEA Qualcomm Ventures Redpoint Ventures Page 75 July 17, 2014

76 MarkLogic Company Description MarkLogic delivers a powerful, agile, and trusted Enterprise NoSQL database platform that enables organizations to turn all data into valuable and actionable information. Organizations around the world rely on MarkLogic s enterprise-grade technology to power the new generation of information applications. Key Products MarkLogic is an Enterprise NoSQL database, bringing multiple features into one unified system: a document-centric, schemaagnostic, structure-aware, clustered, transactional, secure, database server with built-in search, and a full suite of application services. MarkLogic is primarily a database, but it also has built-in search and a full suite of application services. MarkLogic can ingest multiple types of data that are immediately indexed for full-text search, and has REST and Java APIs to ensure developers can quickly and easily build a variety of applications. Key Employees Gary Bloom - CEO and President Jonathan Bakke - SVP, Global Services David Gorbet - VP, Engineering Christopher Lindblad - Founder Peter S. Norman CFO Michaline Todd - CMO Joe Pasqua - SVP, Product Strategy David Ponzini - SVP, Corporate Development Robert A. Roepke, Jr. - VP, Finance Elisa Smith - VP and General Counsel Key Investors Tenaya Capital Sequoia Capital Gary Bloom Northgate Capital Page 76 July 17, 2014

77 MemSQL Company Description MemSQL is a leader in real-time and historical big data analytics based on a distributed in-memory database. MemSQL is purpose built for instant access to real-time and historical data through a familiar SQL interface and uses a horizontally scalable distributed architecture that runs on commodity hardware. Innovative enterprises use MemSQL to accelerate time-to-value by extracting previously untapped value in their data that results in new revenue. MemSQL is proven in production environments across hundreds of nodes in high velocity big data environments. Key Products MemSQL is a database for fast data processing. With a single platform, converging live data with an existing data warehouse to accelerate applications and power real-time operational analytics. By combining in-memory row and flash-optimized column stores, MemSQL enables companies to capture, store, and query hundreds of terabytes of data in real-time. Key Employees Eric Frenkiel - CEO and Co-founder Nikita Shamgunov - CTO and Co-founder Melissa Gordon - VP Global Sales Adam Prout - VP Engineering Michele Chambers- VP Marketing Key Investors Accel Partners Khosla Ventures Data Collective IA Ventures First Round Capital Page 77 July 17, 2014

78 MongoDB Company Description MongoDB is a leading NoSQL database. Fortune 500 companies and startups alike are using MongoDB to create new types of applications, improve customer experience, accelerate time to market and reduce costs. MongoDB has a global community with over 4 million downloads, 100,000 online education registrations, 20,000 user group members and 20,000 MongoDB Days attendees. The company has more than 600 customers, including many of the world s largest organizations. Key Products MongoDB is a document database that provides high performance, high availability, and easy scalability. MongoDB Enterprise includes proactive support, a management platform, advanced security, the customer success program, and a commercial license. Key Employees Dwight Merriman- Chairman and Co-founder Max Schireson - CEO Eliot Horowitz - CTO and Co-Founder Sydney Carey - CFO Key Investors Altimeter Capital Fidelity Investments Flybridge Capital Partners In-Q-Tel Intel Capital NEA Red Hat Salesforce.com Sequoia Capital Union Square Ventures T. Rowe Price Page 78 July 17, 2014

79 Mu Sigma Company Description Mu Sigma is one of the world s largest decision sciences and analytics firms, helps companies institutionalize data-driven decision making and harness big data. It provides clients with a holistic ecosystem of proprietary technology platforms, processes and people, which scales the use of its unique interdisciplinary approach to decision sciences. Key Products Mu Sigma has proprietary platforms for organizations to institutionalize data-driven decision making. Its Decision Support stack has components of Data Sciences, Decision Sciences and Data Engineering. Key Employees Dhiraj Rajaram - Founder, CEO and Chairman Ambiga Dhiraj - COO Key Investors Sequoia Capital General Atlantic Page 79 July 17, 2014

80 MuleSoft Company Description MuleSoft provides a widely used integration platform for connecting SaaS and enterprise applications in the cloud and onpremise. MuleSoft s mission is to connect the world s applications, data and devices. Thousands of organizations in 60 countries, from emerging brands to Global 500 enterprises, use MuleSoft to innovate faster and gain competitive advantage. Key Products MuleSoft Anypoint Platform is a complete integration platform for SaaS, SOA and APIs. The platform includes Mule ESB; CloudHub integration platform as a service (ipaas); an end-toend API platform that includes APIkit, Anypoint API Manager and APIhub, for driving the API ecosystem; as well as Anypoint Studio, a single graphical development environment for developing integration applications either on-premise or in the cloud. Key Employees Greg Schott - President and CEO Ross Mason - Founder and VP Product Strategy Uri Sarid - CTO Matt Langdon - CFO Key Investors New Enterprise Associates Bay Partners Hummer Winblad Venture Partners SAP Ventures Salesforce Cisco Meritech Capital Partners Lightspeed Venture Partners Morgenthaler Ventures Page 80 July 17, 2014

81 Opera Solutions Company Description Opera Solutions combines advanced science, technology, and domain knowledge to extract predictive intelligence from big data and turn it into insights and recommended actions that help people make smarter decisions, work more productively, serve their customers better, grow revenues, and reduce expenses. Its hosted solutions, delivered as a service, are today delivering results in some of the world s most respected organizations in financial services, healthcare, hospitality, telecommunications, and government. Key Products Signal Hubs are domain-specific collections of Signals along with the technology required to continually extract, store, refresh, and present selected Signals and recommended Best Actions. Signal Hubs are delivered as a service in the Cloud or on premise. Signal Products deliver market-tested Signals, along with integrated data and machine learning science, in solutions designed for rapid deployment and easy integration with existing systems and work processes. Each Signal Product is delivered as a Cloud-based subscription service. Scientific & Professional Services group provides services in all areas of solution delivery. Key Employees Arnab Gupta Founder and Executive Chairman Bernhard Nann CEO Sridhar Ramasubbu CFO Yuansong Liao Co-COO Laks Srinivasan - Co-COO Farhan Baaqri CTO Bhavi Mehta - EVP, Corporate Development Pieter Schouten - EVP, Healthcare Group Esmond Jeng GM, Changhai Shishir Kapoor GM, New Delhi Key Investors Wipro Technologies Enlight Silver Lake Partners Accel-KKR Invus Financial Advisors JGE Capital Management Tola Capital Page 81 July 17, 2014

82 Palantir Company Description Palantir makes products for human driven analysis of real world data. It s focused on creating the world s best user experience for working with data, one that empowers people to ask and answer complex questions without requiring them to master querying languages, statistical modeling, or the command line. To achieve this, we build platforms for integrating, managing, and securing data on top of which we layer applications for fully interactive human-driven, machine-assisted analysis. Key Products Palantir Gotham provides a suite of integrated tools optimized for semantic, temporal, geospatial, and full-text analysis. Users can drag and drop data objects from one application to the next for a frictionless, multi-faceted analytic experience. The platform serves as an enterprise knowledgebase, containing the full record of an organization's collective analysis. Palantir Metropolis platform comprises a suite of capabilities for integrating tabular data from many different sources into coherent models, performing complex computations over models, and sharing and iterating on analytic products. Key Employees Alex Karp Co-founder and CEO Peter Thiel Co-founder and Chairman Nathan Gettings CTO Stephen Cohen - EVP Colin Anderson - CFO Key Investors In-Q-Tel Founders Fund Page 82 July 17, 2014

83 Pentaho Company Description By tightly coupling data integration with business analytics, Pentaho brings together IT and business users to access, integrate, blend, visualize and analyze all data that impacts business results. Pentaho's open source heritage drives its innovation in a modern, integrated, embeddable platform built for the future of analytics, including diverse and big data requirements. Powerful business analytics are made easy with Pentaho's cost-effective suite for data access, visualization, integration, analysis, and mining. Key Products Pentaho s platform simplifies preparing and blending any data and includes a spectrum of tools to easily analyze, visualize, explore, report and predict. Open, embeddable and extensible, Pentaho is architected to ensure that everyone from developers to business users can easily translate data into value. Key components include Pentaho Business Analytics, Pentaho Big Data Analytics, and Pentaho Data Integration. Key Employees Quentin Gallivan Chairman and CEO Richard Daley Founder and Chief Strategy Officer Doug Johnson EVP and COO Rosanne Saccone CMO Rod Squires EVP Worldwide Sales Christopher Dziekan EVP and Chief Product Officer Bob Kemper EVP Engineering Eddie White- EVP Business Development Key Investors DAG Ventures New Enterprise Associates Index Ventures Benchmark Page 83 July 17, 2014

84 Pivotal Company Description Pivotal is at the intersection of Big Data, platform-as-service (PaaS), and agile development. Pivotal offers a complete portfolio of products that converge apps, data, and analytics along with Pivotal s comprehensive PaaS platform, powered by Cloud Foundry. Key Products Pivotal One is a comprehensive solution that includes a set of application and data services that run on top of Pivotal Cloud Poundry, a turnkey PaaS solution for agile development teams to rapidly update and scale applications on a private cloud. Key Employees Paul Maritz CEO Bill Cook President and COO Scott Yara - President and Head of Products Key Investors EMC VMware General Electric Page 84 July 17, 2014

85 Platfora Company Description Platfora is a big data analytics platform for Hadoop. An interactive and visual full-stack platform delivered as subscription software in the cloud or on-premises, Platfora Big Data Analytics is creating data-driven competitive advantages in the areas of security, marketing, finance, operations, and the Internet of Things. In June the company announced growth in excess of 200% in annual recurring revenue over the past 12 months. Key Products Platfora Big Data Analytics is a self-service interactive platform that enables business users to directly access and manipulate all types of enterprise data without the intervention of IT. As a native solution for big data in Hadoop, Platfora s distributed inmemory architecture scales horizontally across unlimited nodes to enable big data analytics with high-performance at any scale. Key Employees Ben Werther Founder and CEO Mike Asher CFO Kevin Beyer - CTO Key Investors Andreessen Horowitz Tenaya Capital In-Q-Tel Sutter Hill Ventures Battery Ventures Page 85 July 17, 2014

86 Recommind Company Description Recommind, an enterprise search and categorization platform, organizes, manages, and distributes information from multiple sources. Its patented CORE (Context Optimized Relevance Engine) technology was designed to solve some search, classification, ediscovery, and compliance challenges facing today s large enterprises. In 2014 Recommind introduced Malolo, its next generation, complete technology stack. Key Products The CORE platform automatically develops a contextual understanding of the information contained in content and document collections, allowing the system to automatically work out of the box and obviating the need for long queries, large training sets or taxonomies. It is also able to automatically extract key information such as structured data fields, concepts, and metadata from unstructured documents, as well as categorize and organize them into taxonomies so they can be used productively in analytics centric processes, or simply retrieved accurately. Expanding upon on the foundation of CORE, Malolo adds a fully extensible services layer, built on the SPRING framework, and a new web interface tier built around jquery and HTML 5. Key Employees Bob Tennant CEO Dr Jan Puzicha Co-Founder and CTO Derek Schueren - Co-Founder and GM of Information Access and Governance Jérôme Levadoux CMO Mark Moore -SVP, Products Key Investors SAP Ventures Kennet Page 86 July 17, 2014

87 SAS Institute Company Description SAS Institute is a developer of analytics software. The company is the market leader in advanced analytics software and its business intelligence software is some of the most widely used in the world. The company was founded in 1976, has more than 13,000 employees, and generated more than $3 billion of revenue in SAS is the largest privately owned software company in the world. Key Products The company offers a full suite of enterprise class analytics solutions, including SAS, SAS/STA, SAS Analytics Pro, SAS Data Management, SAS Enterprise Miner, SAS Marketing Optimization, and SAS Visual Analytics. The company s software address advanced analytics, classic BI, cloud-based analytics, data management, decision management, fraud and security intelligences, Hadoop solutions, performance management, risk management, and supply intelligence. Key Employees Jim Goodnight CEO John Sall EVP and Co-founder Don Parker CFO Page 87 July 17, 2014

88 SiSense Company Description SiSense is a vendor of business analytics software focused on big data, business intelligence, reporting, and dashboarding with an easy to use frontend visualization interface. The company was founded in 2010 and is headquartered in Tel Aviv, Israel. Key Products SiSense business intelligence software integrates multiple data sources via connectors with limited data preparation. Business users can drag and drop data sets from multiple databases and applications to create a single vision of truth. Dashboarding is highly visualized with an easy to use interface with no scripting or programming required. The product leverages commodity hardware and delivers results in real-time. Key Employees Amit Bendov CEO Elad Israeli CPO and Co-founder Eldad Farkash CTO and Co-founder Key Investors Opus Capital Genesis Partners Battery Ventures DFJ Growth Page 88 July 17, 2014

89 Sumo Logic Company Description Sumo Logic is a provider of cloud-based log management and analytics software. The company s products leverage big data at scale and provides real-time analytics for actionable insights in IT operations, application management, with a focus on security and compliance. The company was founded in 2010 and is headquartered in Redwood City, California. Partners include Akamai, New Relic, CloudPassage, and ServiceNow. Key Products Sumo Logic Machine Data Analytics provides log management and analytics and focuses on detecting both known unknowns and unknown unknowns through automated machine learning algorithms for predictive analytics. Anomaly Detection automatically detects anomalies in streams of machine data and then categorizes them into events for insights into IT infrastructure and operations. LogReduce rapidly searches large volumes of machine data and presents them in simplified, meaningful patterns. Key Employees Vance Loiselle - President and CEO Kumar Saurabh - Co-founder and VP of Engineering Christian Beedgen - Co-founder and CTO Bruno Kurtic - Founding VP of Product and Strategy Sanjay Sarathy - CMO Key Investors Accel Partners Sutter Hill Ventures Greylock Partners Sequoia Capital Page 89 July 17, 2014

90 Talend Company Description Talend is a vendor of open source data integration, data management, enterprise application integration, and other big data software and services. The company was founded in 2006 and has over 400 employees, with dual headquarters in Los Altos, California, and Suresnes, France. Key Products Talend offers a broad portfolio of data management, application integration, and cloud-based integration platforms and applications. Talend s unified platform covers a wide range of solutions, including big data integration, traditional data integration, data quality, MDM, enterprise service bus, and business performance management. Open Studio for Data Integration, the company s first product, is an open source application for data integration. Key Employees Mike Tuchen - CEO Fabrice Bonan CPO and Co-founder Thomas Tuchscherer CFO and VP of Corporate Development Cédric Carbone - CTO Key Investors Silver Lake Balderton Capital Bpifrance Iris Capital Page 90 July 17, 2014

91 ThoughtSpot Company Description ThoughtSpot is a vendor of next-generation business intelligence (BI) software. The company seeks to simplify BI with a plug and play solution with a search-like interface eliminating requirements for technical query language to conduct analytics. The company was founded in 2012 and includes leadership from emerging storage company Nutanix as well as leading internet companies. Key Products ThoughtSpot s Data Search Appliance combines commodity hardware, in-memory columnar database, and a search engine interface to make BI fast, intuitive, and simplified. Key Employees Ajeet Sing CEO and Co-founder Amit Prakash CTO and Co-founder Ravi Mhatre Co-founder Key Investors Lightspeed Venture Partners Khosla Ventures Page 91 July 17, 2014

92 Tidemark Company Description Tidemark is a provider of cloud-based enterprise performance management solutions built for a mobile device enabled platform. Founded in 2010, the company s solutions offer a modern financial and operational business performance planning and analytics capabilities that aid in business decision making. Key Products Tidemark offers a suite of user-friendly real-time collaborative enterprise analytics applications that help monitor, manage, and analyze all aspects of business performance. Apps including financial planning, financial planning for higher education, financial consolidation, operational planning, metrics management, labor planning and expense, and profitability. Key Employees Christian Gheorghe CEO and Founder Phil Wilmington President and COO Tony Rizzo Chief Product Officer and Co-founder Caroline Japic CMO Bud McGann Chief Revenue Officer Key Investors Redpoint Ventures Greylock Partners Tenaya Capital Andreessen Horowitz Page 92 July 17, 2014

93 Trifacta Company Description Trifacta is a provider of a data transformation platform, which enables business analysts, data scientists, and IT professionals to transform raw, complex data into clean, structured formats for easier analysis. The company s technology is focused on humancomputer interaction, scalable data management, and machine learning. Key Products The company s Data Transformation Platform has three components Discovery, which provides a quick overview of data content and quality; Structure, which provides capabilities to manipulate messy data into standard structures; and Content, which provides visualization to standardize, expand, and distill data. Trifacta s Predictive Interaction technology transforms low-level programming tasks into high-level visual interaction that enables predictive insights from data. Key Employees Joe Hellerstein CEO and Co-founder Sean Jandel CTO and Co-founder Jeffrey Heer Chief Experience Officer and Co-founder Key Investors Accel Partners Greylock Partners Ignition Partners Page 93 July 17, 2014

94 Visier Company Description Visier is a provider of cloud-based workforce analytics and planning solutions. With Visier, organizations can leverage intuitive analytics and planning solutions to better answer key workforce questions, analyze employee turnover, executive flight risk, predict future events, and optimize operational efficiencies. Key Products Visier Workforce Analytics can be implemented in four to eight weeks, unifies all workforce data, provides key workforce metrics, and conducts predictive analytics such as which highvalue employees are at risk of leaving. Visier Workforce Planning is a cloud-based solution that is designed to reduce the administrative burden and complexity of workforce planning by automating the workforce management process. Key Employees John Schwarz CEO and Co-founder Ryan Wong CTO and Co-founder Dave Weisbeck Chief Strategy Officer Steve Bamberger Chief Revenue Officer Key Investors Summit Partners Adams Street Partners Foundation Capital Page 94 July 17, 2014

95 Other Private Companies to Watch Exhibit 32. Select Private Companies Company Business Description Aerospike Aerospike delivers a flash optimized, in memory and NoSQL database for real time big data applications. Alpine Data Labs Allegro Alteryx Automated Insights Ataccama Attensity Basho Technologies BeyondCore Chartio Centage Cirro Continuum Analytics Continuuity Context Relevant Critical Watch Chartbeat ExtraHop Exasol Flurry FoundationDB Hadapt HPCC Systems Infor Alpine's unique visual end to end approach to the analytic workflow enable predictive analytics across the organization. Its unique in cluster analytics technology leverages existing enterprise data platforms, including MPP databases and Hadoop. Allegro is a multi market commodity value chain solution provider, offering real time intelligence and decision making capabilities that improve the management of physical and financial positions, optimize assets and portfolios, and manage risk. Alteryx is a leader in data blending and advanced analytics software. Analysts use the Alteryx Analytics platform to deliver deeper insights by seamlessly blending internal, third party, and cloud data, and then analyze it using spatial and predictive drag and drop tools. This is all done in a single workflow, with no programming required. Automated Insights provides real time content automation services to transform data into narratives, visualizations, and applications. Ataccama is a software company that specializes in solutions for data quality management, master data management, and data governance. Basho provides products and services based on Riak, an open source distributed database. BeyondCore delivers one click automated business analysis. Chartio helps companies make better business decisions through intuitive, real time business analytics. Centage Corporation is a leader in automated, budgeting, and planning software solutions for small to medium sized organizations. Cirro delivers the Next Generation Data Federation platform for analytic data services enabling the access of heterogeneous data platforms and types using only SQL. Continuum s Python based data analytics products and consulting services empower organizations to analyze, manage and visualize big data. Continuuity makes it easy for any Java developer to build, deploy, scale, and manage Apache Hadoop and HBase applications in the cloud or on premise. Context Relevant predictive analytic software solutions provide revenue generating insights, automatically, faster and more accurately. Critical Watch's ACI Platform is a next generation technology that combines comprehensive intelligence with active mitigation. Chartbeat provides real time analytics for publishers and media content creators. Get live real time data about your website performance. ExtraHop provides real time operational intelligence for complex, dynamic production environments. EXASOL s in memory DBMS, EXASolution focuses exclusively on delivering ultra fast analytic performance. Flurry is optimizing the mobile experience for developers, marketers and consumers through personalized ads and mobile analytics. FoundationDB is a scalable NoSQL database with high performance multi key ACID transactions that support multiple data models. Hadapt unifies SQL and Hadoop, enabling customers to analyze all of their data in a single platform. HPCC Systems offers an enterprise ready, open source supercomputing platform to solve big data problems. Infor offers enterprise software for CRM, ERP, HRM, financial management, performance management, and supply chain management. Source: BMO Capital Markets. Page 95 July 17, 2014

96 Exhibit 33. Select Private Companies Company Infobright Information Builders InsightSquared Jitterbit Kaggle Kalido Karmasphere KISSmetrics Kognitio Business Description Infobright provides an open source analytic database for apps and data marts that analyze large volumes of machine generated data. Information Builders provides business intelligence and integration solutions. InsightSquared provides Salesforce analytics solutions that enable SMBs to maximize sales and increase team productivity. Jitterbit develops open source integration software that helps businesses connect applications, data and systems. Kaggle is a platform for predictive modeling and analytics competitions and consulting. Kalido develops software solutions for master data management, data warehouse, and business intelligence. Karmasphere empowers the new customer analytics, providing deep insights on big data to optimize every customer touch point. Using personalized workspaces and self service templates, analytics are rapidly assembled, customized and shared across business teams. KISSmetrics provides web analytics solutions to assist businesses in customer acquisition and decisionmaking. Kognitio's analytical platform can be used as a data science lab or to power comprehensive digital marketing analytics; it runs on industry standard servers, as an appliance, or in Kognitio Cloud, a ready touse analytical Platform as a Service (PaaS) in a public or private cloud environment. Lattice Localytics Loggly Looker LucidWorks Metamarkets Analytics Mixpanel Narrative Science Neo technology Netuitive NuoDB Panorama Software Paxata Predixion Lattice develops cloud based, data driven applications supporting businesses' marketing activities. Localytics is a closed loop app analytics and marketing platform that helps brands acquire, engage, and retain users. Loggly provides a cloud based log management service. Looker is a software company employing business intelligence to make data accessible to organizations and data analysts. LucidWorks provides Search, Discovery and Analytics, delivering the only enterprise grade embedded search development solution built on the power of the Apache Lucene/Solr open source search project. Metamarkets Analytics provides cloud based big data analytics including real time analytics and visualizations Mixpanel is an analytics platform for the mobile and web, supporting businesses to study consumer behavior. Narrative Science is a leading provider of Narrative Analytics, a natural language communication technology that helps organizations analyze data and transform it into narrative reports. Its patented artificial intelligence platform, Quill, mines data for meaning and insight to automatically generate relevant and impactful communications that are easy to understand and are produced at an unprecedented scale. Neo4j is a robust (fully ACID) transactional property graph database. Netuitive provides predictive analytics to assure the performance, health and availability of critical applications and the virtual, physical, and cloud in which they run. NuoDB is a distributed database management system with a rich SQL implementation and true ACID transactions. Panorama provides BI solutions that enable organizations to leverage the power of social decision making and automated intelligence to gain insights more quickly, more efficiently, and with greater relevancy. Paxata is a cloud based, self service solution powered by the Adaptive Data Preparation platform. Paxata dramatically reduces the most painful and manual steps of any analytic exercise, empowering analysts to unlock their full potential and deliver the true business value of analytics to their organizations. Predixion Software develops and markets collaborative predictive analytics solutions to drive informed decision making with big data. Source: BMO Capital Markets. Page 96 July 17, 2014

97 Exhibit 34. Select Private Companies Company RainStor Red Lambda Redis Reflektion Revolution Analytics Rosslyn Analytics Salient Management Company Business Description RainStor is a software company offering a database that manages and analyzes big data for large companies at the lowest total cost. Red Lambda enables businesses and government agencies to effectively secure their data through advanced, big data analytics technologies. Redis Labs (previously Garantia Data) offers enterprise class Redis and Memcached for developers. Its fully managed cloud services Redis Cloud and Memcached Cloud deliver top performance in a highly available, infinitely scalable, predictable, and stable manner. Reflektion s core technology is a predictive analytics platform that powers all modules.through machine learning, its platform evaluates millions of attributes about customers, products and channels and then predicts shopping behavior and critical business trends. Reflektion offers solutions that apply the results of this data science directly. Revolution Analytics is a leading commercial provider of software and services based on the open source R project for statistical computing. Rosslyn Analytics provides cloud based spend data management solutions. Salient Management Company is a developer of super scalable in memory self service visual data mining, discovery and analysis systems, which are used by business and public sector clients worldwide. Skytree Sqrrl SpatialKey Splice Machine SumAll Sysomos Syncsort ThingWorx TransLattice Think Big Analytics ThreatMetrix Upsight VoltDB Zl Technologies Zettaset Skytree is disrupting the Advanced Analytics market with a machine learning platform that gives organizations the power to discover deep analytic insights, predict future trends, make recommendations and reveal untapped markets and customers. Sqrrl, is the creator of Sqrrl Enterprise, a secure, scalable, and flexible NoSQL database, and it is powered by Apache Accumulo. SpatialKey is a SaaS mapping and data visualization platform. Splice Machine provides transactional SQL on Hadoop database for real time big data applications. SumAll is online software that guides decision making by connecting all online marketing and e commerce data into one interactive chart. Sysomos provides social media analytics and monitoring tools to businesses and organizations. Syncsort is a global software company that speeds up data processing, data integration, and data protection and recovery. ThingWorx makes it easy to build and run Machine to Machine and Internet of Things applications. TransLattice is the geographically distributed database company that provides data where and when it is needed, for enterprise, cloud and hybrid environments. Think Big Analytics provides services to assemble big data applications. The company provides data science and engineering services to assemble custom applications oriented toward business outcomes. ThreatMetrix combines comprehensive data collection, big data analytics and the ThreatMetrix Global Trust Intelligence Network to provide security and fraud prevention. Mobile and social developers leverage Upsight s analytics and marketing platform to understand user behavior, decide what it means, and act to impact their key business goals. VoltDB is an in memory relational database. It combines high velocity data ingestion, massive scalability, and real time analytics and decisioning. ZL Unified Archive is a comprehensive information governance solution, combining ediscovery, compliance, records management, and storage optimization under one architecture. Zettaset provides fault tolerant and highly avilable data aggregation software. Source: BMO Capital Markets. Page 97 July 17, 2014

98 Models Exhibit 35. CVLT Model C ommvault Income Statement E ($ in millions except per share) /30/2013 9/30/ /31/2013 3/31/ /30/2014E 9/30/2014E 12/31/2014E 3/31/2015E 2015E 2016E R evenue by Segment Software $ $ $ $ $ $ $ $ $ $ $ $ N N N Services $ $ $ $ $ $ $ $ $ $ $ $ Total Revenue $ $ $ $ $ $ $ $ $ $ $ $ Year-Over-Year Growth: Software 24.6% 20.4% 19.6% 20.2% 9.6% 17.1% 8.7% 8.0% 13.6% 21.5% 13.3% 15.0% Services 19.3% 21.2% 20.5% 18.9% 17.6% 19.5% 16.5% 16.8% 16.9% 17.0% 16.8% 13.2% Total 21.9% 20.8% 20.1% 19.6% 13.4% 18.2% 12.7% 12.4% 15.2% 19.2% 15.0% 14.1% Non-GAAP Cost of Software Revenue $ 2.86 $ 0.66 $ 0.64 $ 0.68 $ 0.62 $ 2.59 $ 0.64 $ 0.69 $ 0.81 $ 0.86 $ 3.00 $ 3.45 Non-GAAP Cost of Service Revenue $ $ $ $ $ $ $ $ $ $ $ $ on-gaap Cost of Revenues $ $ $ $ $ $ $ $ $ $ $ $ on-gaap Gross Profit $ $ $ $ $ $ $ $ $ $ $ $ on-gaap Operating Expenses Sales & Marketing $ $ $ $ $ $ $ $ $ $ $ $ Research & Development $ $ $ $ $ $ $ $ $ $ $ $ General & Administrative $ $ 9.29 $ 9.27 $ $ $ Depreciation & Amortization $ 4.83 $ 1.45 $ 1.50 $ 1.54 $ $ $ $ $ $ 1.64 $ 1.68 $ $ $ $ $ $ 6.98 $ 7.98 Total Non-GAAP Operating Expenses $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Income $ $ $ $ $ $ $ $ $ $ $ $ (+) Depreciation $ 4.94 $ 1.47 $ 1.54 $ 1.58 $ 2.05 $ 6.64 $ 1.64 $ 1.68 $ 1.78 $ 1.86 $ 6.96 $ 9.52 Non-GAAP EBITD A $ $ $ $ $ $ $ $ $ $ $ $ Other income (expense), net $ 1.06 $ 0.24 $ 0.21 $ 0.22 $ 0.22 $ 0.89 $ 0.25 $ 0.25 $ 0.25 $ 0.25 $ 1.00 $ 1.00 Non-GAAP Earnings Bef.Taxes $ $ $ $ $ $ $ $ $ $ $ $ Provision for Income Taxes $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Tax Rate 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% Non-GAAP Net Income (1) $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP EPS $ 1.49 $ 0.40 $ 0.48 $ 0.54 $ 0.52 $ 1.94 $ 0.38 $ 0.42 $ 0.58 $ 0.57 $ 1.96 $ 2.26 Avg. Diluted Shares Outstanding (1) Non-GAAP excludes: amortization, and stock-based comp. Revenue Analysis: Software 50.7% 48.6% 49.9% 51.7% 50.4% 50.2% 46.9% 48.0% 51.0% 51.3% 49.4% 49.8% Services 49.3% 51.4% 50.1% 48.3% 49.6% 49.8% 53.1% 52.0% 49.0% 48.7% 50.6% 50.2% Expense Analysis: Cost of Revenues 12.9% 13.0% 12.6% 11.8% 12.4% 12.4% 13.4% 13.2% 12.5% 12.4% 12.8% 12.7% Sales & Marketing 47.2% 46.8% 44.5% 44.0% 44.1% 44.8% 50.2% 49.5% 45.3% 46.5% 47.7% 46.5% R&D 8.9% 8.8% 8.7% 8.1% 8.9% 8.6% 8.6% 8.3% 7.9% 7.9% 8.2% 8.0% General & Administrative 7.1% 6.9% 6.5% 7.4% 7.9% 7.2% 7.0% 7.0% 7.0% 7.4% 7.1% 7.1% Depreciation & Amortization 1.0% 1.1% 1.1% 1.0% 1.0% 1.0% 1.1% 1.1% 1.0% 1.0% 1.0% 1.0% Margin Analysis: Non-GAAP Gross Margin 87.1% 87.0% 87.4% 88.2% 87.6% 87.6% 86.6% 86.8% 87.5% 87.6% 87.2% 87.3% Non-GAAP Software Gross Margin 98.9% 99.0% 99.1% 99.1% 99.2% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% 99.1% Non-GAAP Service Gross Margin 75.0% 75.7% 75.7% 76.5% 75.8% 75.9% 75.5% 75.5% 75.5% 75.5% 75.5% 75.5% Non-GAAP Operating Margin 22.8% 23.3% 26.6% 27.7% 25.7% 25.9% 19.7% 20.9% 26.3% 24.8% 23.1% 24.6% EBITDA Margin 23.8% 24.4% 27.7% 28.7% 27.0% 27.0% 20.8% 22.0% 27.3% 25.8% 24.2% 25.8% Non-GAAP Tax Rate 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% 37.0% Non-GAAP Net Margin 14.5% 14.8% 16.9% 17.5% 16.3% 16.4% 12.5% 13.3% 16.6% 15.7% 14.7% 15.6% Sequential Growth Rates: Software Revenue (9.5%) 8.5% 11.9% (.3%) (10.2%) 7.7% 17.6% 6.7% Services Revenue 4.5% 2.8% 4.2% 5.1% 3.5% 3.1% 4.2% 5.2% Total Revenue (2.8%) 5.5% 8.0% 2.3% (3.4%) 5.3% 10.7% 5.9% Gross Profit (3.1%) 6.0% 9.0% 1.6% (4.5%) 5.6% 11.6% 6.0% Non-GAAP Operating Margin -1.4% 20.5% 12.4% (5.2%) -25.8% 11.5% 39.1%.0% Non-GAAP Net Income (1.4%) 20.2% 12.4% (5.1%) (25.5%) 11.4% 38.8%.0% Year-Over-Year Growth: Total Revenue 21.9% 20.8% 20.1% 19.6% 13.4% 18.2% 12.7% 12.4% 15.2% 19.2% 15.0% 14.1% Gross Profit 22.0% 21.6% 20.4% 20.7% 13.8% 18.9% 12.2% 11.7% 14.3% 19.3% 14.5% 14.2% Operating expenses 13.8% 16.2% 16.1% 12.7% 9.0% 13.3% 18.4% 22.1% 16.7% 21.0% 19.5% 11.7% Non-GAAP Operating Income 53.5% 38.5% 31.0% 42.3% 26.7% 34.3% (4.6%) (11.8%) 9.2% 15.1% 2.7% 21.2% Non-GAAP Net Income 51.1% 38.1% 30.6% 41.7% 26.3% 33.8% (4.6%) (11.6%) 9.2% 15.1% 2.8% 21.0% Non-GAAP EPS 47.3% 33.3% 25.5% 37.9% 25.6% 30.3% (5.3%) (12.3%) 7.6% 10.8% 1.0% 15.5% Source: BMO Capital Markets Page 98 July 17, 2014

99 Exhibit 36. DATA Model Tableau Income Statement FY2012 FY2013 FY2013 FY2014E FY2014E FY2015E ($ in millions except per share) FY2012 3/31/2013 6/30/2013 9/30/ /31/2013 FY2013 3/31/2014 6/30/2014E 9/30/2014E 12/31/2014E FY2014E FY2015E License Maintenance and services Total Revenue Cost of revenue: license Cost of revenue: maint & services Non-GAAP Gross Profit Non-GAAP Operating Expenses Sales & Marketing R&D General & Administrative Total Non-GAAP Operating Expenses Non-GAAP Operating Income (3.248) (2.551) (3.769) (0.123) (+) Depreciation Non-GAAP EBITDA (1.906) (0.755) Other income (0.054) (0.053) (0.119) (0.177) (0.454) (0.803) (0.207) (0.200) (0.200) (0.200) (0.807) (0.800) Non-GAAP Earnings Bef.Taxes (3.301) (2.751) (3.969) (0.072) (0.930) Provision for Income Taxes (1.493) (0.345) (0.721) Non-GAAP Tax Rate 32.9% 45.2% 65.1% 8.7% -2.5% -4.2% 106.0% 0.0% 0.0% 0.0% % 0.0% Non-GAAP Net Income (1) (1.808) (0.351) (2.751) (3.969) (0.072) (7.143) Non-GAAP EPS $0.17 ($0.05) $0.01 $0.08 $0.20 $0.31 ($0.01) ($0.04) ($0.06) ($0.00) ($0.10) $0.10 Avg. Diluted Shares Outstanding (1) Non-GAAP excludes: One time expenses and stock-based comp. Expense Analysis (non-gaap): Cost of Revenues 8.0% 8.7% 8.5% 7.3% 7.2% 7.8% 9.2% 9.7% 9.7% 8.9% 9.3% 10.2% Sales & Marketing 47.7% 57.1% 52.8% 50.3% 46.9% 50.8% 48.5% 58.5% 60.5% 60.5% 57.5% 55.7% R&D 24.2% 29.8% 25.8% 22.9% 19.9% 23.6% 25.2% 26.0% 25.0% 22.0% 24.3% 23.6% General & Administrative 12.0% 12.6% 10.9% 9.2% 8.7% 10.0% 9.0% 9.0% 9.0% 8.5% 8.8% 8.6% Depreciation 3.0% 3.4% 2.8% 3.0% 2.8% 2.9% 3.4% 3.5% 3.4% 2.9% 3.2% 3.3% Margin Analysis (non-gaap): Product gross margin 99.7% 99.3% 99.7% 99.4% 99.6% 99.5% 99.7% 99.5% 99.5% 99.5% 99.5% 99.5% Maintenance/Services gross margin 73.7% 75.7% 74.8% 77.9% 75.9% 76.1% 74.4% 74.5% 74.5% 74.5% 74.5% 75.0% Gross Margin 92.0% 91.3% 91.5% 92.7% 92.8% 92.2% 90.8% 90.3% 90.3% 91.1% 90.7% 89.8% Operating Margin 8.0% -8.1% 2.1% 10.2% 17.3% 7.8% 8.1% -3.2% -4.2% 0.1% 0.0% 1.8% EBITDA Margin 11.1% (4.8%) 4.9% 13.3% 20.1% 10.7% 11.5%.3% (.8%) 3.0% 3.2% 5.1% Tax Rate 32.9% 45.2% 65.1% 8.7% (2.5%) (4.2%) 106.0%.0%.0%.0% (667.9%).0% Net Margin 5.4% (4.5%).6% 9.1% 17.2% 7.8% (.5%) (3.4%) (4.5%) (.1%) (2.0%) 1.6% Sequential Growth Rates (non-gaap): License (12.1%) 26.8% 25.2% 38.3% (16.5%) 4.5% 11.1% 34.2% Maintenance and services 15.9% 20.4% 16.9% 22.5% 11.4% 12.6% 11.3% 16.9% Total Revenue (4.3%) 24.7% 22.4% 33.4% (8.5%) 7.3% 11.1% 27.8% Gross Profit (5.0%) 24.9% 24.0% 33.5% (10.4%) 6.8% 11.1% 28.9% Operating Margin % (131.5%) 511.2% 125.1% -56.9% (142.0%) 47.8% (103.4%) Net Income (218.9%) (117.5%) % 151.7% (102.5%) 683.7% 44.3% (98.2%) Year-Over-Year Growth (non-gaap): License 102.4% 51.4% 65.6% 89.7% 93.0% 77.9% 83.3% 51.0% 34.0% 30.0% 44.3% 26.6% Maintenance and services 110.9% 88.0% 84.4% 91.0% 99.7% 91.6% 92.1% 79.7% 71.1% 63.3% 74.4% 52.4% Total Revenue 104.8% 62.1% 71.3% 90.1% 94.9% 82.0% 86.3% 60.4% 45.6% 39.6% 53.7% 35.8% Gross Profit 97.9% 58.7% 71.3% 92.9% 96.5% 82.5% 85.3% 58.3% 41.9% 37.0% 51.1% 34.4% Sales & Marketing 104.5% 121.6% 107.8% 102.1% 68.0% 93.9% 58.2% 77.9% 75.0% 79.9% 74.0% 31.5% R&D 74.3% 89.0% 83.6% 75.5% 67.3% 77.5% 57.7% 61.8% 59.2% 54.3% 58.0% 31.7% Operating expenses 98.5% 106.4% 96.3% 85.4% 61.7% 83.1% 54.9% 67.7% 67.0% 68.2% 65.1% 31.7% Source: BMO Capital Markets Page 99 July 17, 2014

100 Exhibit 37. QLIK Model QlikTech Income Statement FY2012 FY2013 FY2013 FY2014E FY2014E FY2015E ($ in millions except per share) FY2012 Q1-Mar-13 Q2-June-13 Q3-Sept-13 Q4-Dec-13 FY2013 Q1-Mar-14 Q2-June-14E Q3-Sept-14E Q4-Dec-14E FY2014E FY2015E License revenue $ $ $ $ $ $ $ $ $ $ $ $ Maintenance revenue $ $ $ $ $ $ $ $ $ Professional services revenue $ $ $ $ $ $ $ $ $ Total Revenue $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: license $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: maintenance $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: professional services $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Gross Profit $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Expenses Sales & Marketing $ $ $ $ $ $ $ $ $ $ $ $ R&D $ $ $ $ $ $ $ $ $ $ $ $ General & Administrative $ $ $ $ $ $ $ $ $ $ $ $ Total Non-GAAP Operating Expenses $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Income $ $ (10.214) $ (1.655) $ $ $ $ (14.487) $ (3.759) $ $ $ $ (+) Depreciation $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP EBITDA $ $ (8.495) $ $ $ $ $ (11.918) $ (0.983) $ $ $ $ Other income $ (2.891) $ (1.451) $ (0.469) $ $ (0.666) $ (2.522) $ (0.328) $ (0.328) $ (0.328) $ (0.328) $ (1.312) $ (1.200) Non-GAAP Earnings Bef.Taxes $ $ (11.665) $ (2.124) $ $ $ $ (14.815) $ (4.087) $ $ $ $ Provision for Income Taxes $ $ (3.499) $ (0.638) $ $ $ $ (4.445) $ (1.226) $ $ $ $ Non-GAAP Tax Rate 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% Loss atributable to non-controlling interest $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - Non-GAAP Net Income (1) $ $ (8.166) $ (1.486) $ $ $ $ (10.370) $ (2.861) $ $ $ $ Non-GAAP EPS $ 0.27 $ (0.09) $ (0.02) $ 0.05 $ 0.31 $ 0.27 $ (0.12) $ (0.03) $ 0.04 $ 0.33 $ 0.24 $ 0.35 Avg. Diluted Shares Outstanding $ $ $ $ $ $ $ $ $ $ $ $ (1) Non-GAAP excludes: amortization, restructuring, impairments, settlements, and stock-based comp. Expense Analysis (non-gaap): Cost of Revenues 10.6% 14.2% 12.9% 13.5% 10.7% 12.5% 15.7% 13.3% 14.0% 11.0% 13.2% 13.3% Sales & Marketing 53.6% 62.4% 58.9% 53.2% 44.7% 53.5% 64.8% 61.4% 54.4% 46.0% 55.2% 53.7% R&D 7.5% 13.0% 11.9% 9.6% 7.2% 10.0% 11.6% 10.6% 9.9% 7.5% 9.6% 9.5% General & Administrative 18.8% 21.0% 17.9% 17.2% 11.8% 16.3% 20.9% 17.7% 16.9% 11.6% 16.1% 15.8% Depreciation 1.4% 1.8% 1.8% 2.1% 1.4% 1.7% 2.3% 2.2% 2.5% 1.8% 2.2% 2.3% Margin Analysis (non-gaap): Product gross margin 97.9% 96.9% 97.5% 97.3% 97.4% 97.3% 97.2% 97.5% 97.3% 97.4% 97.4% 97.4% Maintenance gross margin 93.0% 92.0% 93.4% 94.0% 94.1% 93.4% 93.3% 93.4% 94.0% 94.1% 93.7% 93.7% Pro-serv gross margin 5.3% -12.8% -8.2% -13.7% 8.5% -4.9% -13.5% -8.2% -13.7% 8.5% -5.3% -5.5% Gross Margin 89.4% 85.8% 87.1% 86.5% 89.3% 87.5% 84.3% 86.7% 86.0% 89.0% 86.8% 86.7% Operating Margin (total) 9.4% -10.6% -1.5% 6.5% 25.5% 7.7% -13.0% -3.0% 4.7% 24.0% 5.9% 7.7% EBITDA Margin 10.8% (8.8%).3% 8.7% 27.0% 9.5% (10.7%) (.8%) 7.3% 25.8% 8.1% 10.0% Tax Rate 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% Net Margin 6.1% (8.5%) (1.4%) 4.6% 17.6% 5.0% (9.3%) (2.3%) 3.1% 16.6% 4.0% 5.3% Sequential Growth Rates (non-gaap): Total Revenue (29.8%) 11.9% (3.6%) 55.4% (31.3%) 12.6% (2.3%) 53.0% Gross Profit (33.4%) 13.7% (4.3%) 60.4% (35.2%) 15.8% (3.0%) 58.2% Operating Margin % (83.8%) (510.0%) 508.9% % (74.0%) (254.4%) 671.5% Net Income (136.0%) (81.8%) (422.7%) 493.5% (136.4%) (72.4%) (234.0%) 711.7% Year-Over-Year Growth (non-gaap): Total Revenue 21.2% 22.0% 25.9% 20.9% 17.7% 21.1% 15.1% 15.8% 17.4% 15.5% 15.9% 15.5% Gross Profit 21.0% 18.2% 23.0% 18.0% 16.2% 18.5% 13.1% 15.2% 16.7% 15.1% 15.1% 15.4% Sales & Marketing 17.1% 21.4% 26.0% 17.5% 18.9% 20.9% 19.4% 20.8% 20.2% 18.8% 19.7% 12.2% R&D 52.4% 127.6% 135.7% 45.5% 2.2% 60.8% 3.4% 3.6% 20.4% 19.6% 11.1% 14.5% Operating expenses 23.1% 28.6% 29.1% 15.5% 12.2% 20.8% 16.2% 17.1% 19.2% 17.8% 17.6% 12.8% Operating Income 5.4% 343.1% (170.4%) 59.1% 27.9% (1.1%) 41.8% 127.2% (14.5%) 8.4% (10.8%) 51.0% Net Income 2.3% 215.4% (186.1%) 164.6% 25.6% (.1%) 27.0% 92.5% (20.1%) 9.4% (8.0%) 53.5% EPS (.1%) 208.5% (187.0%) 158.2% 22.5% (1.2%) 23.2% 84.7% (22.3%) 4.1% (11.4%) 47.9% Source: BMO Capital Markets Page 100 July 17, 2014

101 Exhibit 38. SAP Model SAP AG Income Statement FY2011 FY2012 FY2013 FY2013 FY2014E FY2014E FY2015E ( in millions except per share) Year Year Q1-Mar-13 Q2-Jun-13 Q3-Sep-13 Q4-Dec-13 Year Q1-Mar-14 Q2-Jun-14 Q3-Sep-14E Q4-Dec-14E Year Year REVENUE SUMMARY (non-ifrs) Cloud Subscriptions and Support ,313 Software 4,107 4, ,903 4, ,970 4,533 4,749 Support 7,221 8,245 2,113 2,182 2,189 2,274 8,758 2,214 2,280 2,339 2,441 9,274 9,956 SSRS revenue 11,346 13,247 2,937 3,347 3,363 4,387 14,034 3,058 3,479 3,583 4,686 14,805 16,019 Consulting 2,342 2, , ,048 2,120 Other service PSOS revenue 2,914 3, , ,636 2,705 Total revenue (non-ifrs) 14,259 16,305 3,635 4,092 4,058 5,116 16,901 3,701 4,152 4,223 5,366 17,441 18,724 Year over year growth Cloud Subscriptions and Support % 165% 146% 32% 121% 32% 32% 32% 31% 32% 32% Software 20% 13% 3% -7% -5% -2% -3% -5% -3% 1% 4% 0% 5% Support 12% 14% 8% 8% 4% 5% 6% 5% 4% 7% 7% 6% 7% Software and support 15% 14% 7% 3% 1% 2% 3% 2% 2% 5% 6% 4% 7% SRSS revenue 15% 17% 12% 7% 5% 3% 6% 4% 4% 7% 7% 5% 8% Consulting 7% 4% -7% -6% -10% -10% -8% -9% -10% -8% -8% -9% 3% Other service 27% 8% 6% 5% 0% -4% 1% -5% -7% -8% -3% -6% 0% PSOS revenue 10% 5% -4% -4% -8% -8% -6% -8% -10% -8% -7% -8% 3% Total revenue (non-ifrs) 14% 14% 8% 4% 2% 1% 4% 2% 1% 4% 5% 3% 7% Cost of software 1,825 2, , ,578 2,792 Cost of professional and other services 2,200 2, , ,193 2,189 Gross profit 10,235 11,782 2,550 2,984 2,943 3,913 12,391 2,569 2,981 3,058 4,062 12,670 13,743 Software gross margin 83.9% 83.4% 81.7% 83.5% 82.8% 84.2% 83.2% 80.0% 81.1% 83% 84% 81.3% 81.0% Professional services gross margin 24.5% 22.0% 17.2% 21.4% 17.8% 25.7% 20.6% 11.9% 16.9% 16% 22% 16.8% 19.1% Gross margin 71.8% 72.3% 70.2% 72.9% 72.5% 76.5% 73.3% 69.4% 71.8% 72.4% 75.7% 72.6% 73.4% Operating expenses (non-ifrs) Research and development 1,897 2, , ,149 2,299 Sales and marketing 2,953 3, , ,041 3, , ,100 4,020 4,351 General and administrative Total operating expenses 5,524 6,571 1,649 1,765 1,646 1,815 6,875 1,651 1,746 1,694 1,884 6,975 7,480 Non-IFRS operating income 4,711 5, ,220 1,297 2,098 5, ,235 1,364 2,178 5,696 6,263 Other income/expense (79) (173) (10) (2) (1) (3) (16) (11) 4 (4) (4) (15) (16) Financial income, net (45) (68) (15) (23) (7) (23) (68) (9) 17 (10) (10) (12) (40) Pre-tax income 4,587 4, ,195 1,289 2,072 5, ,256 1,350 2,164 5,669 6,207 Income tax 1,215 1, , ,536 1,738 Tax rate 26.5% 27.4% 21.4% 26.8% 27.5% 26.4% 26.0% 25.9% 25.4% 28.0% 28.0% 27.1% 28.0% Non-IFRS net income 3,372 3, ,525 4, ,558 4,135 4,469 Non-IFRS EPS IFRS EPS Diluted Shares 1,190 1,192 1,193 1,193 1,193 1,195 1,194 1,196 1,197 1,196 1,196 1,196 1,196 Expense analysis: Cost of revenues 28% 28% 30% 27% 27% 24% 27% 31% 28% 28% 24% 27% 27% Research and development 13% 13% 15% 13% 13% 11% 13% 14.2% 12.8% 12.5% 10.5% 12.3% 12.3% Sales and marketing 20.7% 22.6% 25.4% 24.8% 23.2% 20.3% 23.2% 25.2% 24.2% 23.2% 20.5% 23.0% 23.2% General and administrative 5% 5% 5% 5% 5% 4% 5% 5.1% 5.0% 4.5% 4.1% 4.6% 4.4% Operating expenses 39% 40% 45% 43% 41% 35% 41% 45% 42% 40% 35% 40.0% 39.9% Margin analysis: Non-IFRS gross margin 72% 72% 70% 73% 73% 76% 73% 69% 72% 72% 76% 73% 73% Non-IFRS operating margin 33.0% 32.0% 24.8% 29.8% 32.0% 41.0% 32.6% 24.8% 29.7% 32.3% 40.6% 32.7% 33.4% Non-IFRS operating margin in C/C 32% 32% 25% 31% 33% 42% 33% 25% 30% 32% 41% 33% 33% EBITDA margin 38% 37% 31% 36% 38% 46% 38% 31% 36% 38% 45% 38% 39% Tax rate 26% 27% 21% 27% 28% 26% 26% 26% 25% 28% 28% 27% 28% Non-IFRS net income 24% 22% 19% 21% 23% 30% 24% 18% 23% 23% 29% 24% 24% Q/Q growth rates: Total revenue -28% 13% -1% 26% -28% 12% 2% 27% Gross profit -33% 17% -1% 33% -34% 16% 3% 33% Non-IFRS operating margin -54% 35% 6% 62% -56% 34% 10% 60% Non-IFRS net income -49% 27% 7% 63% -56% 41% 4% 60% Y/Y growth rates: Total revenue 14% 14% 8% 4% 2% 1% 4% 2% 1% 4% 5% 3% 7% Gross profit 15% 15% 11% 6% 3% 3% 5% 1% 0% 4% 4% 2% 8% Operating expenses 14% 19% 13% 7% 1% -2% 5% 0% -1% 3% 4% 1% 7% Non-IFRS operating margin 17% 11% 8% 4% 5% 7% 6% 2% 1% 5% 4% 3% 10% Non-IFRS net income 22% 7% 18% 5% 12% 12% 11% -3% 7% 4% 2% 3% 8% Non-IFRS EPS 22% 7% 18% 5% 12% 12% 11% -3% 7% 4% 2% 3% 8% Source: BMO Capital Markets Page 101 July 17, 2014

102 Exhibit 39. SPLK Model Splunk Income Statement FY2013 FY2014 FY2014 FY2015E FY2015E FY2016E ($ in millions except per share) FY2013 Q1-Apr-13 Q2-Jul-13 Q3-Oct-13 Q4-Jan-14 FY2014 Q1-Apr-14 Q2-Jul-14E Q3-Oct-14E Q4-Jan-15E FY2015E FY2016E Products and license revenues $ $ $ $ $ $ $ $ $ $ $ $ Maintenance/services revenues $ $ $ Total Revenue $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: product $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: services $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Gross Profit $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Expenses R&D $ $ $ $ $ $ $ $ $ $ $ $ Sales & Marketing $ $ $ $ $ $ $ $ $ $ $ $ General & Administrative $ $ $ $ $ $ $ $ $ $ $ $ Total Non-GAAP Operating Expenses $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Income $ (1.398) $ (5.282) $ (0.761) $ $ $ (1.212) $ (3.573) $ (2.919) $ $ $ $ (+) Depreciation $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP EBITDA $ $ (3.857) $ $ $ $ $ (0.922) $ (0.692) $ $ $ $ Other income $ $ (0.033) $ (0.024) $ (0.975) $ (0.837) $ (1.869) $ (0.090) $ - $ - $ - $ (0.090) $ - Non-GAAP Earnings Bef.Taxes $ (1.246) $ (5.315) $ (0.785) $ (0.102) $ $ (3.081) $ (3.663) $ (2.919) $ $ $ $ Provision for Income Taxes $ $ $ $ (0.500) $ (0.263) $ $ $ - $ - $ - $ $ - Non-GAAP Tax Rate -57.2% -7.6% -46.5% 490.2% -8.4% -0.2% -15.3% 0.0% 0.0% 0.0% 337.6% 0.0% Non-GAAP Net Income (1) $ (1.959) $ (5.719) $ (1.150) $ $ $ (3.087) $ (4.225) $ (2.919) $ $ $ (0.396) $ Non-GAAP EPS $ (0.02) $ (0.06) $ (0.01) $ 0.00 $ 0.03 $ (0.03) $ (0.04) $ (0.02) $ 0.01 $ 0.04 $ (0.00) $ 0.08 Avg. Diluted Shares Outstanding (1) Non-GAAP excludes: amortization, employeer payroll tax, and stock-based comp. Expense Analysis (non-gaap): Cost of Revenues 89.8% 89.6% 90.2% 91.0% 92.0% 90.9% 88.9% 89.3% 90.4% 91.5% 90.2% 90.0% R&D 17.8% 19.7% 18.9% 17.9% 15.5% 17.7% 18.9% 18.4% 17.4% 15.0% 17.2% 16.7% Sales & Marketing 58.6% 64.2% 58.6% 59.5% 60.9% 60.6% 59.3% 60.6% 60.2% 61.6% 60.5% 60.0% General & Administrative 14.1% 14.9% 13.9% 12.5% 11.6% 13.0% 14.8% 13.5% 11.5% 10.6% 12.4% 11.4% Depreciation 2.3% 2.5% 2.2% 2.1% 2.2% 2.2% 3.1% 2.4% 2.3% 2.4% 2.5% 2.5% Margin Analysis (non-gaap): Product gross margin 99.5% 99.8% 99.8% 99.8% 99.9% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% Maintenance/Services gross margin 69.1% 72.0% 72.7% 74.7% 74.6% 73.7% 72.6% 72.7% 74.7% 74.6% 73.7% 73.7% Gross Margin 89.8% 89.6% 90.2% 91.0% 92.0% 90.9% 88.9% 89.3% 90.4% 91.5% 90.2% 90.0% Operating Margin (total) -0.7% -9.2% -1.1% 1.1% 4.0% -0.4% -4.2% -3.1% 1.3% 4.2% 0.1% 1.9% EBITDA Margin 1.6% (6.7%) 1.0% 3.2% 6.2% 1.8% (1.1%) (.7%) 3.6% 6.6% 2.6% 4.4% Tax Rate (57.2%) (7.6%) (46.5%) 490.2% (8.4%) (.2%) (15.3%).0%.0%.0% 337.6%.0% Net Margin (1.0%) (10.0%) (1.7%).5% 3.4% (1.0%) (4.9%) (3.1%) 1.3% 4.2% (.1%) 1.9% Sequential Growth Rates (non-gaap): Total Revenue (12.3%) 16.9% 17.6% 27.1% (14.0%) 9.1% 11.5% 21.4% Gross Profit (13.3%) 17.7% 18.6% 28.5% (16.9%) 9.7% 12.9% 22.8% Operating Margin % (85.6%) (214.7%) 353.4% % (18.3%) (147.3%) 288.3% Net Income (291.4%) (79.9%) (134.6%) 750.3% (224.9%) (30.9%) (147.3%) 288.3% Year-Over-Year Growth (non-gaap): Total Revenue 64.5% 53.8% 50.3% 51.1% 53.2% 52.1% 50.2% 40.2% 32.9% 27.0% 35.8% 32.9% Gross Profit 63.3% 55.2% 50.5% 53.9% 55.5% 53.9% 49.0% 38.8% 32.1% 26.3% 34.8% 32.6% Sales & Marketing 58.9% 57.5% 49.3% 52.2% 67.8% 57.5% 38.7% 45.0% 34.6% 28.5% 35.6% 31.8% Operating expenses 57.4% 54.7% 49.8% 50.6% 57.3% 53.3% 41.4% 41.8% 31.8% 25.9% 34.1% 29.9% Operating Income (71.6%) 50.2% 12.9% (301.6%) 22.7% (13.3%) (32.4%) 283.6% 58.3% 35.6% (121.2%) % Net Income (62.3%) 54.1% 62.2% (175.5%) 13.3% 57.6% (26.1%) 153.8% 247.2% 58.6% (87.2%) (2683.7%) EPS (90.3%) 42.9% 48.8% (161.8%) 10.3% 14.2% (35.7%) 122.4% 241.0% 54.6% (88.1%) (2560.2%) Source: BMO Capital Markets Page 102 July 17, 2014

103 Exhibit 40. ORCL Model Oracle Income Statement FY2013 FY2014 FY2014 FY2015E FY2015E FY2016E ($ in millions except per share) FY2013 Q1-Aug-13 Q2-Nov-13 Q3-Feb-14 Q4-May-14 FY2014 Q1-Aug-14E Q2-Nov-14E Q3-Feb-15E Q4-May-15E FY2015E FY2016E Revenue by Segment (non-gaap) New software licenses $ 10,101 $ 1,399 $ 2,121 $ 2,128 $ 3,769 $ 9,417 $ 1,437 $ 2,185 $ 2,188 $ 3,999 $ 9,808 $ 9,924 Cloud SaaS and Paas $ 257 $ 263 $ 292 $ 327 $ 1,139 $ 334 $ 342 $ 365 $ 422 $ 1,463 $ 1,815 Cloud IaaS $ $ $ 121 $ 128 $ 455 $ 114 $ 102 $ Software license updates and product support $ 17,157 $ 4,432 $ 4,519 $ 4,565 $ 4,695 $ 18,211 $ 4,724 $ 4,813 4,862 Total software and cloud $ 6,197 $ 7,000 $ 7,106 $ 8,919 $ 29,222 $ 6,609 7,441 $ 5,024 $ $ 7,541 9,579 $ $ 478 $ 502 $ $ 19,422 $ 20,587 $ $ 31,170 $ 32,828 Hardware systems products $ 3,033 $ 669 $ 714 $ 725 $ 870 $ 2,978 $ 669 $ 725 $ 741 $ 903 $ 3,038 $ 3,098 Hardware systems support $ 2,326 $ 597 $ 610 $ 600 $ 597 $ 2,404 $ 609 $ 628 $ 618 $ 621 $ 2,476 $ 2,550 Total hardware systems $ 1,266 $ 1,324 $ 1,325 $ 1,467 $ 5,382 $ 1,278 $ 1,353 $ 1,359 $ 1,524 $ 5,514 $ 5,649 Services $ 4,258 $ 918 $ 959 $ 884 $ 940 $ 3,701 $ 923 $ 969 $ 893 $ 949 $ 3,733 $ 3,771 Total Revenue (non-gaap) $ 37,253 $ 8,381 $ 9,283 $ 9,315 $ 11,326 $ 38,305 $ 8,809 $ 9,763 $ 9,793 $ 12,052 $ 40,417 $ 42,247 Year-Over-Year Growth: New software licenses 1.8% (12.2%) (11.7%) (9.0%).0% (6.8%) 2.7% 3.0% 2.8% 6.1% 4.2% 1.2% Cloud SaaS and Paas 23.4% 30.0% 30.0% 25.0% 29.0% 28.4% 24.1% Cloud IaaS 13.3% 5.0% 5.0% 5.0% 5.0% 5.0% 5.0% Software license updates and product support 5.5% 6.9% 6.0% 5.1% 6.6% 6.1% 6.6% 6.5% 6.5% 7.0% 6.6% 6.0% Hardware systems products (20.8%) (14.1%) (2.7%) 8.0% 2.5% (1.8%).0% 1.5% 2.2% 3.8% 2.0% 2.0% Hardware systems support (7.1%) 3.3% 3.4% 4.9% 1.9% 3.4% 2.0% 3.0% 3.0% 4.0% 3.0% 3.0% Services (9.5%) (17.6%) (14.7%) (15.4%) (3.6%) (13.1%).5% 1.0% 1.0% 1.0%.9% 1.0% Total 14.6% 2.1% 1.9% 3.8% 3.3% 2.8% 5.1% 5.2% 5.1% 6.4% 5.5% 4.5% % license updates/support 46.1% 52.9% 48.7% 49.0% 41.5% 47.5% 53.6% 49.3% 49.6% 41.7% 48.1% 48.7% Cost of SaaS/PaaS $ 134 $ 137 $ 137 $ 146 $ 160 $ 580 $ 693 Cost of Iaas $ 83 $ 76 $ 65 $ 81 $ 89 $ 311 $ 316 Cost of software license updates and product sup $ 1,155 $ 282 $ 280 $ 280 $ 297 $ 1,139 $ 236 $ 241 $ 243 $ 251 $ 971 $ 1,003 Hardware systems products $ 1,498 $ 328 $ 368 $ 378 $ 442 $ 1,516 $ 324 $ 351 $ 359 $ 438 $ 1,473 $ 1,487 Hardware systems support $ 886 $ 206 $ 213 $ 206 $ 204 $ 829 $ 210 $ 217 $ 213 $ 214 $ 854 $ 867 Cost of Services $ 3,515 $ 799 $ 844 $ 797 $ 747 3,187 $ $ 710 $ 746 $ $ $ 2,875 $ 2,979 Non-GAAP Gross Profit $ 30,199 $ 6,766 $ 7,578 $ 7,654 $ 9,419 $ 31,417 $ 7,116 $ 8,006 $ 8,063 $ 10,169 $ 33,353 $ 34,902 Non-GAAP Operating Expenses Sales & Marketing $ 7,181 $ 1,667 $ 1,926 $ 1,886 $ 2,195 $ 7,674 $ 1,757 $ 2,034 $ 1,990 $ 2,335 $ 8,116 $ 8,482 R&D $ 4,499 $ 1,140 $ 1,186 $ 1,193 $ 1,248 $ 4,767 $ 1,195 $ 1,195 $ 1,200 $ 1,225 $ 4,815 $ 4,940 General & Administrative $ 907 $ 218 $ 220 $ 209 $ 220 $ 867 $ 230 $ 230 $ 230 $ 245 $ 935 $ 995 Total Non-GAAP Operating Expenses $ 12,587 $ 3,025 $ 3,332 $ 3,288 $ 3,663 $ 13,308 $ 3,182 $ 3,459 $ 3,420 $ 3,805 $ 13,866 $ 14,417 Non-GAAP Operating Income $ 17,612 $ 3,741 $ 4,246 $ 4,366 $ 5,756 $ 18,109 $ 3,934 $ 4,547 $ 4,643 $ 6,364 $ 19,487 $ 20,486 (+) Depreciation $ 492 $ 150 $ 152 $ 150 $ 156 $ 608 $ 165 $ 165 $ 165 $ 165 $ 660 $ 680 Non-GAAP EBITDA $ 18,104 $ 3,891 $ 4,398 $ 4,516 $ 5,912 $ 18,717 $ 4,099 $ 4,712 $ 4,808 $ 6,529 $ 20,147 $ 21,166 Interest expense $ (798) $ (217) $ (230) $ (228) $ (239) $ (914) $ (220) $ (220) $ (220) $ (220) $ (880) $ (880) Non-operating income, net $ 11 $ 7 $ 23 $ (90) $ (81) $ (141) $ - $ - $ - $ - $ - $ - Non-GAAP Earnings Bef.Taxes $ 16,825 $ 3,531 $ 4,039 $ 4,048 $ 5,436 $ 17,054 $ 3,714 $ 4,327 $ 4,423 $ 6,144 $ 18,607 $ 19,606 Provision for Income Taxes $ 3,867 $ 770 $ 884 $ 935 $ 1,251 $ 3,840 $ 873 $ 1,017 $ 1,039 $ 1,444 $ 4,373 $ 4,607 Non-GAAP Tax Rate 23.0% 21.8% 21.9% 23.1% 23.0% 22.5% 23.5% 23.5% 23.5% 23.5% 23.5% 23.5% Loss from JV $ - $ - $ - $ - Non-GAAP Net Income (1) $ 12,958 $ 2,761 $ 3,155 $ 3,113 $ 4,185 $ 13,214 $ 2,841 $ 3,310 $ 3,383 $ 4,700 $ 14,235 $ 14,998 Non-GAAP EPS (1) $ 2.68 $ 0.59 $ 0.69 $ 0.68 $ 0.92 $ 2.87 $ 0.63 $ 0.74 $ 0.77 $ 1.08 $ 3.20 $ 3.41 Non-GAAP EPS (1) (constant currency) $ 2.73 $ 0.53 $ 0.70 $ 0.69 $ 0.91 Avg. Diluted Shares Outstanding 4,844 4,674 4,600 4,575 4,569 4,605 4,518 4,467 4,417 4,366 4,442 4,404 (1) Non-GAAP excludes: amortization, restructuring, impairments, settlements, and stock-based comp. Expense Analysis: Cost of Revenues 9.5% 9.7% 9.3% 9.3% 8.3% 9.1% 8.7% 8.3% 8.3% 7.5% 8.2% 7.9% Sales & Marketing 19.3% 19.9% 20.7% 20.2% 19.4% 20.0% 19.9% 20.8% 20.3% 19.4% 20.1% 20.1% R&D 12.1% 13.6% 12.8% 12.8% 11.0% 12.4% 13.6% 12.2% 12.3% 10.2% 11.9% 11.7% General & Administrative 2.4% 2.6% 2.4% 2.2% 1.9% 2.3% 2.6% 2.4% 2.3% 2.0% 2.3% 2.4% Margin Analysis: Non-GAAP Gross Margin 81.1% 80.7% 81.6% 82.2% 83.2% 82.0% 80.8% 82.0% 82.3% 84.4% 82.5% 82.6% Non-GAAP Operating Margin (total) 47.3% 44.6% 45.7% 46.9% 50.8% 47.3% 44.7% 46.6% 47.4% 52.8% 48.2% 48.5% EBITDA Margin 48.6% 46.4% 47.4% 48.5% 52.2% 48.9% 46.5% 48.3% 49.1% 54.2% 49.8% 50.1% Non-GAAP Tax Rate 23.0% 21.8% 21.9% 23.1% 23.0% 22.5% 23.5% 23.5% 23.5% 23.5% 23.5% 23.5% Non-GAAP Net Margin 34.8% 32.9% 34.0% 33.4% 37.0% 34.5% 32.2% 33.9% 34.5% 39.0% 35.2% 35.5% Sequential Growth Rates: Total Revenue (23.5%) 10.8%.3% 21.6% (22.2%) 10.8%.3% 23.1% Gross Profit (26.0%) 12.0% 1.0% 23.1% (24.5%) 12.5%.7% 26.1% Non-GAAP Operating Margin -32.9% 13.5% 2.8% 31.8% -31.7% 15.6% 2.1% 37.1% Non-GAAP Net Income (32.9%) 14.3% (1.3%) 34.4% (32.1%) 16.5% 2.2% 38.9% Year-Over-Year Growth: Total Revenue 14.6% 2.1% 1.9% 3.8% 3.3% 2.8% 5.1% 5.2% 5.1% 6.4% 5.5% 4.5% Gross Profit 2.7% 4.9% 3.3% 5.3% 3.0% 4.0% 5.2% 5.6% 5.3% 8.0% 6.2% 4.6% Operating expenses 3.2% 5.8% 8.8% 6.4% 2.5% 5.7% 5.2% 3.8% 4.0% 3.9% 4.2% 4.0% Non-GAAP Operating Income 2.3% 4.2% (.6%) 4.5% 3.3% 2.8% 5.1% 7.1% 6.3% 10.6% 7.6% 5.1% Non-GAAP Operating Income (constant currency 4.0% 1.0% 6.0% 2.0% Non-GAAP Net Income 3.5% 5.6% 1.1%.2% 1.7% 2.0% 2.9% 4.9% 8.7% 12.3% 7.7% 5.4% Non-GAAP Net Income (constant currency) 6.0% 3.0% 2.0% 1.0% Non-GAAP EPS 8.9% 11.6% 6.9% 5.3% 5.9% 7.3% 6.4% 8.0% 12.6% 17.5% 11.7% 6.3% Non-GAAP EPS (constant currency) 11.0% 9.0% 7.0% 5.0% Source: BMO Capital Markets Page 103 July 17, 2014

104 Glossary Exhibit 41. Glossary Term Business intelligence (BI) Cloud computing Data mart Data warehouse Distributed file system Document store Extract, transform, and load (ETL) Graph database Hadoop Hbase Key value store Machine learning MapReduce Mashup Definition A type of application software designed to report, analyze, and present data. BI tools are often used to read data that have been previously stored in a data warehouse or data mart. BI tools can also be used to create standard reports that are generated on a periodic basis, or to display information on real time management dashboards, i.e., integrated displays of metrics that measure the performance of a system. A computing paradigm in which highly scalable computing resources, often configured as a distributed system, are provided as a service through a network. Subset of a data warehouse, used to provide data to users usually through business intelligence tools. Specialized database optimized for reporting, often used for storing large amounts of structured data. Data is uploaded using ETL (extract, transform, and load) tools from operational data stores, and reports are often generated using business intelligence tools. A file system that allows access to files from multiple hosts sharing a computer network. Hadoop and other Big Data technologies use this approach to implement parallel processing and improve availability and performance. Distributed file systems often imply replication of data and fault tolerance. A type of NoSQL database that stores entire documents. Software tools used to extract data from outside sources, transform them to fit operational needs, and load them into a database or data warehouse. A type of NoSQL database that uses graph structures with nodes, edges, and properties to represent and store data. An open source (free) software framework for processing huge datasets on certain kinds of problems on a distributed system. Its development was inspired by Google s MapReduce and Google File System. It was originally developed at Yahoo! and is now managed as a project of the Apache Software Foundation. An open source (free), distributed, non relational database modeled on Google s Big Table. It was originally developed by Powerset and is now managed as a project of the Apache Software foundation as part of the Hadoop. A type of NoSQL storage that enables storage of arbitrary data (a value) using a unique identifier (key). A branch of artificial intelligence concerned with the development of algorithms that take as input empirical data, such as from sensors or databases. The algorithm is designed to identify complex relationships thought to be features of the underlying mechanism that generated the data and employ these identified patterns to make predictions based on new data. A software framework introduced by Google for processing huge datasets on certain kinds of problems on a distributed system.32 Also implemented in Hadoop. An application that uses and combines data presentation or functionality from two or more sources to create new services. These applications are often made available on the Web, and frequently use data accessed through open application programming interfaces or from open data sources. Massively parallel processing (MPP) The coordination of a large number of processors (or separate computers) to perform computations, where a processor or group of processors works on different parts of the program. Metadata Data that describes the content and context of data files, e.g., means of creation, purpose, time and date of creation, and author. Non relational database A database that does not store data in tables (rows and columns). NoSQL (not only SQL) A broad class of non relational, non SQL databases that often does not offer ACID guarantees. This class of databases encompasses document store, key value store, BigTable, and graph databases. This class of databases is useful for working with huge quantities of data structured or unstructured when the ability to store and retrieve vast quantities of data is more important than the ability to examine the relationships between the data elements. Source: BMO Capital Markets. Page 104 July 17, 2014

105 Exhibit 42. Glossary Term R Definition An open source (free) programming language and software environment for statistical computing and graphics. Relational database A database made up of a collection of tables (relations), i.e., data are stored in rows and columns. Relational database management systems (RDBMS) store a type of structured data. Semi structured data Data that do not conform to fixed fields but contain tags and other markers to separate data elements. Examples of semi structured data include XML or HTML tagged text. SQL Originally an acronym for structured query language, SQL is a computer language designed for managing data in relational databases. This technique includes the ability to insert, query, update, and delete data, as well as manage data schema (database structures) and control access to data in the database. Stream processing Technologies designed to process large real time streams of event data. Stream processing enables applications such as algorithmic trading in financial services, RFID event processing applications, fraud detection, process monitoring, and location based services in telecommunications. Also known as event stream processing. Streaming analytics Structured data Unstructured data Visualization Source: BMO Capital Markets. Analysis of data as it is generated data in motion. To be compared to the analysis of data after persistence data at rest. Data that reside in fixed fields. Examples of structured data include relational databases or data in spreadsheets. Data that do not reside in fixed fields. Examples include free form text (e.g., books, articles, body of e mail messages), untagged audio, image and video data. Technologies used for creating images, diagrams, or animations to communicate a message that are often used to synthesize the results of big data analyses. Page 105 July 17, 2014

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107 Tableau Software (DATA-NYSE) Stock Rating: Outperform Industry Rating: Market Perform Initiating Coverage With an Outperform Rating and $75 Price Target Investment Thesis We believe Tableau in one of the best-positioned companies in the analytics space with highly differentiated offerings that should experience sustained growth by democratizing data. The core of Tableau s technology, its Visual Query Language, is instrumental in providing a best-in-class user experience and empowers business users to perform analytics and glean valuable insight from corporate data in a way that hasn t been possible before. By virtue of its Live Query Engine, implementation is simple. Customers can leverage prior investments in existing data platforms without moving data, which we believe will help drive rapid adoption. We think that structured dark data stored and underutilized as part of normal business activity remains a substantial opportunity. Tableau is enabling connectors to blend and analyze unstructured data (Hadoop, NoSQL, Machine). Tableau is gaining share at the expense of Qlik and Tibco/Spotfire in the market for data discovery tools, in addition to traditional BI vendors. We see a growing addressable market and distribution as supportive of sustainable above-market growth. Forecasts & Valuation We see a favorable set up for shares. Tableau has the most upside of the analytics vendors, and we see current out-year consensus estimates potentially 10-20% conservative as it implies no upsell and flat ASPs on new customers. Our analysis of repeat purchases shows customers buying a minimum +3x over time supported by Tableau s land and expand strategy, which drives increased visibility and leverage. Shares have pulled in ~40% off their high and the valuation has compressed to a peer multiple, which we believe the stock can hold given what we see as the potential for +40% revenue growth for the next few years in an upside case. Recommendation We are initiating coverage of Tableau with an Outperform rating and a $75 price target, which is based on both our DCF and comparative multiple analyses and equates to 8.6x our 2015 EV/sales estimate. July 17, 2014 Joel P. Fishbein, Jr BMO Capital Markets Corp. [email protected] Brett Fodero / Edward Parker BMO Capital Markets Corp / [email protected] / [email protected] Price (16-Jul) $ Week High $ Target Price $ Week Low $51.99 Tableau Software (DATA) Price: High,Low,Close(US$) Volume (mln) DATA Relative to S&P 500 Earnings/Share(US$) 0.30 Q2 Q3 Q4 Q1 Q Last Data Point: July 15, 2014 (FY-Dec.) 2012A 2013A 2014E 2015E EPS $0.17 $ $0.10 $0.10 P/E na nm CFPS $0.18 $0.35 $0.09 $0.35 P/CFPS nm nm Rev. ($mm) $128 $232 $357 $485 EV/Rev. 27.6x 15.2x 9.9x 7.3x EBITDA ($mm) $14 $25 $11 $25 EV/EBITDA 249.9x 141.3x 308.3x 142.2x Quarterly EPS Q1 Q2 Q3 Q4 2012A $0.05 $0.05 $0.04 $ A -$0.05 $0.01 $0.08 $ E -$0.01a -$0.04 -$0.06 $0.00 Dividend $0.00 Yield 0.0% Book Value $9.45 Price/Book 6.5x Shares O/S (mm) 63.4 Mkt. Cap (mm) $3,870 Float O/S (mm) 37.5 Float Cap (mm) $2,288 Wkly Vol (000s) 5,116 Wkly $ Vol (mm) $354.6 Net Debt ($mm) -$617 Next Rep. Date na Notes: All values in US$ First Call Mean Estimates: TABLEAU SOFTWARE INC (US$) 2014E: -$0.10; 2015E: $ Page 107 July 17, 2014

108 BMO Capital Markets Tableau Software Investment Drivers In our view, Tableau in one of the best-positioned companies in the analytics space. By bringing analytics to the masses and driving increased business productivity across a wide variety of businesses and organizations, we believe Tableau will experienced sustained growth by democratizing data. The core of Tableau s technology, its Visual Query Language, is instrumental in providing a best-in-class user experience and empowers business users to perform analytics and glean valuable insight from corporate data in a way that hasn t been possible before. In doing so, we believe Tableau s offerings are highly differentiated in a market that is benefitting from one of the most powerful trends in information technology big data. By virtue of its Live Query Engine, implementation is simple and allows customers to leverage prior investments in existing data platforms without moving data, which should help drive rapid adoption. We believe that structured dark data stored and underutilized as part of normal business activity remains a substantial opportunity. Tableau is enabling connectors to blend and analyze unstructured data (Hadoop, NoSQL, Machine). We believe Tableau is gaining share at the expense of Qlik and Tibco/Spotfire in the market for data discovery tools, in addition to traditional BI vendors. We see a growing addressable market and distribution as supportive of sustainable above market growth. Market opportunity leaves significant runway Excellent execution in high-velocity transactional business with acceleration in enterprise adoption Sensitivity analysis of new and existing customers points to continued upside International opportunity: just scratching the surface Founding team still on-board Market Opportunity Leaves Significant Runway Data discovery is the new breed of business intelligence (BI), and emerging vendors are once again expanding a market that has matured in recent years. We believe Tableau is instrumental in driving this trend, as its focused sales to business and individual users are unlocking dollars that previously were confined to the narrow focus of the IT department. Moreover, we believe Tableau is taking share from other vendors in this new data discover segment. Today, Traditional BI vendors own ~64% of the market but grew on average of 4.5%, below the market growth rate of 7.9%. A key trend is that data discovery vendors are actually expanding the addressable market for BI application by creating products that are usable, appeal to and empower individual business users. Formerly, classic BI was confined to the IT department and generally required specific skill sets. The benefits of these new vendors business models are faster sales cycles and deployments, expansion into SMBs and departmental organizations, viral usage, and try before you buy selling. As a result, spending on data discovery is now three times faster than that of the traditional BI market. We believe that this accelerating growth in spending is largely Page 108 July 17, 2014

109 BMO Capital Markets Tableau Software incremental to the traditional BI market, but over time we expect it will come at the expense of traditional BI vendors (IBM, SAP, ORCL, SAS), especially as buying centers (IT vs. departmental) converge. While there is significant opportunity for multiple vendors in the emerging data discovery market, we think Tableau is best positioned to outgrow the competition. Tableau is actually gaining share at the expense of Qlik and Tibco/Spotfire in the market for data discovery tools. Tableau gained 11pts of market share over the past two years matching the combined declines of Qlik (7pts) and Tibco/Spotfire (4pts). The market is not zero-sum, and we do expect continued growth across the board. We take a top-down and bottom-up approach to sizing the BI market. Gartner estimates the total BI market at $14.05 billion today growing at a 7.3% CAGR to $18.60 billion by Our bottom-up customer sensitivity analysis yields a potential $25 billion market opportunity. Exhibit 1.Addressable Market and Market Share Analysis CAGR BI Platforms $ 8,442 $ 8,968 $ 9,571 $ 10,180 $ 10,828 $ 11, % CPM Suites $ 2,629 $ 2,827 $ 3,049 $ 3,285 $ 3,538 $ 3, % Analytic Applications and Performance Management $ 2,059 $ 2,261 $ 2,482 $ 2,728 $ 2,998 $ 3, % Total Busienss Intelligence Market $ 13,131 $ 14,055 $ 15,101 $ 16,193 $ 17,365 $ 18, % y o y 6.8% 7.0% 7.4% 7.2% 7.2% 7.1% y/y (traditional) 5.3% 5.6% 5.8% 5.2% 4.9% 4.4% y/y (contribution from Data Discovery) 1.5% 1.4% 1.6% 2.0% 2.3% 2.8% Data Discovery $ 853 $ 1,091 $ 1,395 $ 1,784 $ 2,266 $ 2, % y o y 34.8% 27.9% 27.9% 27.9% 27.0% 26.5% % Total Business Intelligence Market 6.5% 7.8% 9.2% 11.0% 13.0% 15.4% Data Discovery Market Share Tableau License +Maintenance Revenue $ 118 $ 213 $328 $446 $597 $ % QlikTechLicense +Maintenance Revenue $ 359 $ 431 $497 $574 $660 $ % Tibco Spotfire License +Maintenance Revenue $ 163 $ 171 $ 180 $ 189 $ 199 $ % Other $ 213 $ 275 $ 391 $ 575 $ 810 $ 1, % % Data Discovery Tableau 14% 20% 24% 25% 26% 27% QlikTech 42% 40% 36% 32% 29% 26% Tibco Spotfire 19% 16% 13% 11% 9% 7% Other 25% 25% 28% 32% 36% 39% Source: Gartner "High-Tech Tuesday Webinar: Collision of Data Discovery and Business Intelligence Will Cause Destruction", September 2013; Gartner "Forecast: Enterprise Software Markets, Worldwide, , 4Q13 Update", Company Update, BMO Capital Markets Estimates There are various estimates as to the penetration of BI tools used by the 615 million information workers globally (Forrester). In its S-1, Tableau states data from Forrester that assumes ~105 million of them, or 17%, of information workers globally use BI tools today. This is consistent with a February 2014 statement Eron Kelly, general manager of SQL Server Marketing at Microsoft, stating that maybe 10-20% of employees today use BI tools on any given day. Using these assumptions yields an average revenue per user of BI tools today of $134 per user per year given Gartner s estimated market size of $14.05 billion. Expanding the addressable market to business users and into different buying centers has and continues expand the addressable market for BI tools. Again, utilizing data from Tableau s S-1 citing Forrester estimates, ~363 million, or 59%, of information workers globally use spreadsheets Page 109 July 17, 2014

110 BMO Capital Markets Tableau Software today. This is equivalent to roughly a third of the estimated billion Microsoft Office users worldwide. Based on these assumptions, we estimate the incremental addressable market to be 258 million users. Assuming a $134 per user average selling price (ASP) and 33% penetration, this yields an incremental $11 billion opportunity. These estimates are illustrated in Exhibit 2 below. Exhibit 2. Incremental Market Opportunity Selling to Business Outside of IT Incremtal Market Opportunity for Busienss Users (M) Incremental Users est. 258,000, M spreadsheet users - 105M BI users= 258M ASP user Market Size ($M) $ 114 $ 124 $ 134 $ 144 $ 154 Penetration 3.2% $ 927 $ 1,009 $ 1,090 $ 1,171 $ 1, % $ 2,396 $ 2,606 $ 2,817 $ 3,027 $ 3, % $ 3,865 $ 4,204 $ 4,544 $ 4,883 $ 5, % $ 5,333 $ 5,802 $ 6,270 $ 6,739 $ 7, % $ 6,802 $ 7,400 $ 7,997 $ 8,594 $ 9, % $ 8,271 $ 8,997 $ 9,724 $ 10,450 $ 11, % $ 9,740 $ 10,595 $ 11,451 $ 12,306 $ 13,161 Source: BMO Capital Markets Research, Gartner, Forrester Incremental revenue from business users could drive the total addressable market opportunity to roughly $25 billion, which would yield a total user penetration of ~30%. Coincidently this is consistent with a poll from Gartner s 2012 Business Intelligence Summit where respondents reported that a mean of 31% of users had access to analytics tools. Based on our back of the envelope analysis, we estimate that Tableau has roughly 2 million subscribers, or less than 1% penetration of the estimated 615 global information workers. Exhibit 3. Addressable Market Opportunity Adressable Market (M) Total Information Workers 615,000,000 Forrester 2013 estimate ASP user Market Size ($M) $ 114 $ 124 $ 134 $ 144 $ 154 Penetration 17.1% $ 11,955 $ 13,005 $ 14,055 $ 15,105 $ 16, % $ 13,424 $ 14,603 $ 15,782 $ 16,961 $ 18, % $ 14,893 $ 16,201 $ 17,509 $ 18,817 $ 20, % $ 16,361 $ 17,798 $ 19,235 $ 20,672 $ 22, % $ 17,830 $ 19,396 $ 20,962 $ 22,528 $ 24, % $ 19,299 $ 20,994 $ 22,689 $ 24,384 $ 26, % $ 20,768 $ 22,592 $ 24,416 $ 26,240 $ 28,064 Source: BMO Capital Markets Research, Gartner, Forrester Excellent Execution in High Velocity Transactional Business With Acceleration in Enterprise Adoption Tableau has been successful in executing its land and expand business model; existing customers made up 66% of perpetual license revenue in Tableau has expanded the dollar upsell opportunity per customer from 1.8x in 2012, to 2.3x in 2013, and we conservatively estimate this will grow to 3.1x exiting Additionally, ASPs to new customers have been rising steadily every year, going from ~$7,000 per customer in 2011 to ~$8,500 in Page 110 July 17, 2014

111 BMO Capital Markets Tableau Software Exhibit 4. Customer Base and ASP Analysis FY12A FY13A FY14E FY15E Notes Number of Customer Accounts 11,247 17,139 24,039 29,889 Total Accounts including Term license y-o-y growth 46% 52% 40% 24% New Customer Accounts 3,529 5,892 6,900 5,850 Total Accounts including Term license y-o-y growth 51% 67% 17% -15% Cumaltive Perpetual License revenue per average customer $ 15,435 $ 19,362 $ 22,862 $ 27,432 Total Accounts Eccluding Term, Term mix constant y-o-y growth 38% 25% 18% 20% Perpetual license revenue per new customer $ 8,504 $ 8,583 $ 8,869 $ 8,892 Total Accounts Eccluding Term, Term mix constant y-o-y growth 21% 1% 3% 0% Implied Upsell per customer 1.8x 2.3x 2.6x 3.1x Cumaltive Pereptual Revenue/New Perpetual Customer Revenue Perpetual license revenue per existing customer $ 6,913 $ 8,728 $ 8,948 $ 9,140 y-o-y growth 57% 26% 3% 2% Source: BMO Capital Markets, Company Filings The company has gone to market with a high velocity, low ASP model that is not dependant on large customer wins. Typically, Tableau acquires a new customer by offering a free trial or making an initial sale at the department level. Subsequently, through positive word of mouth, the discovery of new use cases, and Tableau s direct sales efforts, these seed deals expand over time across departments, divisions, and geographies and can eventually proliferate enterprisewide. While average revenue per customer remains below $10,000 for both new and existing customers, deal sizes and customer lifetime value (CLTV) grow steadily overtime. Customer metrics have been consistently solid; retention rates remain above 90% as the company continues to focus on acquiring and growing customers. To date, Tableau has 19,000 customers, and has seen accelerating net customer additions (67% in 2013). This acceleration in net adds should lead to increased growth in maintenance and services revenues. Sensitivity Analysis of New and Existing Customers Points to Continued Upside We see the potential for meaningful upside to forward estimates despite a record year in 2013, which saw the release of Tableau 8.0, the company s highly successful IPO, growing market awareness high, a successful customer conference, and a worldwide customer tour. Our view is based on our scenario analysis, which considers perpetual license revenue, new and existing customer contribution, average selling prices (ASP), and customer additions. Our analysis, which conservatively assumes no upsell and flat new customer ASP, demonstrates that that current out-year consensus estimates are potentially 10-20% too low. Key drivers include the following: New product introductions - The release of Tableau 8.0, in March 2013, has had a positive effect on new business sales, owing to the incremental new functionality and marketing around the release. The company released its 8.1 version in 4Q13, 8.2 was released in July, and Tableau 9.0 is expected to be released in 1H15. The key feature in the 8.2 release is Mac support and storylines, which should further expand the addressable market. Expanded distribution - The company s 2014 plan is to accelerate investments in hiring, even after growing sales & marketing headcount 71% in FY2013. OEM partnerships have historically been a single-digit percentage of overall revenue but contribution has been growing. Further, system integrator and consulting partners (Accenture, Deloitte, among others) appear to be placing increased emphasis on analytics as a competitive differentiator, offering a natural tailwind to adoption. Page 111 July 17, 2014

112 BMO Capital Markets Tableau Software Upsell into the install base 66% of perpetual license revenue comes from the installed base and we our model contains what we believe to be conservative assumptions on dollar upsell opportunity per customer (2.7x and 3.1x in FY2014 and FY2015, up from 1.8x in 2102and 2.3x in 2013). Acceleration on large deal activity. Deals over $100K make up a low-single-digit percentage of overall transactions; however, large deals continue to drive upward pressure on the top line as they increase in number (455, up 90% y/y) given the fact that average ASPs are below $10,000. Exhibit 5. License Revenue Current Assumptions License Revenue FY11 FY12 FY13 FY14E FY15E Existing Customer $24 $53 $98 $153 $220 New Customer $16 $30 $51 $61 $52 Total Perpetual license $40 $83 $149 $215 $272 % dollar renewal rate 107% 133% 118% 103% 102% % perpetual license Existing Customer 59.0% 64.0% 66.0% 71.5% 80.9% New Customer 41.0% 36.0% 34.0% 28.5% 19.1% Y-o-Y growth Existing Customer 125.2% 84.0% 56.2% 43.3% New Customer 82.3% 68.5% 21.0% -15.0% Total Perpetual License 107.6% 78.4% 44.3% 26.6% Term license $ 4.26 $ 6.52 $ $ $ % total license 9.6% 7.3% 7.0% 7.0% 7.0% Total License Revenue $44 $90 $160 $231 $292 Y-o-Y growth 83.4% 102.4% 77.9% 44.3% 26.6% Source: BMO Capital Markets, company filings. Based on our subscriber analysis, seen above in Exhibit 4, we note the following: Net new customer accounts accelerated in each of the last three years and our forward estimates assume a deceleration in FY2014 and FY2015. We assume flat perpetual license revenue per new customer in FY2014 and FY2015. Page 112 July 17, 2014

113 BMO Capital Markets Tableau Software Exhibit 6. License Revenue Scenario Analysis ($mm) Scenario Analysis 2014E Mid Bull Existing Customer $153.4 $156.2 $163.6 New Customer $61.2 $63.2 $68.3 Total Perpetual license $214.6 $219.4 $231.9 % dollar renewal rate 103% 105% 110% % perpetual license Existing Customer 71.5% 71.2% 70.6% New Customer 28.5% 28.8% 29.4% Y-o-Y growth Existing Customer 56.2% 59.1% 66.7% New Customer 21.0% 25.0% 35.0% Total Perpetual License 44.3% 47.5% 55.9% Term license $16 $17 $17 % total perpetual revenue 7.5% 7.5% 7.5% Total License Revenue $231 $236 $249 Y-o-Y growth 44.3% 47.5% 55.9% Scenario Analysis 2015E Mid Bull Existing Customer $220 $230 $255 New Customer $52 $60 $68 Total Perpetual license $272 $290 $323 % dollar renewal rate 102% 105% 110% % perpetual license Existing Customer 80.9% 79.3% 78.9% New Customer 19.1% 20.7% 21.1% Y-o-Y growth Existing Customer 43.3% 47.5% 55.9% New Customer -15.0% -5.0% 0.0% Total Perpetual License 26.6% 32.4% 39.4% Term license $20 $22 $24 % total perpetual revenue 7.5% 7.5% 7.5% Total License Revenue $292 $312 $348 Y-o-Y growth 26.6% 32.4% 39.4% Source: BMO Capital Markets, company filings We have also back-tested these assumptions based on various ASPs for both new and existing customer perpetual license spend. In our analysis, we assume all customers are on perpetual licenses and hold the percentage of term license revenue steady. Page 113 July 17, 2014

114 BMO Capital Markets Tableau Software Exhibit 7. Perpetual License Customer-Based Sensitivity Analysis (revenue $mm) FY12 FY13 FY14E FY15E Total Customers 11,247 17,139 24,039 29,889 y/y growth 46% 52% 40% 24% Net Additions 3,529 5,892 6,900 5,850 y/y growth 51% 67% 17% -15% ,039 24,739 25,439 26,139 26,839 Total Customers Customer Additions 6,900 7,600 8,300 9,000 9,700 New $8, % $ $ $ $ $8, % Existing Customer $8, % $ $ $ $ $ $9, % Customer ASP $9, % $ $ $ $ $ $9, % ASP $7, % $ $ $ $ $ $9, % $7, % $ $ $ $ $ $9, % $7, % $ $ $ $ $ $9, % $7, % $ $ $ $ $ $9, % *Assumes all customers are perpetual license ,889 30,739 31,589 32,439 33,289 Total Customers Customer Additions 5,850 6,700 7,550 8,400 9,250 New $8, % $ $ $ $ $9, % Existing Customer $8, % $ $ $ $ $ $9, % Customer ASP $9, % $ $ $ $ $ $9, % ASP $7, % $ $ $ $ $ $9, % $8, % $ $ $ $ $ $9, % $8, % $ $ $ $ $ $9, % $7, % $ $ $ $ $ $9, % *Assumes all customers are perpetual license Source: BMO Capital Markets, company filings. International Opportunity: Just Scratching the Surface Tableau has been demonstrating strong growth internationally, up 112% in FY2013 (and accelerating to 122% in the second half of the year). International makes up only 20% of revenue, and the company cited international as one of the key themes for 2014 on its 4Q13 earnings call. Products currently support eight languages, and the majority of indirect sales internationally are through resellers. The company is aggressively expanding its direct and indirect sales internationally beyond its presence in Australia, Canada, France, Ireland, Japan (2012), Singapore (2012), and the UK. Qlik generates more than 60% of revenue from outside the Americas and more than 70% from outside the US. This stands in sharp contrast to most mature software companies, which typically generate just north of 50% of revenue from outside the US. We view this geographic distribution as a challenge for Qlik given the increased competition in the US. Qlik has been relatively unchallenged outside the US when touting its next-generation in-memory business discovery platform. Tableau s success in the US should be a competitive advantage as it increasingly captures international growth and expands within its customer base. Page 114 July 17, 2014

115 BMO Capital Markets Tableau Software Founding Team Still on-board Tableau was co-founded in 2003 by Christopher Stolte (chief development officer), Patrick Hanrahan (chief scientist) and Christian Chabot (CEO) out of Stanford University and the three still lead the company today. The initial development of VizQL began at Stanford in 1999, and Stanford has granted Tableau an exclusive license to commercialize the software and related patents that have resulted from that research. Retaining a visionary founding team is a rarity and we don t believe should be overlooked. Together, these founders own more than 30% of the company. Company Background Tableau has an opportunity to democratize data and to be an enabler of delivering analytics, which has the ability to drive increased productivity for business users. Its technology is differentiated through its Live Query Engine, which is compatible with more than 30 data sources, including SQL, Hadoop, NoSQL, Data Warehouses, Machine, and Cloud data. This allows users to instantaneously connect with and leverage investments in their existing data platforms without the burdens associated with moving data. Moreover, we believe that its front end Visual Query Language (VizQL) is best in class. Tableau is expanding the market for BI tools by targeting business users, and will increasingly capture dollars that were formerly spent on legacy BI tools. Its product will increasingly contain many of the features found in legacy tools; at the same time, we note that these newer applications will likely never compete with heavier analytical use cases, where business analysts and data scientists will still be needed. Tableau s business model is levered to data growth, but not tied to capacity. Strategically, the company is focused on getting as many users as possible on the system, driving increased usage of the product, encouraging companies to migrate users to enterprise agreements, and growing greater wallet share from customers. Products Tableau goes to market with three paid and one free product offerings: Tableau Desktop (released December 2003), Tableau Server (released March 2007), Tableau Public (released February 2010), and Tableau Online (released July 2013). These products are available in eight languages. Products are sold as a perpetual license (<10% of license is terms based) on a named user basis, with Tableau Server also offered on a CPU capacity basis. Adoption varies by use case. Companies whose customers are light users will typically buy Desktop licenses, and as usage scales (>300 users), Server is typically offered on a CPU basis in order to lower overall costs per user. The company has raised prices on its Server core pricing several times in the past as deals have scaled to support more users. The inflection in six figure deals ($299,000 ASP for core Server) demonstrates increased adoption within organizations. Heavy users, who need high levels of processing power, typically buy Server on a per user basis. Customers also typically buy one year of maintenance, which is generally set at 25% of license price. Page 115 July 17, 2014

116 BMO Capital Markets Tableau Software Exhibit 8. Product Pricing Products Price Desktop Public ASP/User/Year $999-1,999 Free Server Online ASP/User/Year $1,000 (minimum 10 users) $500 ASP/8 Cores/Year $299,000 (unlimited users) Source: Tableau The release of Tableau 8.0 in March 2013 has had a positive effect on new business sales, owing to the incremental new functionality and marketing around the release. In terms of functionality, Tableau 8.0 has several new features including Web and mobile authoring, free form dashboards, forecasting, integration with enterprise applications, such as salesforce.com and Google Analytics, and application programming interface (API) support. The company s API support includes a JavaScript API, which enables third-party applications to control the Tableau application, and a Data Extract API, which allows partners and customers to load data into Tableau programmatically. Web authoring in Tableau 8.0 enabled the release of Tableau Online and extended the use case to partners and web users. The company released an 8.1 release in 4Q13, released version 8.2 on July 9th, and is expected to release Tableau 9.0 in 1H15. The key features in the 8.2 release are Mac support, which wills further expand the addressable market, and Story Points, which is a method of communicating key insights from data through a narrative format. Technological innovation remains a key piece of the investment story, which is reflected in the fact that the company grew its R&D spend over 77% y/y in Tableau Desktop Tableau Desktop is a self-service analytics product targeted at information workers. The differentiating aspect of Tableau Desktop is its ability to integrate visualization with analytics. Its interface is built on Tableau s proprietary VizQL, which describes thousands of easily understood visual presentations of data including tables, maps, time series, dashboards and graphs; together, Tableau s software provides a visual window of data, which is normally encapsulated in and viewed through spreadsheets (Excel), databases (Oracle, SQL Server, SAP HANA, Teradata Aster), web applications (salesforce.com, Google Analytics), and new data sources (Hadoop, NoSQL databases) without any scripting or programming. Tableau Desktop translates users interactions into live queries, across these various platforms, and allows a customer to leverage their investments in database infrastructure. Tableau Desktop contains an in-memory data engine that can be used for rapid analysis. Tableau Public is a free cloud-based platform for analyzing and sharing public data. This offering allows users to easily visualize public data on their websites. People who visit these websites can interact with the visualizations and share them via social media. Page 116 July 17, 2014

117 BMO Capital Markets Tableau Software Exhibit 9. Tableau Desktop Editions Tableau Desktop Public Edition Windows application Personal Edition Windows application Professional Edition Windows application Operating System Saves to the Tableau Public Website Only Option Option Opens Data in Files Yes Yes Yes Opens Data in Databases No No Yes Save Work Locally No Yes Yes Export Results Locally No Yes Yes Data Limitation 1,000,000 rows Unlimited Unlimited Publish to Tableau Server No No Yes Cost Free $999 $1,999 Source: Tableau. Tableau Server Tableau Server is a business intelligence platform for organizations with enterprise-class data management, scalability, and security. The product provides shared visualizations and dashboards of data analyzed and published by individuals using Tableau Desktop and is also a platform for shared data, where organizations can use Tableau Server to centrally manage enterprise data sources and metadata. The collaborative features of Tableau Server are designed to foster more sharing of data to improve the dissemination of information across an organization. Tableau Online Tableau Online, the SaaS version of Tableau Server was released in July 2013 and already has several hundred customers. While Tableau Server is aimed at data that s behind the firewall, Tableau Online is geared toward cloud-based data sources like Salesforce.com, Google BigQuery, and Amazon Redshift. Data sources for Tableau Online must be white-listed by Tableau, and a Tableau Desktop license is required to access on-premise data. Key in the release of Tableau Online was the March 2013 release of Tableau 8.0, which brought with it Web authoring. Tableau Online is expected to be complementary to the company s on-premise business. The company is targeting three different adopters for Tableau Online: new users, Tableau Desktop users that are not using Server, and current on-premise Desktop and Server customers that are using Online in a different group as a complement to existing users. The company is seeing initial use around Tableau Server for hard to move data behind the firewall, and adopting Tableau Online to push out data to partners eliminating the need to provision behind the firewall access. Page 117 July 17, 2014

118 BMO Capital Markets Tableau Software Differentiated Technology: Visual Query Language (VizQL) and Hybrid Data Architecture We believe that Tableau s architecture is significantly differentiated from those of competitors. A key piece of technology differentiation is its Live Query Engine that allows users to instantaneously connect to large volumes of data in its existing format and location and leverage investments in their existing data platforms. Tableau s technology backbone consists of two key components: its Visual Query Language (VizQL) and Hybrid Data Architecture. VizQL is a visual query language for data that translates drag-and-drop actions into data queries and then expresses that information visually. VizQL statements define the mapping of database records to graphical representations. It s a declarative language that allows users to describe what picture should be created, not how to make it. This allows users to transform questions into pictures without the need for software scripts, chart wizards or dialogue boxes, all of which can inhibit speed and flexibility. The initial development of VizQL began at Stanford University in 1999; Stanford has granted Tableau an exclusive license to commercialize the software and related patents that has resulted from that research. Its Hybrid Data Architecture combines the power and flexibility of its Live Query and In- Memory Data Engines. The Hybrid Data Architecture enables these data engines to work together, allowing users the flexibility to access and analyze data from diverse sources and locations, while optimizing speed and performance for each source. The Live Query Engine interprets abstract queries generated by VizQL into syntax (SQL and MDX), which existing database systems (Microsoft, Oracle, IBM, EMC, SAP, Teradata, etc.) can understand. This allows users to instantaneously connect to large volumes of data in its existing format and location. This capability enables customers to leverage investments in their existing data platforms and to capitalize on the capabilities of high-performance databases while reducing the need for data transformation processes typically require the skills of trained specialists. Additionally, while traditional business intelligence products import data from the organization s database systems, Tableau s Live Query Engine enables queries to be run in existing databases with only the results of each query rendered. Live Query Engine is compatible with more than 30 data sources including the following: Structured relational data - much of which is underutilized dark data Hadoop Hortonworks, Cloudera, MapR NoSQL DataStax, MongoDB, MarkLogic Machine Released Connector to Splunk in March Cloud Salesforce.com and Google Analytics (ex. Dashboards in salesforce drives more usage in data sets). Page 118 July 17, 2014

119 BMO Capital Markets Tableau Software Exhibit 10. Select Data Sources Tableau Data Extract -From A File Microsoft Access Text File Microsoft Excel Workbook Tableau Server- From A Server SAP HANA Cloudera Hadoop MySQL SAP NetWeaver Business WH Hortonworks Hadoop Hive Odata SAP Sybase IQ MapR Hadoop Hive Oracle Teradata EDW MongoDB Oracle Essbase Teradata Aster Database MarkLogic HP Vertica IBM DB2 DataStax Enterprise PostgreSQL IBM Netezza Google Analytics Progress OpenEdge Microsoft Analysis Services Google BigQuery Actian Vectorwise Microsoft Powerpivot Salesforce Actian ParAccel Microsoft SQL Server Splunk Pivotal Greenplum Windows Azure MP Datamarket Firebird Other Databases Source: Company documents To complement Tableau s Live Query Engine, its In-Memory Data Engine enables users to import large amounts of data into its proprietary in-memory database. This option is valuable for customers that want to analyze data that is not stored in databases, such as text files, spreadsheets, and logs, and those seeking the performance capabilities offered through inmemory. This eliminates the need to invest in further database systems. Distribution: High Velocity Mostly Direct Sales Model Tableau closes thousands of transactions per quarter both directly and indirectly. With sub- $10,000 customer ASPs, Tableau typically acquires a new customer by offering a free trial or making an initial sale at the department level and expanding over time across departments, divisions, and geographies and eventually proliferating enterprise-wide. Sales cycles for transactions over $100,000 are generally over three months, and transactions of less than $100,000 generally take fewer than three months to close. Just over 50% of revenue comes in the last month of the quarter. The company also has a dedicated customer success team responsible for driving renewals of existing contracts. The company s 2014 plan is to make accelerated investments in hiring. Direct Sales. Direct sales includes inside sales teams and field sales teams. The inside sales team is based in regional sales hubs and focuses on seeding new sales at a low cost that can be expanded over time. Direct field sales team focused on large enterprises covering North America, Europe, Middle East and Africa (EMEA), Asia Pacific (APAC), and Latin America (LTAM). Direct sales teams partner with technical sales representatives who provide pre-sales technical support. Indirect Sales. Less than 10% of the sales organization is focused on indirect sales channels, which have made up less than 25% of revenue historically. Indirect sales channels include technology vendors, resellers, original equipment manufacturers (OEM), and independent software vendors (ISV). The majority of indirect sales are through resellers, predominantly internationally, and some of our technology partners, such as Teradata Corporation, are Page 119 July 17, 2014

120 BMO Capital Markets Tableau Software resellers. OEM partnerships have historically been a single digit but growing percentage of overall revenue. The company has more than 30 OEM relationships consisting of both traditional OEMs that provide a customized version of our products for their applications as well as SaaS-based OEMs that deliver analytics as a service. The company has relationships with system integrators but not on a reseller level. Market Backdrop The $15 billion dollar business intelligence market is going through a significant transition, driven by evolving business user requirements and enabled by advances in in-memory and data discovery/visualization technology. This is illustrated in Gartner s market share data, which shows traditional BI vendors commanding 64% of the market but only growing 4.5%, below the market growth rate of 7.9%. In suit, the BI market is shifting from rearward-looking centralized reporting of the past to forward-looking decentralized near-real time predictive analysis. Underpinning this transition is the explosion of big data, which holds valuable insight for companies willing to invest in technologies to capture and analyze it, thereby forcing other companies to invest lest they lose their competitive edge. Legacy BI tools have not lived up to their promise, particularly around ROI, as consolidation in the space (IBM/Cognos, Oracle/Hyperion, SAP/BusinessObjects) has not reduced complexity and has in fact slowed innovation. A new breed of vendors such as Tableau and Qlik has commoditized traditional reporting/query tools. Importantly, the consumerization of BI technology is in some cases shifting the end user from IT analysts to business users, which in effect is expanding the market opportunity. While this new breed of data discovery vendors is out in front at the moment, incumbents are coming to market with competitive tools, which will lead to an increasingly competitive battle for customer wallet share. Incumbents are attempting to stem customer losses by adding visualization tools to traditional offerings while data discovery vendors will broaden their data management capabilities to address traditional business analysts requirements, leading to a collision course as products and capabilities begin to converge With that said, we don t view the market as a zero-sum game for incumbents. Gartner anticipates that less than 25% of enterprises will fully replace their existing BI solutions. Large organizations will likely settle on multiple platforms, ranging from full enterprise BI suites, to BI embedded into applications and lightweight desktop self-service BI tools for business users. Data discovery vendors are growing market share, although it is unclear whether the BI incumbents (organically, or through acquisition) or the data discovery specialists will ultimately win out. Page 120 July 17, 2014

121 BMO Capital Markets Tableau Software Exhibit 11. Business Intelligence Market Share All Incumbents Losing Share Market Share Business Intelligence ($M) Revs '13 Share '12 Share '13 y/y growth Total $14, % CAGR 7.3% Top 3 share 50% 48% SAP $3, % 21.3% 5.3% Oracle $1, % 13.9% 2.1% IBM $1, % 12.7% 4.9% SAS $1, % 11.8% 6.0% Microsoft $1, % 9.6% 15.9% Qliktech $ % 3.0% 20.1% MicroStrategy $ % 2.9% 4.9% FICO $ % 2.6% 8.8% Tableau $ % 1.5% 80.5% Information Builders $ % 1.3% 0.3% Other vendors $2, % 19.4% 10.5% Gain Loss Source: Gartner The Shift to Data Discovery Data discovery is increasingly taking over as the next-generation BI architecture. Garter expects that by 2015, the majority of BI vendors will make data discovery their primary BI platform offering, shifting BI emphasis from reporting-centric to analysis-centric. As discussed in the above sections, traditional BI reporting tools depend on extracting data from a data warehouse and have been largely confined to reporting yesterday s news in static reports or preconfigured dashboards. Most business users aren t exposed to this information and rely on IT for reporting. Moreover, implementing these systems takes months and maintenance can cost 3-5x the cost of a BI application. Data discovery tools offer an intuitive interface, which makes the application accessible to many more users, enabling them to explore data, conduct rapid prototyping, and create proprietary data structures to store and model data from disparate sources. Business users themselves, unskilled in traditional business intelligence and data analytics, are able to create, modify, mash up and share their data, helping them to make better informed decisions. Based on reported results and our industry conversations, these data discovery deployments are beginning to move from small groups within companies to larger organizations and business units. Currently, IT is often a bottleneck for broader adoption due to internal politics and the lack of a cohesive data management initiative. This is perhaps the single biggest barrier to data discovery adoption and will help protect traditional BI players. Looking ahead, no single vendor is addressing business user ease of use and IT driven enterprise requirements, and as data Page 121 July 17, 2014

122 BMO Capital Markets Tableau Software discovery deployments grow and use cases become more complex this will emphasize the need for governance. This is the impetus of QlikView Next, expected later this year. Over time, search-based data discovery will ultimately drive mainstream adoption of data discovery platforms, similar to the way the web browser brought about ubiquitous use of the internet. Exhibit 12. Traditional BI Platforms vs. Data Discovery Platforms Traditional BI Platforms Data Discovery Platforms Key Buyers IT-driven Business-driven Main Sellers Approach Megavendors, large independents Top-down, IT-modeled (semantic layers), query existing repositories Fast-growing independents Bottom-up, business-usermapped (mashup), move data into dedicated repository User Interface Report/KPI dashboard/grid Visualization/interactive dashboard Use Case Monitoring, reporting Analysis Deployment Consultants Users Source: Gartner What s Enabling Data Discovery? The key underlying driver enabling the emergence of data discovery technology is advances in computing, specifically in the area of in-memory. Traditionally, OLAP systems accessed data stored on hard disk drives, which due to inherent limitations of electromechanical disks, experienced latency and delay. Queries typically could take hours. Storing or caching large amounts of data in-memory was cost prohibitive. However, the shift to 64 bit systems and the sustained reduction in memory prices has at last enabled the building of information systems that leverage memory versus disk in OLAP applications. As a result, analytical query times can be reduced from hours to minutes or even seconds. This shift to 64 bit systems has marked the inflection point for vendors like Qlik and Tableau, and in-memory is a core technology component of the SAP HANA and Oracle Exalytics vision. In-memory will be an important piece of the overall next-generation analytics space. Most solutions in the market max out at up to a billion rows of data and deal mostly with structure data. Machine-generated data creates billions of rows of data, which necessitates other types of processors like parallelization and Hadoop. Business Intelligence Competitive Positioning Market share in BI continues to be concentrated, but sources of innovation are more diverse. According to Information Week, much of the activity in the BI market has been dominated by emerging BI vendors focusing on experimenting with open source technology, producing a Page 122 July 17, 2014

123 BMO Capital Markets Tableau Software diverse set of solutions, marking a trend away from standardization. In 2012, 30% of those surveyed had standardized on a small handful of BI tools, falling from 47% in This reversal has occurred despite massive consolidation in the BI space last decade (SAP/Business Objects, Oracle/Hyperion, and IBM/Cognos). Today, traditional BI vendors command ~64% of the market but grew on less than 5% on average, below the overall market growth rate of 8%. After consolidating the market, the large IT vendors were largely focused on integrating acquired BI solutions into their broader software and infrastructure product portfolio, which generally resulted in underinvestment. As a result, we believe these legacy BI tools are considered old and lacking in modern functionality. Emerging data discovery vendors, by contrast, have lead with innovative solutions, which are rejuvenating the marketplace and leading to growth 3x that of traditional BI platforms. In summary, the consumerization of IT is resulting in a shift in the use of and the buying of BI and related services away from IT and toward individual business users and managers. Data discovery vendors continue to organize their go-to-market strategies around this trend and we expect traditional BI vendors to increasingly pivot away from IT to attack this new opportunity. This is consistent with Gartner s prediction that, by 2014, 40% of BI purchasing will be business-led rather than IT led. Exhibit 13. Standardization on Mega Vendors Is Dropping With Most Survey Respondents Preferring to Adopt Best of Breed Source: BMO Research, Forrester. Products from large vendors such as IBM, Oracle, Microsoft, and SAP are currently the most popular BI choices, with Tableau and Qlik ranking among the top five vendors mentioned in the BI-related inquiries at Forrester. This is consistent with data from Gartner that suggests that best-of-breed solutions are growing mindshare. Page 123 July 17, 2014

124 BMO Capital Markets Tableau Software Exhibit 14. Standardization on Mega Vendors Is Dropping With Most Survey Respondents Preferring to Adopt Best of Breed Source: BMO Research, Gartner We believe traditional BI applications from incumbent vendors will continue to lose share as standard reporting becomes commoditized and is embedded across the application stack. As noted above, in-memory and new data processing frameworks are leading to new application architectures, allowing vendors to offer solutions that let users embed analytics deeper into the business process. Incumbents are responding by focusing on the benefits of offering an end-toend stack while leveraging their massive installed bases, as evidenced by SAP s HANA strategy, Oracle s embedding of analytics within its Fusion Applications, and Microsoft s positioning of SharePoint as a delivery mechanism for BI. SaaS vendors like Salesforce.com, Workday, and Demandware are in the early stages of creating big data solutions that incorporate third-party data into their own cloud-based data and delivery business insights. Data discovery is one of the main disruptors of the incumbent vendors market dominance in the near to medium term, although IT concerns over duplicate data sets remains a potential roadblock. We have heard customers express reluctance around the perception of having to add yet another silo on top of existing systems, since newer data discovery tool often create another copy of corporate data in the process of performing analytics. The main three data discovery vendors Qlik, Tableau, and Tibco Spotfire control ~75% of the ~$1B market however data discovery market accounts for only ~8% of the total BI market. These vendors are moving beyond their original target business user customer and are attacking the enterprise more broadly by adding features that challenge larger vendors in terms of scalability, security, rich Page 124 July 17, 2014

125 BMO Capital Markets Tableau Software metadata, internationalization, and application programming interfaces (APIs), among other industrial-strength enterprise features. The key to the evolution of these vendors is how they approach and manage data governance and other issues sensitive to enterprise buyers. While the three vendors all offer data discovery tools with strong visualization capabilities, they differ in important ways. Qlik Qlik s products are targeted toward business users with strong visualization attributes. Qlik s solutions are based on proprietary systems, which extract data from existing data warehouses into in-memory systems. Qlik offers a data discovery option to connect directly to other data sources. The company is at the front end of a major product cycle with a full release of QlikView.Next (QV.N) slated for later this year. QlikView11(QV.11) is expected to be the primary driver of business in 2014, according to management. Adoption for the move to QV.N is expected to be gradual with some users staying on QV11, some upgrading, and others choosing QV.N for net new transformational use cases or users. We believe QV.N is a transformative release from a product and a consumption perspective that should expand the company s addressable market by bridging the gap between data discovery and complex BI platforms. The core of QV.11 and prior versions is its data discovery capabilities; however, with QV.N designed to give users the immediate insights while providing IT professionals the enterprise manageability and governance they require. Further, the release of the QV.N API extends use cases of the core QV application to very specific guided analytic experiences. Uneven execution remains the overarching issue at Qlik, however we re positive on the solution. Tibco Tibco Spotfire is geared more toward the high end of the market. Like Qlik, Spotfire leverages in-memory and uses connectors to hook into existing data warehouses. Tibco has had execution issues with Spotfire and has been losing share. We hear positive anecdotes on the product s ease of setup and use. Tibco released version 6.5 of Spotfire in 2Q, which includes a personal desktop addition, new simple-to-use mapping and staff capabilities, expanded data connectivity, especially for big data sources like Hadoop clusters, and enhanced usability features. Tibco also recently acquired Jaspersoft, which is strong in embedded BI applications. Tableau Tableau has a distinct architectural approach with its Visual Query Language (VizQL) Live Query Engine, which allows users to instantaneously connect to large volumes of data in an existing databases leverage investments in existing data platforms. Direct query access has been a strength of the platform since the product's inception. To complement Tableau s Live Query Engine, its Hybrid Data Architecture In-Memory Data Engine enables users to import large amounts of data into its in-memory database for customers that want to analyze data that is not already captured in existing databases (such as text files, spreadsheets, and logs) for those seeking the performance capabilities offered through in-memory. The company released its 8.1 release in 4Q13; version 8.2 was released in 2Q14, and Tableau 9.0 is expected to be released in 1H15. The key feature in the 8.2 release is Mac support. Established vendors are attempting to add data discovery features onto existing offerings through organic development or acquisition. Capabilities are thus far in general weaker than those offered by pure play vendors, but this may be sufficient to protect their existing installed base. Page 125 July 17, 2014

126 BMO Capital Markets Tableau Software IBM (Bachman, Market Perform) IBM offers a complete range of enterprise-grade BI, performance management and advanced analytics platform capabilities, complemented by a deep services organization. The IBM Cognos BI platform handles some of the largest deployments in the world, and in 2013, IBM significantly simplified the Cognos licensing model, reducing 28 SKUs to only two. IBM offers data discovery through its IBM Cognos Insight product. IBM has been acquiring domain specific experience in packaged analytic applications, including Algorithmics (credit and operations risk analytics), DemandTec (merchandising analytics), Emptoris (spend analytics), and Varicent (sales performance management). Infor Infor Business Intelligence is part of an end-to-end platform that encompasses BI and performance management offerings, both based on the MIS Alea product (also called MIS DecisionWare) acquired from Systems Union in Infor is investing significantly to enhance the attractiveness of its platform both inside and outside Infor's ERP installed base. Infor BI is used primarily for reporting. Infor BI 10x includes an in-memory multidimensional OLAP database; other offerings include Web frontends, such as Infor BI Dashboards/Motion Dashboards for data presentation and analysis, a feature-rich Microsoft Excel-based interface, a data integration tool, and a modeling tool. Infor BI Planning and Infor BI Consolidation offer data modeling and reporting, in support of a standard planning and financial consolidation process for a diversity of industries and domains. Infor Intelligent Open Network (ION), Business Vault and Workspace integrate BI content for Infor and non-infor ERP customers. Infor BI is a consideration by organizations looking for an integrated BI and performance management solution. Microsoft Microsoft has done a good job delivering a combination of business user capabilities with an enterprise-capable platform. The Microsoft data platform takes advantage of Office, Azure, and SQL Server. For data discovery, Microsoft hopes to tap its ~1 billion Office users and leverage enterprise licensing agreements in order to drive competitive pricing. Microsoft offers Power BI for Office 365, a cloud-based, self-service business intelligence solution with natural language capability and Power Query for Excel, which makes it easier for generalists to perform data discovery. The Azure HDInsight for elastic Hadoop is a managed cloud-based Hadoop service through a partnership with Hortonworks. This integrates with Power BI and Excel. The Microsoft Azure Intelligent Systems Service will help customers embrace the Internet of Things and securely connect to, manage and capture machine-generated data from sensors and devices, regardless of operating system. SQL Server 2014 is the first time in-memory is built into every SQL workload, enabling non-oltp to take advantage of in-memory without changing workloads. The Analytics Platform System (APS) combines the best of Microsoft s SQL database and Hadoop technology in one low-cost offering, leveraging partnerships with Dell and HP. PolyBase brings structured and unstructured data together in a data warehouse appliance. MicroStrategy MicroStrategy remains an industry benchmark in large BI deployments running on top of large enterprise data warehouses, and it is often viewed as a go-to vendor when enterprise requirements are complex. Functionality is one of the main reasons for selecting MicroStrategy; however, time to create reports is among the highest and MicroStrategy lacks user-friendly and comprehensive advanced analytic capabilities, as well as unstructured data support. In late 2013, MicroStrategy released an updated version of its visual data discovery engine. A scalable multiterabyte in-memory engine is also being Page 126 July 17, 2014

127 BMO Capital Markets Tableau Software developed in cooperation with Facebook, for release in This could differentiate MicroStrategy from direct competitors in the data discovery market. MicroStrategy has done a good job releasing cloud products and driving adoption in its installed base Oracle Oracle s BI and analytics technologies include Oracle BI, Oracle Exalytics In- Memory Machine, and Oracle Endeca Information Discovery. Customers choose Oracle for its stack integration and offers more than 80 prebuilt analytic applications for Oracle E- Business Suite, PeopleSoft, JD Edwards, Siebel and other enterprise applications, including industry-specific packaged analytic applications. Most Oracle BI deployments support traditional BI reporting and dashboarding. Oracle purchased Endeca to add searchbased data discovery. Endeca has good functionality, but integration and growth have slowed since the acquisition and the solution now lags pure-play competitors. Oracle BI on Exalytics uses the Oracle TimesTen In-Memory RDBMS for performance optimization. Oracle plays from a position of strength as a result of its dominance in database and applications, but execution around data integration has been uneven. SAP SAP customers use SAP BusinessObjects BI primarily for reporting. SAP has been investing heavily in SAP Lumira to establish a presence in the data discovery market. SAP is used to embed BI content, but is not widely used by customers to embed advanced analytic content, although the introduction of HANA is designed to change this. The company has had issues integrating BusinessObjects with legacy solutions like Business Warehouse Accelerator (BWA). Classic BusinessObjects is now being deemphasized as a front end for BWA. BusinessObjects is being bundled with HANA in an effort to drive the BWA installed base towards HANA adoption. SAP is positioning HANA as the strategic database and in-memory platform that will enable much of the advanced analytics functionality delivered by Lumira, Predictive Analysis and KXEN. SAS SAS tools have been used primarily by power users, data scientists and IT-centric BI developers. It s considered an expensive system that takes time to deliver reports, but has strong roots in advanced analytics. In 2012, SAS released Visual Analytics, a business user-oriented data discovery and BI platform targeted at less technical and analytically sophisticated users. The product is strong but is only relevant for SAS shops. The product uses SAS's LASR Analytic Server, an in-memory server for large-scale data analysis. Importantly, SAS has made the strategic decision to make Visual Analytics its "go forward" BI platform and the front-end reporting and analysis tool for all its analytic applications, which should unify SAS front-end tools, defend its installed base against stand-alone data discovery vendors, and address both business user and business analysts. What About Cloud BI? We expect cloud BI to emerge as a growing trend in the years to come. To date, cloud-based delivery has not been a popular option, representing less than 5% of overall BI spend today. However, the market is growing, up 42% in 2013, and we expect this trend to continue. The primary driver for increasing cloud deployment is that the percentage of data creation occurring off-premise, beyond the firewall, is growing. As this broad secular trend continues, data gravity will pull more workloads, services, and applications, including BI, into the cloud where the data is created and residing. Trust has been and continues to be a hurdle to cloud adoption, but there are indications that this is changing. Forty-five percent of Gartner s recent Magic Quadrant survey respondents noted a willingness to put mission-critical BI in the cloud, Page 127 July 17, 2014

128 BMO Capital Markets Tableau Software compared with 33% in As with most applications, the promise of increased collaboration with customers and partners and mobility are drivers of Cloud BI. Over time, we expect the proliferation of connected devices (the Internet of Things ) and continued growth in cloud services (SaaS, PaaS, IaaS) will create more cloud-based data and drive the adoption of Cloud BI solutions. Cloud business intelligence vendors including Birst, GoodData, Looker, Domo, Adaptive Insights have all garnered significant amounts of venture funding. GoodData s focus on building an end-to-end system from visual interface to customer data management to perform analytics-as-a-service is gaining good market traction. GoodData sees the combination of Tableau and Amazon RedShift in competitive situations. As IT becomes more heterogeneous we expect platforms like this to become more pervasive, and help drive cloud adoption. Established vendors, such as MicroStrategy, Tableau, and Tibco/Jaspersoft also offer cloud BI products. Balance Sheet and Capital Allocation The balance sheet remains solid, with net cash and investments of $617 million, or $9.72 per share as of March 31, We expect FCF of $6.4 million in 2014 and $26.8 million in Exhibit 15. BMO vs. Consensus Current Outlook Revenue guidance for 2014 of $ million implies 46-50% top-line growth. Based on our analysis we see current out year consensus estimates potentially 10-20% conservative as it implies no upsell and flat ASPs on new customers. We are not currently building in much leverage and are modeling flat margins; however, should our upside case play out we expect top-line upside to drop to the bottom line. 2QFY14E FY14E FY15E ($M, exc. EPS) BMO Guidance Street BMO Guidance Street BMO Street License $50.61 $ $ y/y growth 51.0% 44.3% 26.6% Maintenance and services $29.41 $ $ Total Revenue $80.02 $75 80 $ $ $ $ $ $ y/y growth 60.4% 59.3% 53.7% 51.6% 35.8% 35.2% Operating Income ($2.55) ($1 6) ($2.70) ($0.12) ($0 10) ($0.39) $8.79 $9.73 OM (non GAAP) 3.2% 3.4% 0.0% 0.1% 1.8% 2.0% EPS (non GAAP) ($0.04) $ (0.04) ($0.10) $ (0.09) $0.10 $ 0.11 FCF $2 $0 $26 Source: BMO Capital Markets Research, Thomson Reuters. Margin Expansion Opportunity Tableau s book ship license business model lends itself to increased margins over time. Margins and cash flow significantly outperformed in 2013 based on top-line upside, but as of today we are not anticipating a similar occurrence in FY2014 based on increased investments in the business. Our current model calls for 0.0% and 1.8% operating margins in FY2014 and FY2015 respectively versus the company s long-term 20%+ operating margin target. Page 128 July 17, 2014

129 BMO Capital Markets Tableau Software Gross margins are negatively being affected by continuous expansion and investment in global support and operations. We anticipate these will contract to ~89.8% by FY2015 from 92.2% in FY2013, in line with the company s long-term model (88-90%). We model increased spending in S&M and R&D in absolute dollars and as a percentage of revenue in The company s 2014 plan is to make accelerated investments in hiring, after growing S&M headcount 71% in FY2013. In terms of product development, version 8.2 was released in July, and Tableau 9.0 is expected to be released in 1H15. Over time we see leverage in the business from an increasing percentage of revenue related to high margin maintenance revenue and lower opex (predominantly S&M) offset by modestly lower gross margins. Valuation Our 10-year DCF analysis makes the following key assumptions, resulting in our target price of $75, in line with current levels: Revenue CAGR of 29%. EBIT margin slowly increasing to 21%. 11.0% WACC. 20x terminal cash flow multiple. Exhibit 16. DCF Analysis DCF Analysis ($in millions, except per share) FY2014E FY2015E FY2016E FY2017E FY2018E FY2019E FY2020E FY2021E FY2022E FY2023E TV CAGR Revenues $357 $485 $649 $855 $1,111 $1,428 $1,821 $2,305 $2,893 $3,602 $3,962 29% Growth 53.7% 35.8% 33.8% 31.8% 30.0% 28.5% 27.5% 26.5% 25.5% 24.5% 10.0% EBIT ($0) $9 $31 $67 $115 $176 $252 $353 $501 $678 $825 EBIT Margin 0% 2% 5% 8% 10% 12% 14% 15% 17% 19% 21% Tax Rate 0% 0% 0% 0% 15% 35% 35% 35% 35% 35% 35% Taxed EBIT ($0) $9 $31 $67 $97 $114 $164 $229 $326 $440 $536 Depreciation $12 $16 $21 $28 $37 $47 $60 $74 $93 $112 $123 CapEx ($22) ($32) ($41) ($54) ($67) ($86) ($104) ($129) ($153) ($187) ($194) Change in Working Capital $15 $34 $32 $34 $39 $43 $46 $58 $58 $72 $79 Free Cash Flow $4 $27 $44 $75 $106 $119 $166 $232 $323 $437 $545 Growth 70% 41% 12% 39% 40% 39% 35% 24% Discounted FCF $4 $22 $32 $50 $63 $64 $80 $101 $126 $154 Cumulative cash flow $695 17% Tac Rate 35.0% Terminal Value $3,455 83% WACC 11.0% Total DCF value $4,151 Cash Flow 20x Debt $0 Multiple Cash $617 $9.72 Market Value of Equity $4,767 Shares Outstanding 63 Share Price $75.14 Current Price $61.00 upside/(downside) 23% Source: BMO Capital Markets Research, company documents. Our $75 price target is based on 8.6x our 2015 EV/sales estimate, in line with the peer average. We believe the stock can hold at least a peer average multiple given what we see as the potential for +40% revenue growth for the next few years in an upside case. Page 129 July 17, 2014

130 BMO Capital Markets Tableau Software Exhibit 17. Peer Analysis (in Millions, Except Per Share Data) Recent Cash/ EV/Sales Revenue Growth Ticker Rating Price EV Share FY1E FY2E FY0-'FY1E FY1E-'FY2E Tableau Software Inc DATA OP $61.00 $3,254 $ x 6.7x 53.7% 35.8% vs On-Demand: High Growth Subscription -21% -21% vs Software: High Growth License -12% -11% vs Average -19% -18% vs Data Infrastructure 144% 112% On-Demand: High Growth Subscription Demandware Inc DWRE OP $58.99 $1,776 $ x 8.3x 43.1% 43.6% Marketo Inc MKTO NC $26.01 $912 $ x 4.9x 47.3% 31.9% Netsuite Inc N MP $80.79 $5,886 $ x 8.5x 30.9% 27.9% Servicenow Inc NOW NC $56.32 $7,767 $ x 8.6x 54.5% 38.4% Veeva Systems Inc VEEV NC $23.97 $3,079 $ x 8.8x 34.2% 24.2% Workday Inc WDAY MP $79.28 $13,123 $ x 12.0x 59.5% 45.8% Average 11.6x 8.5x 44.9% 35.3% Software: High Growth License Fireeye Inc FEYE NC $34.57 $4,365 $ x 7.2x 155.1% 46.6% Palo Alto Networks Inc PANW NC $77.59 $5,449 $ x 7.0x 46.7% 34.1% Splunk Inc SPLK MP $46.54 $4,561 $ x 8.4x 35.8% 32.9% Average 10.4x 7.5x 79.2% 37.8% Average 11.2x 8.2x 56.3% 36.2% Software: Data Infrastructure Commvault Systems Inc CVLT OP $48.59 $1,918 $ x 2.5x 15.0% 14.1% Informatica Corp INFA NC $33.59 $3,028 $ x 2.6x 11.9% 11.9% Microstrategy Inc MSTR NC $ $1,211 $ x 1.9x 6.2% 7.0% Qlik Technologies Inc QLIK MP $21.28 $1,645 $ x 2.6x 15.9% 15.5% Splunk Inc SPLK MP $46.54 $4,561 $ x 8.4x 35.8% 32.9% Teradata Corp TDC MP $41.78 $6,040 $ x 2.1x 3.4% 5.5% Tibco Software Inc TIBX NC $19.06 $3,147 $ x 2.7x 2.5% 6.3% Verint Systems Inc VRNT NC $47.96 $3,423 -$ x 2.8x 25.4% 9.0% Average 3.7x 3.2x 14.5% 12.8% (Fishbein: CVLT, DATA, DWRE, QLIK, N, SPLK, WDAY) (Bachman: TDC) (NC = Not covered. Thomson data for not covered companies) (OP= Outperform)(MP = Marketperform) *Estimates reflect latest complete and forward fiscal years Source: BMO Capital Markets Research, Thomson Reuters. Stock prices as of the close on July 16, Risks Competition. Tableau faces an intense and increasingly competitive market with rapidly changing technology and evolving standards. Its primary competitors are established enterprise vendors with significantly more resources. Future success will depend in large part on its ability to penetrate the existing market for business analytics software, as well as the continued growth and expansion of the emerging market for analytics solutions for business users. Upsell into install base and move up market. Growth partially depends upon expanding sales of products to and renewing license and maintenance agreements with existing customers and their organizations. Growth in revenues and profitability depends in part on the ability to complete more and larger enterprise sales transactions. These larger transactions may involve significant customer negotiation. Enterprise customers may undertake a significant evaluation process, which can last from several months to a year or longer. Page 130 July 17, 2014

131 BMO Capital Markets Tableau Software Narrow product focus. Tableau currently derives and expects to continue to derive substantially all of revenues from Tableau Desktop, Tableau Server, and Tableau Online. As such, the continued growth in market demand of these software products is critical to continued success. Scaling sales force. Future increases in revenues and profitability is tied to the size of its direct sales force, both in the U.S. and internationally, to generate additional revenues from new and existing customers. Plans are to substantially increase the number of direct sales professionals. International expansion. Roughly 80% of revenues come from customers inside the U.S. and Canada. International expansion is a key part of its growth strategy, subjecting the company to a variety of risks and challenges. Other risks include R&D efficiency, data privacy regulations, hosting failures, security breaches, potential acquisition integration, and litigation. Page 131 July 17, 2014

132 BMO Capital Markets Tableau Software Financial Models Exhibit 18. Income Statement Tableau Income Statement FY2012 FY2013 FY2013 FY2014E FY2014E FY2015E ($ in millions except per share) FY2012 3/31/2013 6/30/2013 9/30/ /31/2013 FY2013 3/31/2014 6/30/2014E 9/30/2014E 12/31/2014E FY2014E FY2015E License Maintenance and services Total Revenue Cost of revenue: license Cost of revenue: maint & services Non-GAAP Gross Profit Non-GAAP Operating Expenses Sales & Marketing R&D General & Administrative Total Non-GAAP Operating Expenses Non-GAAP Operating Income (3.248) (2.551) (3.769) (0.123) (+) Depreciation Non-GAAP EBITDA (1.906) (0.755) Other income (0.054) (0.053) (0.119) (0.177) (0.454) (0.803) (0.207) (0.200) (0.200) (0.200) (0.807) (0.800) Non-GAAP Earnings Bef.Taxes (3.301) (2.751) (3.969) (0.072) (0.930) Provision for Income Taxes (1.493) (0.345) (0.721) Non-GAAP Tax Rate 32.9% 45.2% 65.1% 8.7% -2.5% -4.2% 106.0% 0.0% 0.0% 0.0% % 0.0% Non-GAAP Net Income (1) (1.808) (0.351) (2.751) (3.969) (0.072) (7.143) Non-GAAP EPS $0.17 ($0.05) $0.01 $0.08 $0.20 $0.31 ($0.01) ($0.04) ($0.06) ($0.00) ($0.10) $0.10 Avg. Diluted Shares Outstanding (1) Non-GAAP excludes: One time expenses and stock-based comp. Expense Analysis (non-gaap): Cost of Revenues 8.0% 8.7% 8.5% 7.3% 7.2% 7.8% 9.2% 9.7% 9.7% 8.9% 9.3% 10.2% Sales & Marketing 47.7% 57.1% 52.8% 50.3% 46.9% 50.8% 48.5% 58.5% 60.5% 60.5% 57.5% 55.7% R&D 24.2% 29.8% 25.8% 22.9% 19.9% 23.6% 25.2% 26.0% 25.0% 22.0% 24.3% 23.6% General & Administrative 12.0% 12.6% 10.9% 9.2% 8.7% 10.0% 9.0% 9.0% 9.0% 8.5% 8.8% 8.6% Depreciation 3.0% 3.4% 2.8% 3.0% 2.8% 2.9% 3.4% 3.5% 3.4% 2.9% 3.2% 3.3% Margin Analysis (non-gaap): Product gross margin 99.7% 99.3% 99.7% 99.4% 99.6% 99.5% 99.7% 99.5% 99.5% 99.5% 99.5% 99.5% Maintenance/Services gross margin 73.7% 75.7% 74.8% 77.9% 75.9% 76.1% 74.4% 74.5% 74.5% 74.5% 74.5% 75.0% Gross Margin 92.0% 91.3% 91.5% 92.7% 92.8% 92.2% 90.8% 90.3% 90.3% 91.1% 90.7% 89.8% Operating Margin 8.0% -8.1% 2.1% 10.2% 17.3% 7.8% 8.1% -3.2% -4.2% 0.1% 0.0% 1.8% EBITDA Margin 11.1% (4.8%) 4.9% 13.3% 20.1% 10.7% 11.5%.3% (.8%) 3.0% 3.2% 5.1% Tax Rate 32.9% 45.2% 65.1% 8.7% (2.5%) (4.2%) 106.0%.0%.0%.0% (667.9%).0% Net Margin 5.4% (4.5%).6% 9.1% 17.2% 7.8% (.5%) (3.4%) (4.5%) (.1%) (2.0%) 1.6% Sequential Growth Rates (non-gaap): License (12.1%) 26.8% 25.2% 38.3% (16.5%) 4.5% 11.1% 34.2% Maintenance and services 15.9% 20.4% 16.9% 22.5% 11.4% 12.6% 11.3% 16.9% Total Revenue (4.3%) 24.7% 22.4% 33.4% (8.5%) 7.3% 11.1% 27.8% Gross Profit (5.0%) 24.9% 24.0% 33.5% (10.4%) 6.8% 11.1% 28.9% Operating Margin % (131.5%) 511.2% 125.1% -56.9% (142.0%) 47.8% (103.4%) Net Income (218.9%) (117.5%) % 151.7% (102.5%) 683.7% 44.3% (98.2%) Year-Over-Year Growth (non-gaap): License 102.4% 51.4% 65.6% 89.7% 93.0% 77.9% 83.3% 51.0% 34.0% 30.0% 44.3% 26.6% Maintenance and services 110.9% 88.0% 84.4% 91.0% 99.7% 91.6% 92.1% 79.7% 71.1% 63.3% 74.4% 52.4% Total Revenue 104.8% 62.1% 71.3% 90.1% 94.9% 82.0% 86.3% 60.4% 45.6% 39.6% 53.7% 35.8% Gross Profit 97.9% 58.7% 71.3% 92.9% 96.5% 82.5% 85.3% 58.3% 41.9% 37.0% 51.1% 34.4% Sales & Marketing 104.5% 121.6% 107.8% 102.1% 68.0% 93.9% 58.2% 77.9% 75.0% 79.9% 74.0% 31.5% R&D 74.3% 89.0% 83.6% 75.5% 67.3% 77.5% 57.7% 61.8% 59.2% 54.3% 58.0% 31.7% Operating expenses 98.5% 106.4% 96.3% 85.4% 61.7% 83.1% 54.9% 67.7% 67.0% 68.2% 65.1% 31.7% Source: BMO Capital Markets Research, company documents. Page 132 July 17, 2014

133 BMO Capital Markets Tableau Software Exhibit 19. Balance Sheet ($ in millions) 12/31/ /31/2012 3/31/2013 6/30/2013 9/30/ /31/2013 3/31/2014 6/30/2014E 9/30/2014E 12/31/2014E 12/31/2015E Assets FY2011 FY2012 FY2013 FY2014E FY2015E Cash & Cash Equivalents $ $ $ $ $ $ $ $ $ $ $ Accounts Receivable $ $ $ $ $ $ $ $ $ $ $ Prepaid expenses $ $ $ $ $ $ $ $ $ $ $ Income taxes receivable $ - $ $ $ $ $ $ $ $ $ $ Deferred income taxes $ $ $ $ $ $ $ $ $ $ $ Total Current Assets $ $ $ $ $ $ $ $ $ $ $ PP&E, net $ $ $ $ $ $ $ $ $ $ $ Deferred income taxes $ $ $ $ $ $ $ $ $ $ $ Deposits and other non-current assets $ $ $ $ $ $ $ $ $ $ $ Total Assets $ $ $ $ $ $ $ $ $ $ $ Liabilities Accounts payable $ $ $ $ $ $ $ $ $ $ $ Accrued liabilities $ $ $ $ $ $ $ $ $ $ $ Accrued compensation and related benefits $ $ $ $ $ $ $ $ $ $ $ Income taxes payable $ $ $ $ $ $ $ $ $ $ $ Deferred revenues $ $ $ $ $ $ $ $ $ $ $ Total Current Liabilities $ $ $ $ $ $ $ $ $ $ $ Deferred income taxes $ $ $ $ $ $ - $ - $ - $ - $ - $ - LT Deferred Revenues $ $ $ $ $ $ $ $ $ $ $ Other $ $ $ $ $ $ $ $ $ $ $ Total Liabilities $ $ $ $ $ $ $ $ $ $ $ Stockholders' equity $ $ $ $ $ $ $ $ $ $ $ Total Liabilities + Stockholder's Equity $ $ $ $ $ $ $ $ $ $ $ Balance Sheet Summary Current Ratio Book Value Per Share $0.88 $0.85 $4.25 $3.01 $3.48 $9.45 $8.79 $8.48 $8.24 $7.52 Cash Per Share $1.16 $1.16 $4.53 $3.17 $3.60 $9.72 $9.05 $8.71 $8.42 $7.91 Net Cash Per Share $1.16 $1.16 $4.53 $3.17 $3.60 $9.72 $9.05 $8.71 $8.42 $7.91 Return On Equity 18.7% 11.6% 2.0% 4.6% 10.4% 6.2% 4.0% 1.4% -1.2% 1.4% Return on Assets 6.7% 4.0% 1.1% 2.9% 7.0% 4.7% 3.2% 1.1% -1.0% 1.0% Avg. Diluted Shares Outstanding Model Assumptions DSO (excluding deferred revenue) DSO (billings) Accounts Payable Days (off COGS) Accrued liabilities(as % of Sales) 11% 10% 14% 11% 10% 14% 13% 11% 9% 7% Accrued comp(as % of Sales) 32% 29% 26% 28% 33% 26% 24% 22% 17% 13% Source: BMO Capital Markets Research, company documents. Page 133 July 17, 2014

134 BMO Capital Markets Tableau Software Exhibit 20. Cash Flow Statement FY2012 FY2013 FY2013 FY2014E FY2014E FY2015E ($ in millions) FY2012 3/31/2013 6/30/2013 9/30/ /31/2013 FY2013 3/31/2014 6/30/2014E 9/30/2014E 12/31/2014E FY2014E FY2015E Operating Activities non-gaap non-gaap non-gaap non-gaap non-gaap Net income $ $ (4.035) $ (2.575) $ $ $ $ (5.629) $ (2.751) $ (3.969) $ (0.072) $ (12.421) $ Depreciation $ $ $ $ $ $ $ $ $ $ $ $ Allowance for doubtful accounts $ $ $ $ $ $ $ - $ - $ - Stock-based comp $ $ $ $ $ $ $ $ $ - Excess tax benefit from stock-based compensation $ (1.541) $ (0.043) $ (0.265) $ (0.515) $ (4.902) $ (5.725) $ (3.329) $ (3.329) $ - Deferred taxes $ $ $ $ $ (3.753) $ (3.052) $ $ $ - Donation of stock to Tableau Foundation $ $ - $ - $ - $ - $ - $ - $ - $ - Net change in assets and liabilitites, excl. acquisitions Accounts receivable, net $ (17.567) $ $ (5.425) $ (10.644) $ (17.009) $ (30.488) $ $ (5.046) $ (10.986) $ (21.769) $ (27.146) $ (29.410) Prepaid expenses $ (1.585) $ (0.547) $ (1.252) $ (1.013) $ (1.946) $ (4.758) $ $ $ - Income taxes receivable $ (1.072) $ (2.536) $ (0.225) $ (0.692) $ $ (0.961) $ (0.036) $ (0.036) $ - Deferred revenue $ $ $ $ $ $ $ $ $ $ $ $ Accounts payable and accrued liabilities $ $ (0.748) $ $ $ $ $ (5.172) $ $ $ $ (3.884) $ Income taxes payable $ (0.051) $ $ $ $ (0.154) $ $ (0.072) $ (0.072) $ - Net Cash from Operations $ $ $ $ $ $ $ $ $ $ $ $ Investing Activities Capital Expenditures $ (7.036) $ (3.038) $ (3.306) $ (5.171) $ (6.092) $ (17.607) $ (3.780) $ (5.500) $ (6.500) $ (6.500) $ (22.280) $ (32.000) Net Cash from Investing $ (7.036) $ (3.038) $ (3.306) $ (5.171) $ (6.092) $ (17.607) $ (3.780) $ (5.500) $ (6.500) $ (6.500) $ (22.280) $ (32.000) Financing Activities Proceeds from IPO $ - $ - $ $ - $ - $ Payment on capital leases $ - $ - $ - $ - $ - $ - $ - $ - $ - Proceeds from stock options excercised $ $ $ $ $ $ $ $ $ - Stock buy-backs $ - $ - $ - $ - $ - $ - $ - $ - $ - Deferred initial public offering costs $ (0.271) $ (1.050) $ $ - $ - $ - $ - $ - $ - Excess tax benefits from stock compensation $ $ $ $ $ $ $ $ $ - Source: BMO Capital Markets Research, company documents. Net Cash from Financings $ $ $ $ $ $ $ $ - $ - $ - $ $ - Foreign Currency Impact $ - $ (0.034) $ (0.019) $ $ $ $ $ $ - Net Increase / Decrease in Cash $ $ $ $ $ $ $ $ (1.738) $ (5.547) $ (3.096) $ $ FCFE $ 7.2 $ 1.2 $ 2.1 $ 4.3 $ 12.6 $ 20.1 $ 10.5 $ (1.7) $ (5.5) $ (3.1) $ 0.2 $ 26.0 y-o-y -9% -59% 13% -17% NM 179% 804% -181% -230% -125% -99% 15725% FCFF $ 7.2 $ 1.2 $ 2.2 $ 4.4 $ 13.0 $ 21.0 $ 10.5 $ (1.5) $ (5.3) $ (2.9) $ 6.4 $ 26.8 y-o-y -9% -58% 15% -14% NM 189% 781% -170% -221% -122% -70% 321% FCFF/Share $0.18 $0.03 $0.04 $0.06 $0.19 $0.35 $0.17 -$0.02 -$0.08 -$0.04 $0.09 $0.35 FCF Margin 5.6% 2.9% 4.3% 7.0% 15.4% 8.7% 14.1% -2.2% -6.2% -2.7% 0.0% 5.4% Page 134 July 17, 2014

135 Qlik Technologies (QLIK-NASDAQ) Stock Rating: Market Perform Industry Rating: Market Perform July 17, 2014 Joel P. Fishbein, Jr BMO Capital Markets Corp. Brett Fodero / Edward Parker BMO Capital Markets Corp / [email protected] / [email protected] Initiating Coverage With a Market Perform Rating and $22 Price Target Investment Thesis Qlik has been an early pioneer in next-generation data discovery business intelligence tools, and customer loyalty remains high. However, a history of mixed execution continues to overhang the shares, and we are concerned regarding the near- to medium-term impact of the QlikView.Next launch. While we are positive on the product improvements, we are concerned about the impact that dual product and pricing models (perpetual and new "token" based) could have on customer adoption and sales cycles. In addition, anticipation for QlikView.Next has been building for the better part of two years, and we are cautious on the potential pause ahead of the release slated for 2H14. We look to evaluate sales execution and customer adoption around the pending QlikView.Next product cycle. Forecasts & Valuation Based on our scenario analysis, we see current consensus growth estimates (about 16% for FY2014 and FY2015) as potentially a few points aggressive unless an upside scenario emerges, assuming close rates improve. Our current model implies roughly flat ASPs on existing customers, a slight rebound in new customer revenue, and we are not modeling a significant uptick in business activity for QlikView.Next. However, seasonality is skewed steeper in the 2H, and as noted, we are cautious on the impact of QlikView.Next release. Sales and marketing expenses are high (more than 50% of revenue) relative to growth (less than 20%), and the company needs to reaccelerate growth to expand margins, which have been contracting since FY2010, and help drive the stock. Recommendation We are initiating coverage of Qlik with a Market Perform rating and a $22 price target, which is based on both our DCF and comparative multiple analyses and equates to 2.7x our 2015 EV/sales estimate, roughly in line with multiples of similar growth data infrastructure peers. Price (16-Jul) $ Week High $37.57 Target Price $ Week Low $20.17 QLIK TECHNOLOGIES INC (QLIK) Price: High,Low,Close(US$) Earnings/Share(US$) Volume (mln) QLIK Relative to S&P Last Data Point: July 15, 2014 (FY-Dec.) 2012A 2013A 2014E 2015E EPS $0.27 $0.27 $0.24 $0.35 P/E 88.7x 60.8x CFPS $0.22 $0.20 $0.19 $0.35 P/CFPS nm 60.8x Rev. ($mm) $389 $470 $545 $630 EV/Rev. 4.3x 3.5x 3.0x 2.6x EBITDA ($mm) $42 $44 $44 $63 EV/EBITDA 39.5x 37.2x 37.4x 26.2x Quarterly EPS Q1 Q2 Q3 Q4 2012A -$0.03 $0.02 $0.02 $ A -$0.09 -$0.02 $0.05 $ E -$0.12a -$0.03 $0.04 $0.33 Dividend $0.00 Yield 0.0% Book Value $2.96 Price/Book 7.2x Shares O/S (mm) 89.2 Mkt. Cap (mm) $1,898 Float O/S (mm) 88.5 Float Cap (mm) $1,883 Wkly Vol (000s) 8,395 Wkly $ Vol (mm) $229.0 Net Debt ($mm) -$253 Next Rep. Date na Notes: All values in US$ First Call Mean Estimates: QLIK TECHNOLOGIES INC (US$) 2014E: $0.25; 2015E: $ Page 135 July 17, 2014

136 BMO Capital Markets Qlik Technologies Investment Drivers Qlik has been an early pioneer of next-generation data discovery business intelligence tools, and customer loyalty remains high. However, a history of mixed execution continues to overhang the shares and we are concerned regarding the near- to medium-term impact of the QlikView.Next launch. While we are positive on the product improvements, we are concerned about the impact that dual product and pricing models (perpetual and new "token" based) could have on customer adoption and sales cycles. In addition, anticipation for QlikView.Next has been building for the better part of two years, and we are cautious on the potential pause ahead of the release slated for 2H14. We look to evaluate sales execution and customer adoption around the pending QlikView.Next product cycle. Main factors to consider: Large market opportunity pending execution Sensitivity analysis of new and existing customers points to modest upside Competitive landscape and QlikView.Next Mixed execution: concerns over impact of QlikView.Next Large Market Opportunity Pending Execution Data discovery is the new breed of Business Intelligence (BI), and emerging vendors are once again expanding a market that has matured in recent years Today, traditional BI vendors own about 64% of the market, but grew an average of 4.5% below the market growth rate of 7.9% in FY13. Tableau is gaining share at the expense of Qlik, Tibco/Spotfire in the market for data discovery tools based on our analysis, as well as traditional BI vendors. Tableau gained 11pts of market share over the past two years, matching the combined declines of Qlik (7pts) and Tibco/Spotfire (4pts). The market is not zero-sum, and we do expect continued growth across the board. (See our separately published note, "Next Generation Data Analytics: Data as a Strategic Currency," for a more comprehensive industry overview.) A key trend is that data discovery vendors are actually expanding the addressable market for BI application by creating products that are usable and appeal to and empower individual business users. Formerly, classic BI was confined to the IT department and generally required specific skill sets. The benefits of these new vendors business models are faster sales cycles and deployments, expansion into SMBs and departmental organizations, viral usage, and try before you buy selling. As a result, spending on data discovery is now growing three times faster than that of the traditional BI market. We believe that this accelerating growth in spending is largely incremental to the traditional BI market, but over time, we expect it will come at the expense of traditional BI vendors (IBM, SAP, ORCL, SAS), especially as buying centers (IT vs. departmental) converge. While there is a significant opportunity for multiple vendors in the emerging data discovery market, we think Tableau is best positioned to outgrow the competition, which as stated above, is gaining share in this emerging segment. Page 136 July 17, 2014

137 BMO Capital Markets Qlik Technologies We take a top-down and bottom-up approach to sizing the Business Intelligence market. Gartner estimates the total BI market is at $14.05 billion today and is growing at a 7.3% CAGR to $18.60 billion by Our bottom-up customer sensitivity analysis yields a potential $25 billion market opportunity. Exhibit 1. Addressable Market and Market Share Analysis ($ thousands) CAGR BI Platforms $ 8,442 $ 8,968 $ 9,571 $ 10,180 $ 10,828 $ 11, % CPM Suites $ 2,629 $ 2,827 $ 3,049 $ 3,285 $ 3,538 $ 3, % Analytic Applications and Performance Management $ 2,059 $ 2,261 $ 2,482 $ 2,728 $ 2,998 $ 3, % Total Busienss Intelligence Market $ 13,131 $ 14,055 $ 15,101 $ 16,193 $ 17,365 $ 18, % y o y 6.8% 7.0% 7.4% 7.2% 7.2% 7.1% y/y (traditional) 5.3% 5.6% 5.8% 5.2% 4.9% 4.4% y/y (contribution from Data Discovery) 1.5% 1.4% 1.6% 2.0% 2.3% 2.8% Data Discovery $ 853 $ 1,091 $ 1,395 $ 1,784 $ 2,266 $ 2, % y o y 34.8% 27.9% 27.9% 27.9% 27.0% 26.5% % Total Business Intelligence Market 6.5% 7.8% 9.2% 11.0% 13.0% 15.4% Data Discovery Market Share Tableau License +Maintenance Revenue $ 118 $ 213 $328 $446 $597 $ % QlikTechLicense +Maintenance Revenue $ 359 $ 431 $497 $574 $660 $ % Tibco Spotfire License +Maintenance Revenue $ 163 $ 171 $ 180 $ 189 $ 199 $ % Other $ 213 $ 275 $ 391 $ 575 $ 810 $ 1, % % Data Discovery Tableau 14% 20% 24% 25% 26% 27% QlikTech 42% 40% 36% 32% 29% 26% Tibco Spotfire 19% 16% 13% 11% 9% 7% Other 25% 25% 28% 32% 36% 39% Source: Gartner "High-Tech Tuesday Webinar: Collision of Data Discovery and Business Intelligence Will Cause Destruction", September 2013; Gartner "Forecast: Enterprise Software Markets, Worldwide, , 4Q13 Update", Company Update, BMO Capital Markets Estimates There are various estimates as to the penetration of BI tools used by the 615 million information workers globally (Forrester). In its S-1, Tableau quotes data from Forrester which assumes about 105 million, or 17%, of information workers globally use BI tools today. This is consistent with a February 2014 statement made by Eron Kelly, general manager of SQL Server Marketing at Microsoft, stating that maybe 10%-20% of employees today use BI tools on any given day. Using these assumptions yields an average revenue figure for BI tools today of $134/user per year given Gartner s estimated market size of $14.05 billion. Expanding the addressable market to business users and into different buying centers continues to expand the addressable market for BI tools. Again, utilizing data from Tableau s S-1, which cites Forrester estimates, about 363 million, or 59%, of information workers globally use spreadsheets today. This is equivalent to roughly a third of the estimated billion Microsoft Office users worldwide. Based on these assumptions, we estimate the incremental addressable market to be 258 million users. Assuming a $134/user average selling price (ASP and 33% penetration, this yields an incremental $11 billion opportunity. These estimates are illustrated in Exhibit 2. Page 137 July 17, 2014

138 BMO Capital Markets Qlik Technologies Exhibit 2. Incremental Market Opportunity Selling to Business Outside of IT Incremtal Market Opportunity for Busienss Users (M) Incremental Users est. 258,000, M spreadsheet users - 105M BI users= 258M ASP user Market Size ($M) $ 114 $ 124 $ 134 $ 144 $ 154 Penetration 3.2% $ 927 $ 1,009 $ 1,090 $ 1,171 $ 1, % $ 2,396 $ 2,606 $ 2,817 $ 3,027 $ 3, % $ 3,865 $ 4,204 $ 4,544 $ 4,883 $ 5, % $ 5,333 $ 5,802 $ 6,270 $ 6,739 $ 7, % $ 6,802 $ 7,400 $ 7,997 $ 8,594 $ 9, % $ 8,271 $ 8,997 $ 9,724 $ 10,450 $ 11, % $ 9,740 $ 10,595 $ 11,451 $ 12,306 $ 13,161 Source: BMO Capital Markets Research, Gartner, Forrester Incremental revenue from business users could drive the total addressable market opportunity to roughly $25 billion, which would yield a total user penetration of about 30%. Coincidently, this is consistent with a poll from Gartner s 2012 Business Intelligence Summit where respondents reported that a mean of 31% of users had access to analytics tools. Exhibit 3. Addressable Market Opportunity Adressable Market (M) Total Information Workers 615,000,000 Forrester 2013 estimate ASP user Market Size ($M) $ 114 $ 124 $ 134 $ 144 $ 154 Penetration 17.1% $ 11,955 $ 13,005 $ 14,055 $ 15,105 $ 16, % $ 13,424 $ 14,603 $ 15,782 $ 16,961 $ 18, % $ 14,893 $ 16,201 $ 17,509 $ 18,817 $ 20, % $ 16,361 $ 17,798 $ 19,235 $ 20,672 $ 22, % $ 17,830 $ 19,396 $ 20,962 $ 22,528 $ 24, % $ 19,299 $ 20,994 $ 22,689 $ 24,384 $ 26, % $ 20,768 $ 22,592 $ 24,416 $ 26,240 $ 28,064 Source: BMO Capital Markets Research, Gartner, Forrester Sensitivity Analysis of New and Existing Customers Points to Modest Downside We see the potential for modest downside to forward consensus estimates pending execution. Our current below-consensus model calls for an acceleration in business activity. We are cautious on the execution surrounding the release of QlikView.Next. Based on our scenario analysis of perpetual license revenue, new and existing customer contributions, and ASPs, we see current consensus estimates as potentially modestly aggressive as they imply roughly a slight acceleration in new customer revenue. Page 138 July 17, 2014

139 BMO Capital Markets Qlik Technologies Exhibit 4. Billings - Current Assumptions ($ millions) Billings FY10 FY11 FY12 FY13 FY14E FY15E Existing Customer $161 $230 $301 $365 $432 $502 New Customer $80 $108 $106 $122 $140 $158 Total Billings $241 $338 $407 $487 $572 $660 % dollar retention 95% 89% 90% 89% 88% % billings Existing Customer 67.0% 68.0% 74.0% 75.0% 75.5% 76.0% New Customer 33.0% 32.0% 26.0% 25.0% 24.5% 24.0% Y-o-Y growth Existing Customer 42.2% 31.3% 21.1% 18.3% 16.3% New Customer 35.9% -2.0% 14.9% 15.0% 13.2% Total Billings 40.1% 20.6% 19.5% 17.4% 15.5% Book to Bill 106.4% 105.3% 104.8% 103.5% 104.8% 104.8% Source: BMO Capital Markets, Company Filings Exhibit 5. Billings - Scenario Analysis ($ millions) In our scenario analysis we note that billings form existing customers have been increasing over the past two years as growth has declined. We model this as stabilizing and new customer growth as rebounding. In addition, we have backed tested these assumptions for license revenue based on new and existing customer license mix and book to bill comparisons. Scenario Analysis 2014E Mid Bull Existing Customer $432 $437 $441 New Customer $140 $142 $145 Total Billings $572 $578 $586 % dollar renewal rate 89% 90% 91% % billings Existing Customer 75.5% 75.5% 75.3% New Customer 24.5% 24.5% 24.7% Y-o-Y growth Existing Customer 18.3% 19.6% 20.9% New Customer 15.0% 16.6% 19.0% Total Billings 17.4% 18.8% 20.4% Book to Bill 104.8% 104.8% 104.8% Scenario Analysis 2015E Mid Bull Existing Customer $502 $514 $527 New Customer $158 $162 $167 Total Billings $660 $676 $694 % dollar renewal rate 88% 89% 90% % billings Existing Customer 76.0% 76.0% 75.9% New Customer 24.0% 24.0% 24.1% Y-o-Y growth Existing Customer 16.3% 17.7% 19.3% New Customer 13.2% 14.3% 15.6% Total Billings 15.5% 16.8% 18.4% Book to Bill 104.8% 104.8% 104.8% Source: BMO Capital Markets, Company Filings Competitive Landscape and QlikView.Next The company is at the front end of a major product cycle with a full release of QlikView.Next (QV.N) slated for later this year. QlikView11(QV.11) is expected to be the primary driver of business in 2014, according to management. Adoption for the move to QV.N is expected to be gradual, with some users staying on QV11, some upgrading, and others choosing QV.N for net new transformational use cases or users. We believe QV.N is a transformative release from a product and a consumption perspective that should expand the company s addressable market Page 139 July 17, 2014

140 BMO Capital Markets Qlik Technologies by bridging the gap between data discovery and complex BI platforms. Uneven execution remains the overarching issue at Qlik; however, we re positive on the solution. Product. The core of QV.11 and prior versions is their data discovery capabilities; however, QV.N is designed to give users the immediate insights they need and IT professionals the enterprise manageability and governance they require. Further, the release of the QV.N API makes it a platform to extend use cases of the core QV application to very specific, guided analytic experiences using its APIs. QV.N will also bring a new release cadence. One major release and three feature releases annually versus the historical release cadence of once every two to four months. Pricing. Pricing is expected to move from a license based on desktops and servers to token based (named and concurrent) to align more with customer adoption patterns and infrastructure variability. Customers will then be able to manage the license allocations based on customer usage. QV.N is expected to address the growing need for governed data discovery. The lack of enterprise features in relation to governance, administration and scalability is preventing more mainstream BI adoption of data discovery tools. IT is often a bottleneck for broader adoption due to internal politics and the lack of a cohesive data management initiative. Exhibit 6. R&D Spending Below Analytics Peers R&D ($ millions) Run Rate % Revenue y/y growth Analytics Tableau $ % 58% Splunk $ % 44% Qlik $ % 3% Source: BMO Capital Markets Research, Company Data Qlik generates more than 60% of its revenue from outside the Americas and over 70% from outside the US. This stands in sharp contrast to most software companies, which when mature generate just north of 50% of revenue from outside the US. We believe Qlik s current geographic revenue distribution may be a harbinger of increasing competition. Competition in the data discovery market is more intense in the US, as opposed to in Europe where Qlik has gone relatively unchallenged. Tableau, arguably the company s number one competitor, has been demonstrating strong growth internationally, up by 112% in FY2013 and accelerating in 2H13, up 122%. Mixed Execution: Concerns Over Impact of QlikView.Next Qlik has a history of mixed execution, and we remained concerned over the near- and mediumterm effects of the QV.N launch later this year. The release of QV.N means two products and two pricing models for several years. QV.N is expected to be launched in a phased fashion and will be sold along with QV.11 for several more years. We have two major concerns around this strategy. First, the Page 140 July 17, 2014

141 BMO Capital Markets Qlik Technologies Exhibit 7. History of Mixed Execution ($ millions) introduction of a significantly enhanced product and a new pricing model could cause confusion and elongate sales cycles. We worry that this could be a multi-year issue if customers are not receptive to token pricing and choose to buy QV.11 perpetual licenses instead. Second, QlikTech has missed its third quarter two years in a row (weak Europe and channel sales in 3Q13 and softness in the Americas and with direct enterprise deals in 3Q12), and there could be a pause in buying ahead of the QV.N release, which is slated for 2H14. To be fair, Qlik has not experienced a slowdown in front of prior releases and management is confident that QV.N will be no different, but given the significant changes being introduced in product and pricing and with anticipation for QV.N building for the better part of two years, we believe a more cautious stance is justified. We note the company s customer conference is November this year. Management discipline. Qlik has struggled with sales force attrition issues in the past. While the company spent 2012 strengthening sales force execution and improving productivity, the unintended consequence was that sales teams became more focused on closing quarters rather than building the pipeline, resulting in uneven execution in Management continues to address sales engagement and the coverage model but execution remains uneven, and pipeline management is a key company initiative. Quarter Q1-Mar-12 Q2-June-12 Q3-Sept-12 Q4-Dec-12 Q1-Mar-13 Q2-June-13 Q3-Sept-13 Q4-Dec-13 Q1-Mar-14 Consensus $ 78.2 $ 84.8 $ 88.4 $ $ 91.3 $ $ $ $ Reported $ 79.3 $ 85.8 $ 86.1 $ $ 96.6 $ $ $ $ % Delta 1.5% 1.2% -2.6% 8.9% 5.8% 1.4% -3.4% 2.2% -1.9% Source: BMO Capital Markets, Company Filings, Thomson Company Background Qlik was founded in 1993 as a consulting company in Lund, Sweden. The company subsequently developed Qlik's patented in-memory associative technology. Its flagship product, QlikView, has more than 32,000 customers in over 100 countries worldwide. The company s online community [ has over 100,000 registered users. Its products address a range of customers from small businesses to middle-market customers to large enterprises. Typical customer adoption starts at the user or department level and then expands across the organization by targeting other business units, geographies, and use cases. As customers expand their deployments and in larger organizations, Qlik increasingly sells to IT departments, as well as business users. Its Business Discovery platform is powered by its in-memory engine which maintains associations in data and calculates aggregations, rapidly enabling users to explore live data, uncover insights used to see hidden trends, and make discoveries and solve problems in new ways. Users can define whatever view or type of insight they want and are not limited to predefined calculations or preconceived insights based on data joins made by IT. The key value proposition provided by QlikView is the ability to allow non-technical users to explore and glean insights from corporate data. Qlik s proprietary Natural Analytics technology and design approach taps into people s natural ability to detect patterns, compare, sort, and categorize Page 141 July 17, 2014

142 BMO Capital Markets Qlik Technologies information. The tool helps users anticipate outcomes and collaborate on decisions without requiring reports from highly trained data analysts. Its app model allows users of varying skill levels to build apps themselves, and they can often take over maintenance of apps that developers have built. Importantly, Qlik, like other data discovery vendors, can easily leverage structured system of record business data as well as new forms of unstructured data through technology integrations. Exhibit 8. QlikView Architecture Source: Qlik Technologies Products QlikView deployments range from small workgroup desktop deployments to enterprise-wide server deployments that provide centralization of all QlikView analysis. The company s land and expand business model encourages the move toward broader server-based deployments to more easily share information and collaborate around discoveries made. The historical per user or per server basis licensing model will change to align more with usage with the introduction of tokens in QV.N. QlikView is sold as an on-premise license; however, users have also deployed it in the cloud, enabled by the QlikView cloud console in QV.N that provides a framework to build and deploy QlikView servers on top of cloud infrastructure (AWS, Rackspace, private clouds). Deployment models are as follows: QlikView Desktop is designed to provide business users with a simple and efficient way to create analytic applications to solve specific business challenges. QlikView Desktop enables the user to load data from disparate sources such as databases, data warehouses, flat files, SAP, Salesforce.com, XML files, or web services into the system s memory. QlikView Desktop is a free download (called QlikView Personal Edition) that has full capabilities to develop analyses, Page 142 July 17, 2014

143 BMO Capital Markets Qlik Technologies but is restricted to analyses built by customers themselves. To share the analyses with another user, each user must have an individual QlikView license, rather than a personal use license. This deployment approach is typically favored by organizations with small user populations and/or poor network connectivity. QlikView Server provides centralization for all QlikView analysis and is designed for departmental and enterprise deployments. QlikView Server supports authentication and security models to ensure appropriate user access and simultaneous access to analyses by large user groups and is designed to spread calculations over all available CPU cores. QlikView Server licenses come in two flavors: Small Business Edition, for fewer than 25 users, and Enterprise Edition for more than 25 users. Access to the QlikView Server is governed by a client access license (CAL) most commonly a named user CAL. The QlikView Publisher component is an administrative interface for maintaining QlikView analyses that is licensed on a per server basis. Looking ahead, we expect later releases to include more predictive analytics. Technical Differentiation The core of QlikView is its patented software engine that takes data from other sources, compresses it (to approximately 10% of its original size), and holds it in memory, where it is available for immediate exploration by multiple users. Qlik s software is designed to distribute and manage workloads across all available CPU cores. By moving all data in-memory, its software does not require the use of data warehouses for high-performance analyses, resulting in more immediate time to value. In most situations, customers will identify data subsets to solve specific business problems and deploy QlikView apps around these subsets. For datasets too large to fit in-memory, QlikView connects directly to the data source (Hadoop cluster, data warehoduse, etc.) using QlikView Direct Discovery, allowing more value to be derived from an existing investment. However, we believe that Qlik is playing catch up to Tableau with its direct Discovery capabilities. Some key attributes of the Qlik solution: Patented core technology. Generates views of information for nontechnical users as needed without the need to develop reports or visualizations by those users. The associative experience. Business Discovery platform engine automatically manages all the relationships in the data and presents information to the user using a green/white/gray interface. User selections are highlighted in green, associated data is represented in white, and excluded (unassociated) data appears in gray. Interactive visualization. A combination of visualization with powerful back-end processing capabilities and associative navigation approach that provides a greater level of interactivity and insight. Page 143 July 17, 2014

144 BMO Capital Markets Qlik Technologies Collaboration and mobility. Enabling users to incorporate people and places into decision making, enable real-time collaborative sessions, and shared bookmarks, and platform capabilities are available on most mobile devices. QV.N is expected to address the growing need for governed data discovery. The lack of enterprise features in relation to governance, administration, and scalability is preventing more mainstream BI adoption for data discovery vendors. The current QlikView Governance Dashboard helps IT professionals maximize data governance by discovering how QlikView is used at a granular level. Distribution: Land and Expand Model With Heavy in-direct Mix The Qlik business model focuses on growing the value of a customer over time. Typical customer adoption starts at the user or department level and then expands across the organization by targeting other business units, geographies, and use cases. As customers expand their deployments, Qlik increasingly sells to IT departments as well as business users. To encourage adoption, it offers QlikView Personal Edition, a downloadable, easy-to-install, fullfeatured version of QlikView for individual use free of charge. The sales approach leverages a direct sales and an indirect partner network of more than 1,700 which includes solution providers, OEM relationships, and systems integrators. The sales mix of license and first-year maintenance was 47% direct and 53% indirect exiting 4Q13. Direct sales operate predominantly on a named account basis, so the major enterprises around the world are supported by named account managers. No individual partner represents more than 2% of revenue. The company typically enters new markets through partnerships and solution providers where it has no direct sales presence and over time builds a direct sales force in a particular market if demand materializes. To date, the company serves 120 countries through partners versus 26 directly. In addition, OEM partners and technology partners accounted for approximately 8% of total billings in FY13. We summarize Qlik s go-to-market below: Investing in direct sales capacity and capability, targeting both the enterprise and small businesses. Continuing to focus on building vertical and functional expertise to support Manufacturing, Financial Services, Retail, Healthcare, LifeSciences, Communication/Media, Public Sector, Energy and Utilities verticals. Focusing on system integrators and alliance partners, which enable better reach to important senior-level decision makers and create compelling joint go-to-market opportunities. Expanding the reseller channel globally to reach new SMB markets. Increasing marketing spending to drive higher lead volume and continuing to improve core commercial processes to drive better lead conversion rates. Page 144 July 17, 2014

145 BMO Capital Markets Qlik Technologies In 2013, QlikView was licensed by customers such as American Apparel, Coca-Cola HBC AG Group of Companies, Connecticut Children s Medical Center, Danone SA, Dreyer Clinic, Fox Head, Harman International Industries, Hartford Healthcare Corp, Healthfirst, Humana Italia SPA, MedAmerica, Michelin Tyre Company, Mitsubishi Chemical, Omnicare, Red Hat, Sixt GmbH & Company, Swissphone Telecom AG, Taylor Wimpey PLC, TELUS International, Toys R Us, the U.S. Food and Drug Administration, Unum Group, Virgin Australia, WM Morrison Supermarkets Plc., and Waffle House. Qlik Is an International Company With Over 60% of Revenue Derived Outside the Americas Qlik generates more than 60% of revenue from outside the Americas and over 70% from outside the US. This stands in sharp contrast to most software companies, which when mature generate just north of 50% of revenue from outside the US. We believe Qlik s current geographic revenue distribution may be a harbinger of increasing competition. Competition in the data discovery market is more intense in the US, as opposed to in Europe where Qlik has gone relatively unchallenged. Tableau, arguably Qlik s largest competitor, has been demonstrating strong growth internationally, up 112% in FY2013 (and accelerating to 122% in the second half of the year). International make up only 20% of revenue, and the company cited international as one of the key themes for 2014 on its 4Q13 earnings call. Products currently support eight languages, and the majority of indirect sales internationally are through resellers. The company is aggressively expanding its direct and indirect sales internationally beyond its presence in Australia, Canada, France, Ireland, Japan (2012), Singapore (2012), and the United Kingdom. Qlik continues to increase its presence in North America, both directly and indirectly. It also plans to continue to seek to enter new international markets (Asia-Pacific, Latin America, Eastern Europe, Middle East, and Africa) by establishing distribution partnerships to drive sales. Market Backdrop The $15 billion dollar business intelligence market is going through a significant transition, driven by evolving business user requirements and enabled by advances in in-memory and data discovery/visualization technology. This is illustrated by Gartner s market share data, which shows traditional BI vendors commanding 64% of the market but growing by only 4.5%, below the market growth rate of 7.9%. In suit, the BI market is shifting from rearward-looking centralized reporting of the past to forward-looking decentralized nearly real-time predictive analysis. Underpinning this transition is the explosion of Big Data, which holds valuable insight for companies willing to invest in technologies to capture and analyze it, thereby forcing other companies to invest lest they lose their competitive edge. Legacy BI tools have not lived up to their promises, particularly around ROI, as consolidation in the space (IBM/Cognos, Oracle/Hyperion, SAP/BusinessObjects) has not reduced complexity and has in fact slowed innovation. A new breed of vendors, such as Tableau and Qlik, has commoditized traditional Page 145 July 17, 2014

146 BMO Capital Markets Qlik Technologies reporting/query tools. Importantly, the consumerization of BI technology is in some cases shifting the end user from IT analysts to business users, which in effect is expanding the market opportunity. While this new breed of data discovery vendors is out in front at the moment, incumbents are coming to market with competitive tools, and this will lead to an increasingly competitive battle for customer wallet share. Incumbents are attempting to stem customer losses by adding visualization tools to traditional offerings while data discovery vendors will broaden their data management capabilities to address traditional business analysts requirements, leading to a collision course as products and capabilities begin to converge With that said, we don t view the market as a zero-sum game for incumbents. Gartner anticipates that fewer than 25% of enterprises will fully replace their existing BI solutions. Large organizations will likely settle on multiple platforms, ranging from full enterprise BI suites to BI embedded into applications and lightweight desktop self-service BI tools for business users. Data discovery vendors are growing market share, although it is unclear whether the BI incumbents (organically or through acquisition) or the data discovery specialists will ultimately win out. Exhibit 9. Business Intelligence Market Share All Incumbents Losing Share Market Share Business Intelligence ($M) Revs '13 Share '12 Share '13 y/y growth Total $14, % CAGR 7.3% Top 3 share 50% 48% SAP $3, % 21.3% 5.3% Oracle $1, % 13.9% 2.1% IBM $1, % 12.7% 4.9% SAS $1, % 11.8% 6.0% Microsoft $1, % 9.6% 15.9% Qliktech $ % 3.0% 20.1% MicroStrategy $ % 2.9% 4.9% FICO $ % 2.6% 8.8% Tableau $ % 1.5% 80.5% Information Builders $ % 1.3% 0.3% Other vendors $2, % 19.4% 10.5% Gain Loss Source: Gartner Page 146 July 17, 2014

147 BMO Capital Markets Qlik Technologies The Shift to Data Discovery Data discovery is increasingly taking over as the next-generation BI architecture. Garter expects that by 2015, the majority of BI vendors will make data discovery their primary BI platform offering, shifting BI emphasis from reporting-centric to analysis-centric. As discussed in the previous sections, traditional BI reporting tools depend on extracting data from a data warehouse and have been largely confined to reporting yesterday s news in static reports or preconfigured dashboards. Most business users aren t exposed to this information and rely on IT for reporting. Moreover, implementing these systems takes months, and maintenance can cost three to five times the cost of a BI application. Data discovery tools offer an intuitive interface, which makes the application accessible to many more users, enabling them to explore data, conduct rapid prototyping, and create proprietary data structures to store and model data from disparate sources. Business users themselves, unskilled in traditional business intelligence and data analytics, are able to create, modify, mash up, and share their data, helping them to make better informed decisions. Based on reported results and our industry conversations, these data discovery deployments are beginning to move from small groups within companies to larger organizations and business units. Currently, IT is often a bottleneck for broader adoption due to internal politics and the lack of a cohesive data management initiative. This is perhaps the single biggest barrier to data discovery adoption and will help protect traditional BI players. For the future, no single vendor is addressing business user ease of use and IT-driven enterprise requirements, and as data discovery deployments grow and use cases become more complex, this will emphasize the need for governance. This is the impetus of QlikView Next, expected later this year. Over time, search-based data discovery will ultimately drive mainstream adoption of data discovery platforms, similar to the way the web browser brought about ubiquitous use of the Internet. Page 147 July 17, 2014

148 BMO Capital Markets Qlik Technologies Exhibit 10. Traditional BI Platforms vs. Data Discovery Platforms Traditional BI Platforms Data Discovery Platforms Key Buyers IT-driven Business-driven Main Sellers Approach Megavendors, large independents Top-down, IT-modeled (semantic layers), query existing repositories Fast-growing independents Bottom-up, business-usermapped (mashup), move data into dedicated repository User Interface Report/KPI dashboard/grid Visualization/interactive dashboard Use Case Monitoring, reporting Analysis Deployment Consultants Users Source: Gartner What s Enabling Data Discovery? The key underlying driver enabling the emergence of data discovery technology is advances in computing, specifically in the area of in-memory. Traditionally, OLAP systems accessed data stored on hard disk drives, which due to inherent limitations of electromechanical disks, experienced latency and delay. Queries typically could take hours. Storing or caching large amounts of data in-memory was cost prohibitive. However, the shift to 64-bit systems and the sustained reduction in memory prices have at last enabled the building of information systems that leverage memory versus disk in OLAP applications. As a result, analytical query times can be reduced from hours to minutes or even seconds. This shift to 64-bit systems has marked the inflection point for vendors like Qlik and Tableau, and in-memory is a core technology component of the SAP HANA and Oracle Exalytics vision. In-memory will be an important piece of the overall next-generation analytics space. Most solutions in the market max out at up to a billion rows of data and deal mostly with structure data. Machine-generated data creates billions of rows of data which necessitates other types of processors like parallelization and Hadoop. Business Intelligence Competitive Positioning Market share in BI continues to be concentrated, but sources of innovation are more diverse. According to Information Week, much of the activity in the BI market has been dominated by emerging BI vendors focusing on experimenting with open source technology, producing a diverse set of solutions, marking a trend away from standardization. In 2012, 30% of those Page 148 July 17, 2014

149 BMO Capital Markets Qlik Technologies surveyed had standardized on a small handful of BI tools, falling from 47% in This reversal has occurred despite massive consolidation in the BI space last decade (SAP/Business Objects, Oracle/Hyperion, and IBM/Cognos). Today, traditional BI vendors command about 64% of the market but grew on less than 5% on average, below the overall market growth rate of 8%. After consolidating the market, the large IT vendors were largely focused on integrating acquired BI solutions into their broader software and infrastructure product portfolio, which generally resulted in underinvestment. As a result, we believe these legacy BI tools are considered old and lacking in modern functionality. Emerging data discovery vendors, by contrast, have led with innovative solutions, which is rejuvenating the marketplace and leading to growth three times that of traditional BI platforms. In summary, the consumerization of IT is resulting in a shift in the use of and the buying of BI and related services away from IT and toward individual business users and managers. Data discovery vendors continue to organize their go-to-market strategies around this trend, and we expect traditional BI vendors to increasingly pivot away from IT to attack this new opportunity. This is consistent with Gartner s prediction that, by 2014, 40% of BI purchasing will be business-led rather than IT led. Exhibit 11. Tableau and Qlik Gaining Mindshare Source: BMO Capital Markets Research, Forrester Products from large vendors such as IBM, Oracle, Microsoft, and SAP are currently the most popular BI choices, with Tableau and Qlik ranking among the top five vendors mentioned in the BI-related inquiries at Forrester. This is consistent with data from Gartner that suggests that best of breed solutions are growing mindshare. Page 149 July 17, 2014

150 BMO Capital Markets Qlik Technologies Exhibit 12. Standardization on Mega Vendors Is Dropping With Most Survey Respondents Preferring to Adopt Best of Breed Source: BMO Capital Markets Research, Gartner We believe traditional BI applications from incumbent vendors will continue to lose share as standard reporting becomes commoditized and is embedded across the application stack. As noted, in-memory and new data processing frameworks are leading to new application architectures, allowing vendors to offer solutions that let users embed analytics deeper into the business process. Incumbents are responding by focusing on the benefits of offering an end-toend stack while leveraging their massive installed bases, as evidenced by SAP s HANA strategy, Oracle s embedding of analytics within its Fusion Applications, and Microsoft s positioning of SharePoint as a delivery mechanism for BI. SaaS vendors like Salesforce.com, Workday, and Demandware are in the early stages of creating Big Data solutions that incorporate third-party data into their own cloud-based data and delivery business insights. Data discovery is one of the main disruptors of the incumbent vendors market dominance in the near to medium term, although IT concerns over duplicate data sets remains a potential roadblock. We have heard customers express reluctance around the perception of having to add yet another silo on top of existing systems, since newer data discovery tools often create another copy of corporate data in the process of performing analytics. The main three data discovery vendors QlikTech, Tableau, and Tibco Spotfire control about 75% of the $1 billion Page 150 July 17, 2014

151 BMO Capital Markets Qlik Technologies market; however, the data discovery market accounts for only about 8% of the total BI market. These vendors are moving beyond their original target business user customer and are attacking the enterprise more broadly by adding features that challenge larger vendors in terms of scalability, security, rich metadata, internationalization, and application programming interfaces (APIs), among other industrial-strength enterprise features. The key to the evolution of these vendors is how they approach and manage data governance and other issues sensitive to enterprise buyers. While the three vendors all offer data discovery tools with strong visualization capabilities, they differ in important ways. Qlik Qlik s products are targeted toward business users with strong visualization attributes. Qlik s solutions are based on proprietary systems that extract data from existing data warehouses into in-memory systems. Qlik offers a data discovery option to connect directly to other data sources. The company is at the front end of a major product cycle with the release of QlikView11(QV.11), which is expected to be the primary driver of business in 2014 prior to the full release of QlikView.Next (QV.N) later this year. Adoption for the move to QV.N is expected to be gradual with some users staying on QV11, some upgrading, and others choosing QV.N for net new transformational use cases or users. We believe QV.N is a transformative release from a product and a consumption perspective that should expand the company s addressable market by bridging the gap between data discovery and complex BI platforms. The core of QV.11 and prior versions is its data discovery capabilities; however, with QV.N designed to give users the immediate insights while providing IT professionals the enterprise manageability and governance they require. Further, the release of the QV.N API extends use cases of the core QV application to very specific guided analytic experiences. Uneven execution remains the overarching issue at Qlik; however we re positive on the solution. Tibco Tibco Spotfire is geared more toward the high end of the market. Like Qlik, Spotfire leverages in-memory and uses connectors to hook into existing data warehouses. Tibco has had execution issues with Spotfire and has been losing share. We hear positive anecdotes on the product s ease of setup and use. Tibco is released version 6.5 of Spotfire in 2Q, which includes a personal desktop addition, new simple-to-use mapping and staff capabilities, expanded data connectivity, especially for big data sources like Hadoop clusters, and enhanced usability features. Tibco also recently acquired Jaspersoft, which is strong in embedded BI applications. Tableau Tableau has a distinct architectural approach with its Visual Query Language (VizQL) Live Query Engine, which allows users to instantaneously connect to large volumes of data in in existing databases leverage investments in existing data platforms. Direct query access has been a strength of the platform since the product's inception. To complement Tableau s Live Query Engine, its Hybrid Data Architecture In-Memory Data Engine enables users to import large amounts of data into its in-memory database for customers that want to analyze data that is not already captured in existing databases (such as text files, spreadsheets, and logs) for those seeking the performance capabilities offered through in-memory. The company released its 8.1 release in 4Q13, version 8.2 was released in 2Q14, and Tableau 9.0 is expected to be released in 1H15. The key feature in the 8.2 release is Mac support. Page 151 July 17, 2014

152 BMO Capital Markets Qlik Technologies Established vendors are attempting to add data discovery features onto existing offerings through organic development or acquisition. Capabilities are thus far in general weaker than those offered by pure-play vendors, but this may be sufficient to protect their existing installed base. IBM IBM offers a complete range of enterprise-grade BI, performance management and advanced analytics platform capabilities, complemented by a deep services organization. The IBM Cognos BI platform handles some of the largest deployments in the world, and in 2013, IBM significantly simplified the Cognos licensing model, reducing 28 SKUs to only two. IBM offers data discovery through its IBM Cognos Insight product. IBM has been acquiring domain specific experience in packaged analytic applications including Algorithmics (credit and operations risk analytics), DemandTec (merchandising analytics), Emptoris (spend analytics), and Varicent (sales performance management). Infor Infor Business Intelligence is part of an end-to-end platform that encompasses BI and performance management offerings, both based on the MIS Alea product (also called MIS DecisionWare) acquired from Systems Union in Infor is investing significantly to enhance the attractiveness of its platform both inside and outside Infor's ERP installed base. Infor BI is used primarily for reporting. Infor BI 10x includes an in-memory multidimensional OLAP database. Other offerings include Web frontends, such as Infor BI Dashboards/Motion Dashboards for data presentation and analysis, a feature-rich Microsoft Excel-based interface, a data integration tool, and a modeling tool. Infor BI Planning and Infor BI Consolidation offer data modeling and reporting, in support of a standard planning and financial consolidation process for a diversity of industries and domains. Infor Intelligent Open Network (ION), Business Vault and Workspace integrate BI content for Infor and non-infor ERP customers. Infor BI is a consideration by organizations looking for an integrated BI and performance management solution. Microsoft Microsoft has done a good job delivering a combination of business user capabilities with an enterprise-capable platform. The Microsoft data platform takes advantage of Office, Azure, and SQL Server. For data discovery, Microsoft hopes to tap its 1 billion Office users and leverage enterprise licensing agreements to drive competitive pricing. Microsoft offers Power BI for Office 365, a cloud-based, self-service business intelligence solution with natural language capability and Power Query for Excel, which makes it easier for generalists to perform data discovery. The Azure HDInsight for elastic Hadoop is a managed cloud-based Hadoop service through a partnership with Hortonworks. This integrates with Power BI and Excel. The Microsoft Azure Intelligent Systems Service will help customers embrace the Internet of Things and securely connect to, manage and capture machine-generated data from sensors and devices, regardless of operating system. SQL Server 2014 is the first time in-memory is built into every SQL workload, enabling non-oltp to take advantage of in-memory without changing workloads. The Analytics Platform System (APS) combines the best of Microsoft s SQL database and Hadoop technology in one low-cost offering, leveraging partnerships with Dell and HP. PolyBase brings structured and unstructured data together in a data warehouse appliance. MicroStrategy MicroStrategy remains an industry benchmark in large BI deployments running on top of large enterprise data warehouses, and it is often viewed as a go-to vendor Page 152 July 17, 2014

153 BMO Capital Markets Qlik Technologies when enterprise requirements are complex. Functionality is one of the main reasons for selecting MicroStrategy; however, the time to create reports is among the longest and MicroStrategy lacks user-friendly and comprehensive advanced analytic capabilities, as well as unstructured data support. In late 2013, MicroStrategy released an updated version of its visual data discovery engine. A scalable multiterabyte in-memory engine is also being developed in cooperation with Facebook, for release in This could differentiate MicroStrategy from direct competitors in the data discovery market. Microstrategy has done a good job releasing cloud products and driving adoption in its installed base Oracle Oracle s BI and analytics technologies include Oracle BI, Oracle Exalytics In- Memory Machine, and Oracle Endeca Information Discovery. Customers choose Oracle for its stack integration, and it offers more than 80 prebuilt analytic applications for Oracle E- Business Suite, PeopleSoft, JD Edwards, Siebel and other enterprise applications, including industry-specific packaged analytic applications. Most Oracle BI deployments support traditional BI reporting and dashboarding. Oracle purchased Endeca to add search-based data discovery. Endeca has good functionality, but integration and growth have slowed since the acquisition, and the solution now lags pure-play competitors. Oracle BI on Exalytics uses the Oracle TimesTen In-Memory RDBMS for performance optimization. Oracle plays from a position of strength as a result of its dominance in database and applications, but execution around data integration has been uneven. SAP SAP customers use SAP BusinessObjects BI primarily for reporting. SAP has been investing heavily in SAP Lumira to establish a presence in the data discovery market. SAP is used to embed BI content, but is not widely used by customers to embed advanced analytic content, although the introduction of HANA is designed to change this. The company has had issues integrating BusinessObjects with legacy solutions like Business Warehouse Accelerator (BWA). Classic BusinessObjects is now being deemphasized as a front end for BWA. BusinessObjects is being bundled with HANA in an effort to drive the BWA installed base toward HANA adoption. SAP is positioning HANA as the strategic database and in-memory platform that will enable much of the advanced analytics functionality delivered by Lumira, Predictive Analysis, and KXEN. SAS SAS tools have been used primarily by power users, data scientists, and IT-centric BI developers. It s considered an expensive system that takes time to deliver reports, but has strong roots in advanced analytics. In 2012, SAS released Visual Analytics, a business user-oriented data discovery and BI platform targeted at less technical and analytically sophisticated users. The product is strong but is only relevant for SAS shops. The product uses SAS's LASR Analytic Server, an in-memory server for large-scale data analysis. Importantly, SAS has made the strategic decision to make Visual Analytics its "go forward" BI platform and the front-end reporting and analysis tool for all its analytic applications, which should unify SAS front-end tools, defend its installed base against stand-alone data discovery vendors and address both business user and business analysts. Page 153 July 17, 2014

154 BMO Capital Markets Qlik Technologies What About Cloud BI? We expect cloud BI to emerge as a growing trend in the years to come. To date, cloud-based delivery has not been a popular option, representing less than 5% of overall BI spending today. However, the market is growing - it was up by 42% in and we expect this trend to continue. The primary driver for increasing cloud deployment is that the percentage of data creation occurring off-premise, beyond the firewall, is growing. As this broad secular trend continues, data gravity will pull more workloads, services, and applications, including BI, into the cloud where the data is created and residing. Trust continues to be a hurdle to cloud adoption, but there are indications that this is changing. Forty-five percent of Gartner s recent Magic Quadrant survey respondents noted a willingness to put mission-critical BI in the cloud, compared with 33% in As with most applications, the promise of increased collaboration with customers and partners and mobility are drivers of Cloud BI. Over time, we expect the proliferation of connected devices (the Internet of Things ) and continued growth in cloud services (SaaS, PaaS, IaaS) will create more cloud based data and drive the adoption of cloud BI solutions. Cloud Business Intelligence vendors including Birst, GoodData, Looker, Domo, Adaptive Insights have all garnered significant amounts of venture funding. GoodData s focus on building an end-to-end system from visual interface to customer data management to perform analytics-as-a-service is gaining good market traction. GoodData sees the combination of Tableau and Amazon RedShift in competitive situations. As IT becomes more heterogeneous we expect platforms like this to become more pervasive and help drive cloud adoption. Established vendors such as MicroStrategy, Tableau, and Tibco/Jaspersoft also offer cloud BI products. Balance Sheet and Capital Allocation The balance sheet remains solid, with net cash and investments of $253 million, or $2.84 per share as of March 31, We expect FCF of $17.9 million in 2014 and $33.2 million in Current Outlook 2014 revenue/eps guidance of $ million/$ implies about 16%-18% top-line growth. We sit at the low end of guidance and slightly below consensus. Seasonality is skewed steeper in the 2H, and as noted, we are cautious on the impact of QlikView.Next release. Page 154 July 17, 2014

155 BMO Capital Markets Qlik Technologies Exhibit 14.DCF Analysis Exhibit 13. BMO vs. Consensus 2Q 2014E 2014E 2015E ($M, except EPS) New Guidance Street New Guidance Street New Street Revenues $125 $124 $128 $125 $545 $545 $555 $548 $630 $639 y/y 15.8% 16.0% 15.9% 16.4% 15.5% 16.6% EBIT $3.8 ($5.0) to ($2.0) $3.8 $32.3 $30 to $35 $32.6 $48.8 $51.0 margin 3.0% 3.0% 5.9% 6.0% 7.7% 8.0% EPS $0.03 ($0.04) to $0.02) $0.03 $0.24 $0.23 to $0.27 $0.25 $0.35 $0.39 CFO $3 $32 $33 $50 $61 Source: BMO Capital Markets Research, Thomson Reuters. Valuation Our 10-year DCF analysis makes the following key assumptions, resulting in our target price of $22, in line with current levels: Revenue CAGR of 14%; EBIT margin slowly increasing to 17.5%; 11.0% WACC; and 17x terminal cash flow multiple. DCF Analysis ($in millions, except per share) FY2014E FY2015E FY2016E FY2017E FY2018E FY2019E FY2020E FY2021E FY2022E FY2023E TV CAGR Revenues $545 $630 $725 $830 $947 $1,075 $1,216 $1,368 $1,533 $1,710 $1,686 14% Growth 15.9% 15.5% 15.0% 14.5% 14.0% 13.5% 13.0% 12.5% 12.0% 11.5% 10.0% EBIT $32 $49 $65 $85 $109 $134 $164 $198 $238 $282 $295 EBIT Margin 5.9% 7.7% 9.0% 10.2% 11.5% 12.5% 13.5% 14.5% 15.5% 16.5% 17.5% Tax Rate 30% 30% 30% 30% 30% 30% 30% 30% 30% 30% 30% Taxed EBIT $23 $34 $46 $60 $76 $94 $115 $139 $166 $197 $207 27% Depreciation $12 $14 $17 $19 $22 $25 $28 $31 $35 $39 $38 CapEx ($15) ($18) ($19) ($22) ($25) ($29) ($31) ($35) ($40) ($44) ($43) Change in Working Capital $9 $2 $7 $8 $9 $11 $12 $14 $15 $17 $17 Free Cash Flow $28 $33 $50 $65 $82 $101 $123 $148 $177 $209 $218 25% Growth 51% 29% 27% 23% 23% 20% 19% 18% 4% Discounted FCF $26 $27 $37 $43 $49 $54 $59 $64 $69 $74 Cumulative cash flow $501 30% Tax Rate 30.0% Terminal Value $1,178 70% WACC 11.0% Total DCF value $1,679 Cash Flow 17x Debt $0 Multiple Cash $253 $2.84 Market Value of Equity $1,932 Shares Outstanding 89 Share Price $21.66 Current Price $21.28 upside/(downside) 2% Source: BMO Capital Markets Research, company documents. Our $22 price target is based on 2.7x our 2015 EV/sales estimate, roughly in line with multiples for similar growth data infrastructure peers. Page 155 July 17, 2014

156 BMO Capital Markets Qlik Technologies Exhibit 15. Peer Analysis (in Millions, Except Per Share Data) Recent Cash/ EV/Sales Revenue Growth Ticker Rating Price EV Share FY1E FY2E FY0-'FY1E FY1E-'FY2E Qlik Technologies Inc QLIK MP $21.28 $1,645 $ x 2.6x 15.9% 15.5% vs Data Infrastructure -29% -24% vs Data Infrastructure <15% growth 15% 9% Software: Data Infrastructure Commvault Systems Inc CVLT OP $48.59 $1,918 $ x 2.5x 15.0% 14.1% Datawatch Corp DWCH NC $13.29 $73 $ x 1.6x 19.7% 25.1% Tableau Software Inc DATA OP $61.00 $3,254 $ x 6.7x 53.7% 35.8% Informatica Corp INFA NC $33.59 $3,028 $ x 2.6x 11.9% 11.9% Microstrategy Inc MSTR NC $ $1,211 $ x 1.9x 6.2% 7.0% Splunk Inc SPLK MP $46.54 $4,561 $ x 8.4x 35.8% 32.9% Teradata Corp TDC MP $41.78 $6,040 $ x 2.1x 3.4% 5.5% Tibco Software Inc TIBX NC $19.06 $3,147 $ x 2.7x 2.5% 6.3% Varonis Systems Inc VRNS NC $22.23 $419 $ x 3.4x 30.8% 26.4% Verint Systems Inc VRNT NC $47.96 $3,423 -$ x 2.8x 25.4% 9.0% Average 4.2x 3.4x 20.4% 17.4% Software: Data Infrastructure <15% growth Commvault Systems Inc CVLT OP $48.59 $1,918 $ x 2.5x 15.0% 14.1% Informatica Corp INFA NC $33.59 $3,028 $ x 2.6x 11.9% 11.9% Microstrategy Inc MSTR NC $ $1,211 $ x 1.9x 6.2% 7.0% Teradata Corp TDC MP $41.78 $6,040 $ x 2.1x 3.4% 5.5% Tibco Software Inc TIBX NC $19.06 $3,147 $ x 2.7x 2.5% 6.3% Verint Systems Inc VRNT NC $47.96 $3,423 -$ x 2.8x 25.4% 9.0% Average 2.6x 2.4x 10.8% 9.0% (Fishbein: CVLT, DATA QLIK, SPLK) (Bachman: TDC) (NC = Not covered. Thomson data for not covered companies) (OP= Outperform)(MP = Marketperform) *Estimates reflect latest complete and forward fiscal years Source: BMO Capital Markets Research, Thomson Reuters. Stock prices as of the close on July 16, Risks Competition. Qlik competes in an intense and increasingly competitive market with rapidly changing technology and evolving standards. Its primary competitors are established enterprise vendors with significantly more resources. Future success will depend in large part on its ability to penetrate the existing market for business analytics software, as well as the continued growth and expansion of the emerging market for analytics solutions for business users. Narrow product focus. Qlik currently derives and expects to continue to derive substantially all of revenues from its QlikView product line. Thus, continued growth in market demand for this product is critical to continued success. Channel distribution focus. For the year ended December 31, 2013, transactions by indirect channel partners accounted for more than 50% of total product licenses and first-year maintenance billings. The company expects to continue to rely substantially on its channel partners in the future, and maintaining successful relationships and generating revenue from current and future channel partners will directly affect growth. Page 156 July 17, 2014

157 BMO Capital Markets Qlik Technologies International concentration. Qlik generates more than 60% of revenue from outside the Americas and over 70% from outside the US. This stands in sharp contrast to most software companies which at maturity generate just north of 50% of revenue from outside the US. We view this as a potential challenge given the increased competition in the US and Qlik has been relatively unchallenged outside the US touting its next generation in-memory Business Discovery platform. Other risks include R&D efficiency, perpetual license business model, data privacy regulations, hosting failures, security breaches, potential acquisition integration, and litigation. Page 157 July 17, 2014

158 BMO Capital Markets Qlik Technologies Financial Models Exhibit 16. Income Statement QlikTech Income Statement FY2012 FY2013 FY2013 FY2014E FY2014E FY2015E ($ in millions except per share) FY2012 Q1-Mar-13 Q2-June-13 Q3-Sept-13 Q4-Dec-13 FY2013 Q1-Mar-14 Q2-June-14E Q3-Sept-14E Q4-Dec-14E FY2014E FY2015E License revenue $ $ $ $ $ $ $ $ $ $ $ $ Maintenance revenue $ $ $ $ $ $ $ $ $ $ $ $ Professional services revenue $ $ $ $ $ $ $ $ $ $ $ $ Total Revenue $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: license $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: maintenance $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: professional services $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Gross Profit $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Expenses Sales & Marketing $ $ $ $ $ $ $ $ R&D $ $ $ $ General & Administrative $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ Total Non-GAAP Operating Expenses $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Income $ $ (10.214) $ (1.655) $ $ $ $ (14.487) $ (3.759) $ $ $ $ (+) Depreciation $ $ $ Non-GAAP EBITDA $ $ (8.495) $ $ $ $ $ $ $ $ $ $ $ $ $ $ (11.918) $ (0.983) $ $ $ $ Other income $ (2.891) $ (1.451) $ (0.469) $ $ (0.666) (2.522) $ $ (0.328) $ (0.328) $ (0.328) (0.328) $ $ (1.312) $ (1.200) Non-GAAP Earnings Bef.Taxes $ $ (11.665) $ (2.124) $ $ $ $ (14.815) $ (4.087) $ $ $ $ Provision for Income Taxes $ $ (3.499) $ (0.638) $ $ $ $ (4.445) $ (1.226) $ $ $ $ Non-GAAP Tax Rate 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% Loss atributable to non-controlling interest $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - Non-GAAP Net Income (1) $ $ (8.166) $ (1.486) $ $ $ $ (10.370) $ (2.861) $ $ $ $ Non-GAAP EPS $ 0.27 $ (0.09) $ (0.02) $ 0.05 $ 0.31 $ 0.27 $ (0.12) $ (0.03) $ 0.04 $ 0.33 $ 0.24 $ 0.35 Avg. Diluted Shares Outstanding $ $ $ $ $ $ $ $ $ $ $ $ (1) Non-GAAP excludes: amortization, restructuring, impairments, settlements, and stock-based comp. Expense Analysis (non-gaap): Cost of Revenues 10.6% 14.2% 12.9% 13.5% 10.7% 12.5% 15.7% 13.3% 14.0% 11.0% 13.2% 13.3% Sales & Marketing 53.6% 62.4% 58.9% 53.2% 44.7% 53.5% 64.8% 61.4% 54.4% 46.0% 55.2% 53.7% R&D 7.5% 13.0% 11.9% 9.6% 7.2% 10.0% 11.6% 10.6% 9.9% 7.5% 9.6% 9.5% General & Administrative 18.8% 21.0% 17.9% 17.2% 11.8% 16.3% 20.9% 17.7% 16.9% 11.6% 16.1% 15.8% Depreciation 1.4% 1.8% 1.8% 2.1% 1.4% 1.7% 2.3% 2.2% 2.5% 1.8% 2.2% 2.3% Margin Analysis (non-gaap): Product gross margin 97.9% 96.9% 97.5% 97.3% 97.4% 97.3% 97.2% 97.5% 97.3% 97.4% 97.4% 97.4% Maintenance gross margin 93.0% 92.0% 93.4% 94.0% 94.1% 93.4% 93.3% 93.4% 94.0% 94.1% 93.7% 93.7% Pro-serv gross margin 5.3% -12.8% -8.2% -13.7% 8.5% -4.9% -13.5% -8.2% -13.7% 8.5% -5.3% -5.5% Gross Margin 89.4% 85.8% 87.1% 86.5% 89.3% 87.5% 84.3% 86.7% 86.0% 89.0% 86.8% 86.7% Operating Margin (total) 9.4% -10.6% -1.5% 6.5% 25.5% 7.7% -13.0% -3.0% 4.7% 24.0% 5.9% 7.7% EBITDA Margin 10.8% (8.8%).3% 8.7% 27.0% 9.5% (10.7%) (.8%) 7.3% 25.8% 8.1% 10.0% Tax Rate 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% 30.0% Net Margin 6.1% (8.5%) (1.4%) 4.6% 17.6% 5.0% (9.3%) (2.3%) 3.1% 16.6% 4.0% 5.3% Sequential Growth Rates (non-gaap): Total Revenue (29.8%) 11.9% (3.6%) 55.4% (31.3%) 12.6% (2.3%) 53.0% Gross Profit (33.4%) 13.7% (4.3%) 60.4% (35.2%) 15.8% (3.0%) 58.2% Operating Margin % (83.8%) (510.0%) 508.9% % (74.0%) (254.4%) 671.5% Net Income (136.0%) (81.8%) (422.7%) 493.5% (136.4%) (72.4%) (234.0%) 711.7% Year-Over-Year Growth (non-gaap): Total Revenue 21.2% 22.0% 25.9% 20.9% 17.7% 21.1% 15.1% 15.8% 17.4% 15.5% 15.9% 15.5% Gross Profit 21.0% 18.2% 23.0% 18.0% 16.2% 18.5% 13.1% 15.2% 16.7% 15.1% 15.1% 15.4% Sales & Marketing 17.1% 21.4% 26.0% 17.5% 18.9% 20.9% 19.4% 20.8% 20.2% 18.8% 19.7% 12.2% R&D 52.4% 127.6% 135.7% 45.5% 2.2% 60.8% 3.4% 3.6% 20.4% 19.6% 11.1% 14.5% Operating expenses 23.1% 28.6% 29.1% 15.5% 12.2% 20.8% 16.2% 17.1% 19.2% 17.8% 17.6% 12.8% Operating Income 5.4% 343.1% (170.4%) 59.1% 27.9% (1.1%) 41.8% 127.2% (14.5%) 8.4% (10.8%) 51.0% Net Income 2.3% 215.4% (186.1%) 164.6% 25.6% (.1%) 27.0% 92.5% (20.1%) 9.4% (8.0%) 53.5% EPS (.1%) 208.5% (187.0%) 158.2% 22.5% (1.2%) 23.2% 84.7% (22.3%) 4.1% (11.4%) 47.9% Source: BMO Capital Markets Research, company documents. Page 158 July 17, 2014

159 BMO Capital Markets Qlik Technologies Exhibit 17. Balance Sheet ($ in millions) Q4-Dec-12 Q1-Mar-13 Q2-June-13 Q3-Sept-13 Q4-Dec-13 Q1-Mar-14 Q2-June-14E Q3-Sept-14E Q4-Dec-14E Q4-Dec-15E Assets FY2012 FY2013 FY2014E Cash & Cash Equivalents $ $ $ $ $ $ $ $ $ $ Accounts Receivable $ $ $ $ $ $ $ $ $ $ Prepaid expenses $ $ $ $ $ $ $ $ $ $ Income tax receivable $ - $ $ $ $ - $ $ $ $ $ Deferred tax assets $ $ $ $ $ $ $ $ $ $ FY2015E Total Current Assets $ $ $ $ $ $ $ $ $ $ PP&E, net $ $ $ $ $ $ $ $ $ $ Intangible assets, net $ $ $ $ $ $ $ $ $ $ Goodwill $ $ $ $ $ $ $ $ $ $ Deferred income taxes $ $ $ - $ - $ $ $ $ $ $ Deposits and other non-current assets $ $ $ $ $ $ $ $ $ $ Total Assets $ $ $ $ $ $ $ $ $ $ Liabilities Line of credit $ - $ $ - $ - $ - $ - $ - $ - $ - $ - Income taxes payable $ $ - $ - $ - $ $ - $ - $ - $ - $ - Accounts payable $ $ $ $ $ $ $ $ $ $ Deferred revenues $ $ $ $ $ $ $ $ $ $ Accrued comp $ $ $ $ $ $ $ $ $ $ Accrued other $ $ $ $ $ $ $ $ $ $ Deferred income taxes $ $ $ $ $ $ $ $ $ $ Total Current Liabilities $ $ $ $ $ $ $ $ $ $ LT Deferred revenues $ $ $ $ $ $ $ $ $ $ Deferred income taxes $ $ $ $ $ $ $ $ $ $ Other long-term liabilities $ $ $ $ $ $ $ $ $ $ Total Liabilities $ $ $ $ $ $ $ $ $ $ Stockholders' equity $ $ $ $ $ $ $ $ $ $ Total Liabilities + Stockholder's Equity $ $ $ $ $ $ $ $ $ $ Balance Sheet Summary Current Ratio Book Value Per Share $2.56 $2.61 $2.66 $2.83 $3.01 $2.96 $2.87 $2.85 $3.11 $3.39 Cash Per Share $2.22 $2.48 $2.56 $2.60 $2.52 $2.84 $2.78 $2.73 $2.68 $2.95 Return On Equity 11.5% 8.5% 6.7% 7.6% 9.6% 8.4% 7.6% 7.2% 8.0% 11.2% Return on Assets 6.9% 5.1% 4.0% 4.5% 5.7% 4.9% 4.4% 4.1% 4.6% 6.2% Working Capital, net ($30) ($22) ($28) ($25) ($36) ($27) ($24) ($24) ($44) ($53) Avg. Diluted Shares Outstanding Model Assumptions DSO (excluding deferred revenue) DSO (billings) Accounts Payable Days (off COGS) Accrued Expenses (as % of Sales) 27% 33% 31% 33% 29% 36% 30% 30% 30% 30% Source: BMO Capital Markets Research, company documents. Page 159 July 17, 2014

160 BMO Capital Markets Qlik Technologies Exhibit 18. Cash Flow Statement FY2012 FY2013 FY2013 FY2014E FY2014E FY2015E ($ in millions) FY2012 Q1-Mar-13 Q2-June-13 Q3-Sept-13 Q4-Dec-13 FY2013 Q1-Mar-14 Q2-June-14E Q3-Sept-14E Q4-Dec-14E FY2014E FY2015E Operating Activities non-gaap non-gaap non-gaap non-gaap non-gaap Net income $ $ (13.206) $ (8.049) $ $ $ (9.979) $ (25.880) $ (2.861) $ $ $ $ epreciation and amortization $ $ $ $ $ $ $ $ $ $ $ $ D Stock-based comp $ $ $ $ $ $ $ $ $ - eferred income taxes $ $ - $ - $ - $ - $ - $ - D Excess tax benefit from stock options $ (4.789) $ (4.181) $ (2.290) $ $ $ (4.047) $ (3.445) $ (3.445) $ - Other $ $ $ $ $ (9.267) $ $ $ $ - N et change in assets and liabilitites, excl. acquisitions $ $ $ (17.581) $ (5.693) $ Accounts receivable, net $ (32.307) $ $ $ $ $ (70.994) $ (22.780) $ (36.972) Prepaid expenses $ (4.098) $ (1.973) $ (3.542) $ - $ - $ - $ (3.542) $ - Income taxes $ $ (15.684) $ (3.283) $ (3.283) $ - Deferred revenue $ $ $ $ (1.248) $ (4.561) $ $ $ Accounts payable $ $ (5.507) $ (4.327) $ (0.598) $ $ $ (3.771) $ Source: BMO Capital Markets Research, company documents. Accrued comp $ - $ (2.008) $ (0.868) $ $ $ et Cash from Operations $ $ $ $ $ $ $ $ $ $ $ $ N nvesting Activities Purchases of ST investments $ - $ - $ - $ - $ - $ - $ - $ - Proceeds from sales of marketable securities $ - $ - $ - $ - $ - $ - $ - $ - apital Expenditures $ (10.792) $ (2.762) $ (1.754) $ (2.648) $ (6.187) $ (13.351) $ (3.407) $ (4.000) $ (4.000) $ (4.000) $ (15.407) $ (17.500) Acquisitions $ (10.334) $ (4.371) $ - $ (5.268) $ (9.639) $ - $ - $ - hange in restricted cash $ - $ - $ - $ - $ - $ - $ - $ - et Cash from Investing $ (21.126) $ (2.762) $ (6.125) $ (2.648) $ (11.455) $ (22.990) $ (3.407) $ (4.000) $ (4.000) $ (4.000) $ (15.407) $ (17.500) inancing Activities PO proceeds (net) $ - $ - $ - $ - $ - $ - Proceeds from exercise of stock options $ $ $ $ $ $ $ $ $ - Excess tax benefit from stock options $ $ $ $ (0.705) $ (1.719) $ $ $ $ - epayment of convert $ - $ - $ - $ - $ - $ - $ - $ - Proceeds from preferred $ - $ - $ - $ - $ - $ - Proceeds from restricted stock $ - $ - $ - $ - $ - $ - $ - $ - Credit facility $ - $ - $ - $ - $ - $ - $ - $ - I C C N F I R Credit facility repayment $ (0.558) $ $ (0.402) $ - $ (1.237) $ (1.457) $ - $ - $ - Other $ - $ - $ - $ - $ - $ - $ - $ - Net Cash from Financings $ $ $ $ $ (1.043) $ $ $ - $ - $ - $ $ - Foreign Currency Impact $ $ (1.279) $ (2.137) $ $ (0.072) $ (0.567) $ $ $ - Net Increase / Decrease in Cash $ $ $ $ $ (7.461) $ $ $ (0.732) $ $ $ $ FCFE $ $ $ $ (1.878) $ (1.078) $ $ $ (0.732) $ $ $ $ y-o-y 89% -38% 65% -69% 22% -3% 34% -110% -157% -164% 4% 90% FCFF $ $ $ $ (1.923) $ (0.612) $ $ $ (0.503) $ $ $ $ y-o-y 100% -36% 75% -61% -34% -4% 25% -107% -167% -250% -1% 85% FCFF/Share $0.22 $0.15 $0.09 -$0.02 -$0.01 $0.20 $0.18 -$0.01 $0.01 $0.01 $0.19 $0.35 Page 160 July 17, 2014

161 Splunk (SPLK-NASDAQ) Stock Rating: Market Perform Industry Rating: Market Perform Initiating Coverage With Market Perform and $51 Price Target Investment Thesis Splunk is in the midst of transitioning from being a pioneer in the nascent Operational Intelligence market into becoming a broader data analytics platform provider. Splunk faces a substantial market opportunity and has differentiated technology; however, the company is relatively small and we believe distribution and scale are significant constraints. The company needs to continue to invest in specialty overlay sales capacity to address new pockets of spending within organizations and must align product pricing to customer capacity growth. Near term we are concerned that the shift from transactional to more enterprise agreements could weigh on sales cycles until a repeatable pricing model is developed. Slower-than-expected growth likely keeps shares range bound despite our view that competition concerns are overblown and the market opportunity is significant. Forecasts & Valuation We view FY2015 as a transitional year. Consensus estimates appear attainable but we don t see material upside, and with shares trading at 8.4x FY2016 sales the setup is not favorable, in our view. Our FY2015/16 top-line estimates are in line with consensus. Near-term 2Q estimates appear beatable ($93.7 million, 12% q/q vs. 19% q/q in FY2014 and 24% q/q in FY2013), and we will closely monitor deferred revenue ($207.1 million, 71% y/y vs. 81% y/y in 1Q15) and billings ($106.8 million, 22% q/q vs. 29% q/q in FY2014 and 21% q/q in FY2013) growth as indicators of further enterprise traction. Recommendation We are initiating coverage of Splunk with a Market Perform rating and a $51 price target, which is based on both our DCF and comparative multiple analyses and equates to 9.4x our 2016 EV/sales estimate. We believe a premium valuation is reasonable, given Splunk s leadership in the nascent operational intelligence market. July 17, 2014 Joel P. Fishbein, Jr BMO Capital Markets Corp. [email protected] Brett Fodero / Edward Parker BMO Capital Markets Corp / [email protected] / [email protected] Price (16-Jul) $ Week High $ Target Price $ Week Low $39.35 Price: High,Low,Close(US$) Splunk Inc (SPLK) Volume (mln) SPLK Relative to S&P 500 Earnings/Share(US$) Last Data Point: July 15, 2014 (FY-Jan.) 2013A 2014A 2015E 2016E EPS - $ $0.03 $0.00 $0.08 P/E na nm CFPS $0.47 $0.60 $0.58 $0.76 P/CFPS 80.2x 61.2x Rev. ($mm) $199 $303 $411 $546 EV/Rev. 23.6x 15.5x 11.4x 8.6x EBITDA ($mm) $3 $5 $11 $24 EV/EBITDA nm nm nm nm Quarterly EPS Q1 Q2 Q3 Q4 2013A -$0.04 -$0.01 -$0.01 $ A -$0.06 -$0.01 $0.00 $ E -$0.04a -$0.02 $0.01 $0.04 Dividend $0.00 Yield 0.0% Book Value $6.68 Price/Book 7.0x Shares O/S (mm) Mkt. Cap (mm) $5,459 Float O/S (mm) Float Cap (mm) $5,307 Wkly Vol (000s) 10,639 Wkly $ Vol (mm) $650.4 Net Debt ($mm) -$897 Next Rep. Date na Notes: All values in US$ First Call Mean Estimates: SPLUNK INC (US$) 2015E: $0.00; 2016E: $ Page 161 July 17, 2014

162 BMO Capital Markets Splunk Details & Analysis Splunk is in the midst of transitioning from being a pioneer in the nascent Operational Intelligence market into becoming a broader data analytics platform provider. Splunk faces a substantial market opportunity and has differentiated technology; however, the company is relatively small, and we believe distribution and scale are significant constraints. The company needs to continue to invest in specialty overlay sales capacity to address new pockets of spending within organizations and must align product pricing to customer capacity growth. Near term we are concerned that the shift from transactional to more enterprise agreements could weigh on sales cycles until a repeatable pricing model is developed. Slower-thanexpected growth likely keeps shares range bound despite our view that competition concerns are overblown and the market opportunity is significant. FY2015 is a transitional year for the company. Can the salesforce scale fast enough? Significant market opportunity Weighing the competitive landscape Capacity-based licensing model and platform strategy should support premium multiple FY2015 Is a Transitional Year for the Company We view FY2015 as a transitional year for the company. We see the potential for another leg of growth if the company can navigate several challenges around capacity expansion, product expansion, and adoption pricing, all of which are in a natural transition as the company scales. If successful in scaling the business along these three dimensions, Splunk should be able to access new pockets of spending within enterprise customers. Management has experience with scale, and we are positively inclined regarding the company s outlook. Capacity expansion. Sales capacity growth is the biggest driver of the business. The capacity to hire not only quality reps but also sales engineering talent is the most significant constraint on growth. 1Q15 was a strong hiring quarter, and more field capacity is expected to be added in FY2015 than in FY2014. The company is adding market segment focused personnel and is building a team of overlay sales engineers (SEs) for specific use cases. Organization changes are an important aspect of the company s growth but bring near-term execution risk. Product expansion. Splunk s platform and hybrid architecture lends itself to expanded usage over time across use cases and datasets. Over the past year, Splunk has gradually expanded from an on-premise solution, focusing on machine log data, to a hybrid cloud architecture that includes connectors to SQL databases (DB Connect) and Hadoop (Hadoop Connect, HadoopOps, Hunk). Enterprise 6 has been well received since its release in 3Q14 and features several enhancements focused on business users that should lead to wider enterprise adoption.. Splunk is positioning its platform as the "Data Fabric" responsible for data regardless of location, enabling real time operational intelligence. These new products/connectors are pushed into new places within the organization (data analysts and architects) with new sets of competitors (Business Intelligence, Web Analytics), and time will tell the extent to which it is Page 162 July 17, 2014

163 BMO Capital Markets Splunk able to expand its customer reach outside of IT operations/security to data analysts and business users. Adoption pricing. Pricing has been a topic of investor debate around Splunk for some time. Our research suggests that the Enterprise product is considered to be expensive and the company s push toward selling more enterprise agreements is creating complexities and optics in reported results. We view complexities in pricing as a scaling issue that while noisy in the near term, is ultimately necessary to increase the lifetime value of a customer. In the near term we are concerned that the shift from smaller, transactional-based deals to more enterprise agreements could weigh on sales cycles until a repeatable pricing model is developed. Exhibit 1. Variability in Enterprise Agreements Is Creating Optics in Reported Results Enterprise/Term % Billings 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 9% 14% 7% 8% 11% 17% 7% 13% 16% 18% 33% 43% 25% Source: BMO Research, Company Data Splunk s Enterprise pricing is based on how much data is indexed on an average daily basis and has historically been sold on either a perpetual or term license basis. We believe customer pushback on pricing for the traditional term license really begins to increase when the volume of daily indexed data on existing deployments reaches the terabyte level. With adoption moving from departmental to enterprise wide, more customers are entering into larger strategic licensing arrangements with more predictable usage that allows them to scale more easily and expand their use cases. Revenue under these circumstances is deferred for the life of the contract, and the combination of term licenses and enterprise adoption agreements (EAAs) is expected to be 20%-30% of license bookings in a quarter, making bookings the primary measure of growth. Can the Salesforce Scale Fast Enough? Sales capacity growth is the biggest driver of the business. Splunk continues to scale its coverage model, which continues to be subscale relative to demand. For example, the company has customers in more than 90 countries but with employee resources in fewer than 20. We believe adding sales capacity will be a persistent issue, carrying growth opportunities but also execution risk. Page 163 July 17, 2014

164 BMO Capital Markets Splunk Exhibit 2. Sales Capacity Analysis Overall, ramped sales capacity increased by about 68% in FY2014, and we assume a 39% increase in FY2015, driven predominantly by the 36% growth in overall sales capacity in FY2014. The deceleration in net sales additions in FY2014 may weigh on growth rates in the near term; however, 1Q15 was a strong hiring quarter and more field capacity is expected to be added in FY2015 than in FY2014. Our sales capacity analysis implies a 4% decline in sales productivity in FY2015E and FY2016 based on billings (see Exhibit 3), implying attainable assumptions following a 13% decline in productivity in FY2014. FY13A FY14 FY15E FY16E Notes Quota-carrying sales reps (QCSR) - end of year net adds Adding more in FY15 than FY14 y/y 73% 35% 35% 28% Quota-carrying sales reps -productive Enterprise is 2/3 w/ $2M quota, Inside is 1/3 w/ $1M quots y/y 68% 39% 35% % productive 50% 62% 64% 68% ~50% exiting FY14 and ~60% exiting FY14 License revenues ($M) $136 $199 $259 $340 y/y 54% 46% 30% 31% License revenues per productive QCSR $1,667,755 $1,451,317 $1,365,080 $1,325,977 Enterprise is 2/3 w/ $2M quota, Inside is 1/3 w/ $1M quota y/y -13% -6% -3% Billings ($M) $261 $380 $503 $651 y/y 72% 46% 32% 29% Billings per productive QCSR $3,202,344 $2,771,347 $2,649,760 $2,535,898 y/y -13% -4% -4% Source: BMO Capital Markets Research, company documents. The capacity to hire not only quality reps but also sales engineering talent is the biggest constraint on productivity. Sales reps are not put in the field without an SE, and as the variety of product use cases has grown, time to ramp has been constrained (9-12 months until full productivity). The continued hiring of overlay SEs should help to alleviate this bottleneck but hiring is being heavily scrutinized. Now the company is in the process of shifting from a functional org structure to one that includes market segments and is putting in overlay SEs for specific use cases. Overlay sales forces are the key to taking advantage of emerging product lines and expanding out of IT operations/security. Splunk built a security field team in 2013 under a new VP of security markets; The company is searching for a VP to head the Application and IT Operations Management group. The next likely segment VP position to be added is a head of business analytics. Given the time required to hire specific segment VPs, measured growth seems more likely, but at some point, a more aggressive stance may be necessary to maintain its competitive lead and expand into new markets. Still, indirect revenue surpassed 50% of revenue in 1Q15, up from 30% the prior year, driven mostly by a federal team that saw robust activity beyond security with orders in the IT operations area. The partner ecosystem includes resellers, technology partners, system integrators (SIs), managed service providers (MSPs) and original equipment manufacturers (OEMs). Indirect distribution is largely regional with the majority of the business in APAC from partners, less so in Europe, and even less in the Americas. Significant Market Opportunity Splunk is competing for data infrastructure, security, operations, and analytics market dollars. Big data is not about technology but about the real-time use of data, and we believe that Big Data won t represent a discrete market segment that one can easily size. The company is Page 164 July 17, 2014

165 BMO Capital Markets Splunk levered to machine data growth, and IDC projects that the digital universe will reach 40 zettabytes (ZB) by 2020 with machine-generated data expected to represent 40% of that. Pricing per GB capacity comes down with scale, and given the capacity-based model, a multibillion dollar market opportunity exists and there is room for multiple competitors. We believe Splunk faces a $42 billion market growing at a 8% CAGR to $52 billion by This includes IT Operations ($20 billion), Security Information and Event Management ($1.8 billion), Data Integration and Data Quality ($4.5 billion), and Business Intelligence ($15 billion) markets. Most organizations have not been analysing log files, so we believe the majority of Splunk s market is greenfield. Splunk faces little competition here as it is mostly replacing homegrown solutions. Security and business analytics are more traditional markets that have more established competition. In business analytics, we believe that Splunk does begin to capture some of the $15 billion spent annually on BI software through its DB Connect and Hunk products. The market for OI is in the early innings but the following metrics suggest that the market is poised for very strong growth:: SumoLogic s recently raised $30 million in funding bring its total to over $75 million. VMware has stated that 75% of IT organizations are not doing log management in any real sense. Significant M&A activity in the log and event management space: HP acquired ArcSight; Tibco acquired LogLogic; Solarwinds acquired TriGeo; and IBM acquired Q1-Labs, among others. The company is underpenetrated in its customer base and markets. It typically engages only the IT operations department.. That said, of the company s approximately 7,400 customers, Spunk counts about 70 of the Fortune 100 and about 330 of the Fortune 500 as customers, indicating the strategic value it delivers. With 7,400 customers, we see ample room to grow. According to management, emerging opportunities in Business Analytics and the Internet of Things segments are likely to grow faster even than its core IT Applications and Operations Management and Security segments. All five major segments are relatively similar in size, suggesting significant addressable market expansion opportunity. Weighing the Competitive Landscape Competition has been of growing investor concern over the past several months. Our conversations with private competitors, partners, and ecosystem providers lead us to conclude that Splunk commands technology leadership in an underpenetrated market with room for multiple competitors. The core opportunity in the machine log management market is nascent; IDC estimates that about 16 zettabytes of machine data will be produced by We believe Splunk remains out in front from a product functionality and mindshare standpoint. We think investor concerns regarding the competitive threat posed by SumoLogic are overblown given the size of the overall opportunity. The more relevant competitive focus should be pointed toward emerging Page 165 July 17, 2014

166 BMO Capital Markets Splunk use cases around business analytics and digital intelligence, where there is more established competition. Competition comes from many different places, including in-house custom development, security, systems management, web analytics, business intelligence, and big data market vendors. As Splunk becomes deployed more enterprise wide and sees increasing adoption among business users, it will inevitably face more competition from web analytics and traditional BI vendors. The sales focus will move outside of traditional security and systems management vendors (IT Operations and Application Management) toward selling to data analysts and architects and individual business units. To grow, Splunk will need to rely on specialized sales people as the company faces different buying requirements than traditional enterprise buyers. As mentioned above, scaling the sales organization presents an ongoing challenge. Exhibit 3. Product Expansion Into Emerging Use Cases Changes Competitive Landscape Core - Machine Data Use case Products % Revenue Competition IT Infrastructure and Enterepise 6, Splunk for VMware 30% BMC Software, CA, Loggly, Microsoft (EventViewer), Operations Management SumoLogic, TIBCO (LogLogic) and VMware (LogInsight) Application Management Enterepise 6 30% Security and Compliance Enterepise 6, Splunk for Enterprise HP (Arcsight), IBM (Q1 Labs), LogRythm, Intel/McAfee 30% Security, Splunk for PCI Complaince (NitroSecurity) Other 10% Busienss Analytics Enterepise 6 Business intelligence vendors including IBM, Oracle, SAP, Tableau, Qliktech Digital Intelligence Enterepise 6 Web analytics vendors including Adobe Systems, Google, IBM and Webtrends Big Data vendors including Hadoop distribution and NoSQL data stores such as Hortonworks, Cloudera, MapR provide coopetition with overlapping monitoring and management capabilities. Hunk brings it into competition with specialist BI tools for analyzing data in Hadoop such as Datameer, Plaforma, Karmasphere. Emerging - SQL and Hadoop Data Internet of Things Enterepise 6, Hunk Source: BMO Capital Markets Research, company data From a product perspective, Splunk s front-end user interface has been viewed with mixed opinions, and while it is improved in Enterprise 6, it is still perceived as not having the easiest usability. The company s recent partnership with Tableau demonstrates some of Splunk s frontend weakness, and we wouldn t be surprised to see Splunk be more acquisitive in this area, especially as it expands into newer use cases. Given its focus on machine data and specialization around data connectors, Splunk actually touches and monetizes more forms of data then the emerging Data Discovery BI vendors, which are more focused on new delivery mechanism to extend traditional relational BI data sets. Thus, Splunk may have a larger addressable market than that of the data discovery vendors. Splunk has been late to the game when it comes to cloud delivery. While Splunk needs to improve in this area, data gravity, which describes how data often stays where it is created, ensures that there will likely be plenty of on-premise machine data to keep Splunk busy in the near term. Over time we expect this to shift as the Internet of Things and Cloud Services (SaaS, PaaS, IaaS) proliferation will create more data in the cloud and drive the adoption to cloud BI solutions. In the core IT Operations and Application management space, we see cloud-based vendors as the biggest competitive hurdle, although not necessarily in a zero-sum scenario. SumoLogic, a commonly cited competitor, is targeting the same core enterprise space as Splunk but is going Page 166 July 17, 2014

167 BMO Capital Markets Splunk to market with its pure cloud-based delivery model. This enables SumoLogic to effectively manage capacity and pricing as well as offer insights around infrastructure by benchmarking between clients. Its current focus is on application availability/performance and security. We have heard of existing Splunk customers implementing SumoLogic; however, we think it s important to keep in mind that Sumo has roughly 300 customers versus Splunk s 7,400. Over time, we believe the cloud/subscription model will be preferred by some customers as a way to predominantly manage the growth in capacity relative to price. VMware s vcenter Log Insight appears to be the stiffest potential competition in on-premise deployments. This product is still in a 1.0 version but could ultimately be a price challenger to Splunk. CommVault is trying to enter the operational intelligence market through its content store but has had little traction to date. Incumbent BI vendors appear to have mixed traction in the log management market, based on our field research. Most of the players in the log management arena have been focused on the security space. Privately held LogRhythm is competing and winning deals in the security segment, mostly at the expense of HP/ArcSight, which has traditionally led in the SIEM market but has seen diminishing product innovation. LogRhythm claims it wins against Splunk when security organizations are leading the buying initiative. Splunk tends to win by virtue of its incumbency in IT Operations and Application Management. With the release of Hunk, Splunk now sells directly to data analysts and architects. At the same time, the company is now competing more directly with Big Data vendors such as Datameer, Plaforma, and Karmasphere. In addition, the release of DB Connect will increasingly bring it into competition with the traditional data management providers Oracle, IBM, Microsoft. One piece of competitive differentiation that we don t believe should be forgotten is the company s application ecosystem. Apps can be built by customers for internal use, by third parties for consumption of the company s application platform, or offered directly by Splunk. A growing app ecosystem could create a competitive moat as apps feed indexes and drive capacity based on revenue growth. Splunk 6 introduced the Splunk web framework, which helps developers quickly build Splunk custom apps. The bigger opportunity for the company is to go to corporate customers and demonstrate how corporate developers inside of the enterprise can write apps internally and expand usage. For example, writing a customer support app with a forms view brings users inside the support organization to consume data, increasing what they pay Splunk. Further, Splunk should have incremental opportunity by directly monetizing the apps themselves. We believe this app strategy likely supports future module use cases, extension into adjacent markets, and potential acquisitions. Capacity-Based Licensing Model and Platform Strategy Should Support Premium Multiple Based on net new customers added since FY2011, Splunk has an expected lifetime revenue opportunity of about $1 billion+, with upside to these assumptions as the company introduces new products. The current enterprise value of Splunk is about $4.5 billion, or 4.5x the value of that opportunity. We believe that Splunk has a significant opportunity from its recent customer additions alone. As can be seen in Exhibit 4, the average repeat purchase per customer is x the initial Page 167 July 17, 2014

168 BMO Capital Markets Splunk deal. Enterprise pricing is based on how much data is indexed on an average daily basis and has historically been sold on either a perpetual or term license basis. We estimate that about 30% of revenues in the next two years will come from expansions from new customers booked in This number encompasses upgraded capacity in existing use cases (about 33% of upsell bookings) and expansion into new use cases (about 66% of upsell bookings). Given that the incremental cost of an add-on sale is usually significantly less than the cost of obtaining a new customer, repeat business should drive margin expansion. Exhibit 4. Lifetime Revenue Opportunity of Customer Base Lifetime revenue model drivers Inputs Notes Net new customers added since FY11 (1) 4, = 66% of total customer base Average initial product purchase per customer (2) $35,000 $30-40K initial ASP reported Average repeat purchase per customer over time (3) 5.0x 4.5-5x as on 1Q14 Maintenance as % of perpetual sale (4) 20% Company list price Lifetime revenue per customer License revenues (2) x (3) = (A) $175,000 Assumes TCV of term equivalent to perpetual Maintenance revenues (A) x (4) = (B) $35,000 Total $210,000 Lifetime revenue based on net new customers added ($M) License revenues (1) x (A) $805 Maintenance revenues (1) x (B) $161 Total $966 Source: BMO Capital Markets and company data We see upside to these assumptions as the company has introduced incremental revenue opportunities: Paid opportunities with Hunk, Splunk App for VMware paid, Splunk Cloud, and BugSense for machine-generated data for mobile devices, which would be incremental expansion opportunities with existing customers. Enabling capacity consumption methods with Enterprise 6.0, which opens up the platform for incremental consumption from business users; the ODBC driver, which provides industry standard connectivity between version 6 and third-party analytics tools such as Excel and Tableau; DB Connect to integrate between structured data from relational databases and Splunk; and acquired network data capture technology with Cloudmeter. Page 168 July 17, 2014

169 BMO Capital Markets Splunk Exhibit 5. Significant Expansion of Paid Product, Enablers, and Product Delivery 1Q13 2Q13 3Q13 4Q13 1Q14 2Q14 3Q14 4Q14 Paid Splunk Enterprise 6.0 Splunk Cloud Splunk Enterprise 5.0 Splunk App for VMware 2.0 Enterprise Security Version 2.0 Splunk Storm Splunk App for PCI 2.0 Hunk Bugsense Acquisition Splunk App for VMware 3.0 Splunk app for Microsoft Exchange Splunk App for VMware Splunk app for HadoopOps SDK for Java and Python Splunk DB Connect Splunk Storm Free Cloudmeter Acquisition Splunk App for Windows Active Directory Splunk Hadoop Connect ODBC driver Splunk App for Palo Alto Networks Source: BMO Capital Markets Enablers Splunk increased R&D spending (44% y/y) faster than product billings (43% y/y) in 1Q, indicating the pace of R&D activity should support further expansion into new use cases and consumption. Spending and growth relative to peers in the analytics space are in the upper echelon of analytics peers. Exhibit 6. Pace of R&D Activity Should Support Further Expansion Into New Use Cases and Consumption R&D ($ millions) Run Rate % Revenue y/y growth Analytics Tableau $ % 58% Splunk $ % 44% Qlik $ % 3% Source: BMO Research, Company Data Industry Backdrop The term Big Data has become a ubiquitous marketing term, so any discussion must start with a definition. At its core, Big Data refers to datasets whose size is beyond the ability of traditional IT assets, processes, and applications to capture, store, manage, and analyze cost effectively. The premise of Big Data is that businesses can extract business decision making insight from transactional or structured data (traditional business data), unstructured data (web-based data, files, media), and machine-generated data (machine logs),with the goal of affecting business outcomes positively. Page 169 July 17, 2014

170 BMO Capital Markets Splunk Data has become a currency to create better products and services, make R&D more productive, establish new business models, improve the quality of customer interactions, and create a competitive advantage to take market share. Machine data and "digital exhaust" are two types of data holding significant amounts of value that until recently have fallen by the wayside because a traditional RDBMS isn t optimized to manage data on this scale. Machine data is one of the fastest-growing, most complex (lacks standardized formatting), and most valuable (contains a categorical record) segments of Big Data and is projected to increase 15x by 2020 to represent 40% of the digital data universe, according to IDC. Machine data affects almost every industry and business unit encompassing GPS, RFID, hypervisor, web servers, , messaging clickstreams, mobile, telephony, IVR, databases, sensors, telematics, storage, servers, security devices, desktops, temperature, alarms, alerts, etc., which generate massive streams of data in an array of unpredictable formats that are difficult to process and analyze by traditional methods or in a timely manner. The continued rise of the digital economy is creating a trail of digital exhaust unstructured information/data that is a byproduct of the online activities of Internet users. Examples include Amazon/eBay transactions, shipping records, Twitter feeds, LinkedIn data, YouTube views, Facebook interactions, and general web page views. This data had previously been considered insignificant because value could not be extracted through traditional OLAP systems, and it was typically discarded. However, with systems based on newer data management paradigms, such as NoSQL, examining this data is now possible and can provide insight into user and customer behavior and preferences. Key Drivers IDC projects that the digital universe will reach 40 zettabytes (ZB) by 2020, resulting in a 50x growth from the beginning of 2010, equivalent to 5,247 GB per person worldwide. IDC estimates that only 0.5% of the world s data is being analyzed and 3% is being tagged. Machine-generated data is a key driver in the growth of the world s data and is projected to increase 15x by 2020 (representing 40% of the digital universe) The McKinsey Global Institute estimates that data volume has been growing 40% per year, and will grow by 44 times this rate between 2009 and In 2012, Internet users generated 4 exabytes of data, fed by more than one billion computers and one billion smartphones. Over 50% of Internet connections are things. In 2011, there were 15+ billion permanent and 50+ billion intermittent connections and that is expected to rise by 2020 to 30+ billion permanent and >200 billion intermittent. Page 170 July 17, 2014

171 BMO Capital Markets Splunk Exhibit 7. 50x Growth in Digital creation from 2010 to 2020 Zettabyte (ZB) Source: BMO Research, IDC Product Digital Data Created Machine generated data is a key driver in the growth of the world s data which is projected to increase 15x by 2020 (representing 40% of the digital universe) Splunk s platform and hybrid architecture lends itself to expanded usage over time across use cases and data sets. Splunk is positioning its platform as the "Data Fabric" that collects data from anywhere to perform real-time operational intelligence. The company has gradually expanded from machiene log data to include connectors to SQL databases and Hadoop. We re positive on its strategy, but time will tell the extent to which Splunk is able to expand its customer reach outside of IT operations/security into the world of data analysts and business users. Splunk Enterprise is a proprietary machine data engine, comprising collection, indexing, search, reporting analysis, and data management capabilities. The core of Splunk s product is based around indexing data,breaking log files into indivdual events and making this data available for searching, monitoring, analysis, and visualizations (Index Search Analyze Report). This information can then be used for troubleshooting applications, servers, and networks, for business analytics or for complying with security and compliance requirements. For example, Splunk can correlate a "customer ID" to a customer order of a purchase and glean as to where a customer churned. The key fundamental difference between Splunk and traditional data frameworks is its patented data architecture. Splunk s data architecture creates dynamic schema in real time, enabling users to run queries on data without having to understand the structure of the data prior to collection and indexing. Splunk believes that search will be the de facto data query language standard, which will eliminatethe ETL/data warehouse/mdm model. Investments in R&D have enabled the expansion from a single product on-premise solution serving machine data and supporting mostly IT users into a multi product company supporting other unstructured (industrial, hadoop, etc) and structured (RDBMS) data types and now includes cloud delivery and business user support. Enterprise adoption pricing strategies are concurrently enabling broader adoption for enterprise customers. Some customers have Page 171 July 17, 2014

172 BMO Capital Markets Splunk thousands of users getting data from the product, but only dozens that use the queries themselves. This was a key consideration in the latest Enterprise 6 release, which focused on addressing business users to enable broader enterprise adoption. Key enhancements in Enterprise 6 include: Faster and easier analytics for business users to interface directly with the product through a pivot interface and dashboards; The Splunk web framework, which helps developers build Splunk custom apps; and The latest 6.1 release include multi-site clustering and the ability to embed Splunk reports into other business applications. Part of its differentiation is its flexibility for customers to run on premise, in public or private clouds, through MSPs or purely as a service. MSP delivery has been available for years, but in 3Q14 a full-featured Enterprise version of Splunk Cloud was introduced for large-scale production environments. Early customer wins, including ConstantContact, Overstock.com, and SurveyMonkey, indicate cloud adoption is a popular choice of companies that generate much of their data in the cloud. This is consistent with our market view on cloud analytics adoption. Cloud progress has been increasing after a slow start, and the company is positive on the longer-term ability to offer a hybrid cloud and on-premise architecture. This followed a year in which the cloud was initially targeted at developers through Splunk Storm. During that time, the company observed its cloud business bifurcating into developers and enterprise operational use cases, ultimately leading to the Splunk Cloud release. Splunk Storm is now free for developers,(up to 20GB). Storm is strategic to the company because as more developers build Splunk into their products, more machine data or logs are being generated, and hence, a greater opportunity for extraction and analysis. Exhibit 8. Product Comparison Functionality Comparision Splunk Free Splunk Enteprise Splunk Cloud Universal Indexing X X X Search X X X Distributed Search X Monitoring & Alerting X X Reporting/Dashboards X X X Knowledge Mapping X X X Data Model X X X Pivot X X X High Performance Analytics Store X X Report Acceleration X X Embedded Reports X X X PDF Delivery X X Access Control and Single Sign- X X Single-site Clustering X Single-site Cluster Management X Multi-site Clustering X Universal Forwarder X X X Forwarder Management X X X Rich Developer Environment X X X Apps X X X Premium Apps X X Standard Support X X X Enterprise Support X X Source: BMO Capital Markets Research, company documents. Page 172 July 17, 2014

173 BMO Capital Markets Splunk In the past 12 months, Splunk s product portfilio has been expanded dramatically. Paid opportunities with Hunk, Splunk Analytics for Hadoop, Splunk App for VMware paid version, BugSense an app monitoring solution for machine-generated data for mobile devices, and Splunk Cloud are all incremental expansion opportunities with existing/new customers. Further, enabling capacity consumption methods include the ODBC driver, which provides industry standard connectivity between Enterprise 6.0 and third-party analytics tools such as Excel and Tableau Desktop, DB Connect to integrate between structured data from relational databases and Splunk, and acquired network data capture technology with Cloudmeter. Exhibit 9. Emerging Use Cases Support TAM Expansion Use case % Description Core Proactively monitor and ensure uptime, and to rapidly pinpoint IT Infrastructure and Operations Management 30% and resolve problems when they occur Application Management 30% Troubleshoot across the complete application stack from one place and monitoring for performance degradation Security and Compliance 30% Provide rapid incident response, correlation and in-depth monitoring across all data sources Gain visibility and intelligence on customers, services and Other (IoT, digital intelligence, analytics, etc.) 10% Emerging transactions, industrial data Source: BMO Capital Markets and company data Splunk has the ability to derive different information from the same source data, which makes it applicable across many use cases. The product is evolving from search and investigation, to proactive monitoring, operational visability, and providing business insights. Three core use cases are being extended to emerging use cases, focused more on business users outside of IT. According to management, emerging opportunities in Business Analytics and the Internet of Things segments are likely to grow even faster than its core IT Applications and Operations Management and Security segments. All five major segments are relatively similar in size, suggesting a significant adressable market expansion opportunity. IT Operations Management Splunk collects, indexes and harnesses live data generated from virtually any source, format or location, including packaged and custom applications, app servers, web servers, databases, networks, virtual machines, hypervisors, and operating systems. Splunk Enterprise can integrate siloed data into actionable information that quickly finds and fixes problems, users don t have to manually sort through logs or custom scripting. For example, examining log files from database and web servers in conjunction with one another can pinpoint the point of failure automatically. Application Management Splunk complements existing developer tools, such as APM software, by providing real-time visibility into how an application is working in development and production environments. Developers can get the visibility they need to fix and optimize their code for complex deployments without needing direct access to production systems. According to NBC Universal, the use of Splunk allows developers to focus on business projects rather than building inferior manual, in-house tools to monitor the health and scalability of web sites. Security Splunk is also being used as an analytical engine capable of addressing advanced threats, fraud, and other high-value security use cases. Security was the largest segment in Q1, as organizations are using Splunk either to complement or to replace traditional SIEMs. Splunk collect and indexes any type of data (system and application log data, flowdata and packet data, threat intelligence data and context data) that enables analysts to ask threat Page 173 July 17, 2014

174 BMO Capital Markets Splunk scenario-based questions to proactively find threats by examining data patterns in normal activities. Business Analytics Splunk is being used by data architects, business analysts, developers and IT leaders to complement and extend existing analytics tools and data sets by incorporating machine data for real-time business insights to executive, sales, product, marketing, operations and customer service teams. Splunk is being used to provide new insights from machine data, enrich machine data with structured data for business context (Splunk DB and Hadoop Connect), or complement existing BI tools and other big data technologies (e.g., Tableau partnership). Splunk's Digital Intelligence solutions provide an end-to-end view of customer interactions across various digital channels, including web, mobile, social, and offline by accessing machine data and correlate dataset across various digital channels. The platform can also feed content management systems and other relevant systems with input for meaningful actions. Business analytics use cases are limited at this point, but bring a new set of competitors in web analytics vendors. The challenge Splunk faces here is the need for heavily specialized sales resources because marketers have different buying requirements than traditional enterprise buyers. Exhibit 10. Business Analytics Use Cases At SplunkLive NY, in mid-may 2014, Splunk demonstrated a business analysis use case by monitoring retail and webstore sales of iphone units in real time. The demonstration showed how real-time sales data could be retrieved through dashboards and pivot interfaces to optimize marketing and promotional campaigns. IT users were able to gain visibility into the webstore (webserver, app server, database) from one place to perform break-fix and to keep the site up and running. Source: Splunk Page 174 July 17, 2014

175 BMO Capital Markets Splunk Industrial Data and the Internet of Things Splunk is a key enabler of extracting value and insights from machine data, including data from sensors, user interaction logs, devices, and mechanical systems. This is particularly applicable for manufacturers, hospitals, enterprises with a large number of connected devices, and industrial facilities that are able to gain insight from data that had previously not been possible using traditional business intelligence tools. For example, Ford launched Connected Car Dashboards, a collaborative project with Splunk Enterprise and Cisco that collected and analyzed data from vehicles to gain insight into driving patterns and vehicle performance. The company used its Ford OpenXC research platform to enable developers to read data from a vehicle's internal communications network. Data is indexed, analyzed, and visualized in Splunk and made available in Connected Car Dashboards, which include visualizations specific to both electric and gas-powered vehicles. Insights gained include analysis of the accelerator pedal position, vehicle speed, steering, and wheel position, among other metrics. Apps: Going Wide and Deep Splunk Enterprise is complemented by additional free/paid apps/add-ons that can be deployed directly on top of the core platform to drive value directly from broader data sets and specific use cases. Apps can be built by customers for internal use, by third parties for consumption of the company s application platform, or offered directly by Splunk. A growing app ecosystem could create a competitive moat as apps feed indexes and drive capacity-based revenue growth Secondarily, Splunk should have an incremental opportunity from directly monetizing the apps themselves. We believe this app strategy likely supports future module use cases, extension into adjacent markets, and potential acquisitions. There are three tiers of apps on the company s app platform: Paid applications apps that have dedicated development resources and workflows into them. Splunk directly monetizes only three applications, Enterprise Security, PCI Compliance, and VMware. In 4Q14, version 3 of the Splunk app for VMware was released, and more than 30 opportunities were closed. In 1Q15 more than 100 orders included at least Enterprise Security or PCI. Templates Not directly monetizeable based on basic functionality. For example, Office Documents Template System (ODTS) for Splunk generates office documents with Splunk search results inside. Add-ons Not directly monetizeable connectors. There are over 224 add-ons in the Splunk app store. For example, Splunk recently released its ODBC driver, which provides industry standard connectivity between Enterprise 6 and third-party analytics tools such as Excel and Tableau, enabling business users to combine machine data with structured data to support business analytics use cases. Splunk 6 introduced the Splunk web framework which helps developers quickly build Splunk custom apps. The bigger opportunity for the company is go to corporate customers and demonstrate how corporate developers inside of the enterprise can write apps internally and expand the usage. Page 175 July 17, 2014

176 BMO Capital Markets Splunk Hunk: Gaining Insights From Hadoop and NoSQL With the release of Hunk, Splunk now sells directly to data analysts and architects; at the same time, Splunk is now competing more directly with Big Data vendors such as Datameer, Plaforma, and Karmasphere. In addition, the release of DB Connect will increasingly bring it into competition with the traditional data management providers, Oracle, IBM, and Microsoft. Splunk could be considered more capable of analyzing Hadoop data than traditional SQL-based BI data discovery vendors like Tableau. Hunk is a fully integrated analytics platform for Hadoop that enables users to interactively explore, analyze, and visualize historical data that rests in Hadoop or NoSQL data stores in real time without moving the data. This instant access to data takes out the manual process of extracting data, reduces complexity, and speeds time to value. Standalone Hadoop currently has limited capabilities for generating insight from data and is typically run in conjunction with traditional information management and processing technologies. Splunk s Virtual Index decouples the data storage tier from the data access and analytics tiers to enable seamless interactive exploration, analysis, and visualization for data stored in Hadoop. The need for more analytical Big Data applications and tools to build applications is one of the top barriers to more mainstream Hadoop adoption. Developers can integrate data and functionality from Hunk into enterprise big data applications using the Splunk web framework, documented REST API, Eclipse plug-in, and software development kits (SDKs). Go-to-market partnerships with the main Hadoop distributors Hortonworks, Cloudera, and MapR support commentary of a solid pipeline exiting 1Q15. Several early customer wins in 1Q included: Enterprise-wide license with a global internet company whose grid services team runs one of the largest deployments of Hadoop. Vantrix, which is using Hunk to analyze data generated by its global media networks. Hunk was Vantrix s first purchase of Splunk products. A state public health agency purchased Hunk to analyze pharmacy and health records. It is trying to identify unusual prescription patterns from pharmacies selling drugs that are prone to abuse, helping to reduce drug abuse and fraudulent claims. Pricing: Near-Term Noise Offset by Long-Term Accretion Pricing has been a topic of investor debate around Splunk for some time. Our research suggests that the Enterprise product is considered to be expensive, and the company s push toward selling more enterprise agreements is creating complexities and optics in reported results. We view complexities in pricing as a scaling issue that, while noisy in the near term, is ultimately necessary to increase the lifetime value of a customer. In the near term we are concerned that the shift from smaller, transactional-based deals to more enterprise agreements could weigh on sales cycles until a repeatable pricing model is developed. Splunk s Enterprise pricing is based on how much data is indexed on an average daily basis and has historically been sold on either a perpetual or term license basis. We believe customer Page 176 July 17, 2014

177 BMO Capital Markets Splunk pushback on pricing for the tradition term license really begins to increase the when volume of daily indexed data on existing deployments reaches the terabyte level. With adoption moving from a departmental to an enterprise-wide function, more customers are entering into larger strategic licensing arrangements with more predictable usage that allow them to scale more easily and expand their use cases. Revenues under these circumstances are deferred for the life of the contract and the combination of term licenses and enterprise adoption agreements (EAAs) is expected to account for 20%-30% of license bookings in a quarter, making bookings the primary measure of growth. Enterprise adoption agreements the introduction of EAAs in 3Q13 has increased complexity with respect to the company s revenue recognition. In an EAA, a customer buys a perpetual license with a prescribed maximum daily indexing volume. The customer can spike above the limit without penalty. The agreement requires that at some point in the future, typically three years, Splunk will measure the amount of usage over the limit and upgrade the license at a rate specified in the contract. Term a customer buys a term license with a negotiated pricing level and a capacity level that has a predictable forward-year pricing model. The weighted average lifespan of a term transaction is typically 12 months, although some go up to three years. Hunk is also sold on a term license subscription basis, and Splunk Cloud is sold as a subscription. In February, as part of the company s push to drive higher enterprise usage, Splunk doubled the license capacity to up to 20GB of daily indexing at entry levels for Splunk Enterprise. This has driven more data usage as intended, and in 1Q, the company reported significantly faster yearover-year growth in the number of transactions in the product SKUs where prices were reduced. The offsetting higher volume resulted in overall ASPs consistent with expectations and previous levels.. Exhibit 11. Product Pricing Comparison Features Splunk Free Splunk Enteprise Splunk Cloud Hunk Splunk Storm Delivery Model Install Install Cloud Install Cloud Description Splunk Free license is intended for individual use The Platform for Operational Intelligence Splunk Enterprise as a cloud service Splunk Analytics for Hadoop and NoSQL Data Stores Splunk Storm is mainly focused on Developers Revenue Model License Option - Perpetual License Option - Term Free for 500MB of daily index volume for 60 days Data volume indexed per day $4,500 for 1 GB/day, plus annual support fees Term licenses start at $1,800 per year, which include annual support fees Subscription $1,000 per month for data Subscription Option volumes up to 5 GB/day *Users of Splunk Enterprise can currently exceed the daily indexing amount specified in their license up to five times per month. Source: BMO Capital Markets Research, company documents. Term license based on the number of TaskTrackers (Compute Nodes in YARN) One-year term license of Hunk starts at $2,500 w/ a minimum of 10 Task Trackers Free for developers limited to 20 GB of total storage for machine data with a retention period of 30 days Page 177 July 17, 2014

178 BMO Capital Markets Splunk Distribution: Capacity Constraints a Hurdle and Opportunity Sales capacity growth is the biggest driver of the business. Splunk continues to scale its coverage model, which continues to be subscale relative to demand. For example, the company has customers in more than 90 countries but employee resources in fewer than 20. We believe adding sales capacity will be a persistent issue, carrying growth opportunities as well as execution risk. Splunk has shifted from a predominantly geographic inside sales team to a tiered direct sales team, based around named major accounts, SMB territory management, and SMB inside sales. The company is adding market segment focused personnel and overlay SEs for specific use cases. Organization changes are an important aspect of the company s growth but bring nearterm execution risk. Overlay sales forces are the key to taking advantage of emerging product lines and expanding out of IT operations/security. Splunk built a security field team in 2013 under a new VP of security markets. The company is searching for a VP to head the Application and IT Operations Management group. The next likely segment VP position to be added is a head of business analytics. Given the time required to hire specific segment VPs, measured growth seems more likely, but at some point, a more aggressive stance may be necessary to maintain its competitive lead and expand into new markets. Sales capacity growth is the biggest driver of the business. On average it takes quota carriers anywhere from 9 to 12 months to reach full productivity, and the average quota for a tenured outside sales rep is about $2 million, while for an inside rep it is about $1 million. The capacity to hire not only quality reps but also sales engineering talent is the biggest constraint on productivity. Sales reps are not put in the field without an SE; and as the variety of product use cases has grown, time to ramp has been constrained (9-12 months until full productivity). The continued hiring of overlay SEs should help to alleviate this bottleneck, but hiring is being heavily scrutinized. On the positive side of distribution, indirect revenue surpassed 50% of revenue in 1Q14, driven mostly by the federal team, which saw robust activity beyond security with orders in the IT operations area. The partner ecosystem includes resellers, technology partners, system integrators (SIs), managed service providers (MSPs) and original equipment manufacturers (OEMs). Indirect distribution is largely regional with the majority of business in APAC from partners, whereas other regions tend to be more direct. Resellers include Accuvant, CDW, Fishnet Security, Forsyth, and Gotham Technology Group. Technology partners include Tableau, Hortonworks, Cloudera, MapR, Cisco, PaloAlto Networks. Global SIs include Accenture and Wipro. Page 178 July 17, 2014

179 BMO Capital Markets Splunk Operational Intelligence Market The collection and analysis of data in organizations is not a new phenomenon. Data warehouses have been in use since the 1980s, Business Intelligence tools have been deployed since the 1990s, and organizations have spent tens of billions of dollars (about $15 billion in Business Intelligence spending in FY2015 forecast) to analyse their structured data sources. However, despite this investment, only 0.05% of digital data created is being captured for use in the real time reporting and analytics process. Operational Intelligence (OI) is a form of real-time dynamic, business analytics that delivers visibility and insight into business operations. OI solutions run query analysis against machine logs, live feeds, and event data to deliver real-time visibility and insight into business and IT operations, enabling people to make better, faster decisions. OI automates processes for responding to events by using business rules and incoming event information. Contrary to Business Intelligence, which is data-centric, OI is primarily activity-centric. Exhibit 12. Traditional Data Types Still Lead Use Cases for Big Data Analysis Source: BMO Research, Gartner Splunk believes that search will be the de facto data query language standard, which will eliminate the ETL/data warehouse/mdm model. Splunk collects and indexes all the streaming data from IT systems and technology devices in real time in tens of thousands of sources in various formats and types, defining the data schema at read, not at write time. Competitve Landscape Competition comes from many different places including in-house custom development, security, systems management, web analytics, business intelligence, and big data market vendors. As Splunk becomes more enterprise wide and moves toward business users, it will increasingly compete with web analytics and BI vendors outside of traditional security and systems management vendors (IT Operations and Application Management). Additionally, Big Data providers are acting as technology go-to-market partners while competition exists for emerging data infrastructure market dollars. Page 179 July 17, 2014

180 BMO Capital Markets Splunk Exhibit 13. Competitive Landscape Security, systems management and other IT vendors, including BMC Software, CA, HP (Arcsight), IBM (Q1 Labs), Intel/McAfee (NitroSecurity), Loggly, LogRhythm, Microsoft (EventViewer), SumoLogic, TIBCO (LogLogic), and VMware (LogInsight). Web analytics vendors, including Adobe Systems, Google, IBM, and Webtrends. Business intelligence vendors, including IBM, Oracle, SAP, Tableau, and Qliktech. Big Data vendors, including Hadoop distribution, and NoSQL data stores, such as Hortonworks, Cloudera, and MapR, provide competition with overlapping monitoring and management capabilities. Hunk brings it into competition with specialist BI tools for analyzing data in Hadoop such as Datameer, Plaforma, and Karmasphere. Security, Systems Management, IT Management Cloud Vendors Business Intelligence Web Analytics Big Data Business Intelligence Big Data Source: BMO Capital Markets Research, company documents. Current Outlook 2014 revenue guidance of $ million implies about 34% top-line growth at the midpoint. We are essentially in line with consensus ahead of the guided range for FY2015 at $411 million. Operating margins are expected to be breakeven, and CFO margins are expected to be about 20%, which is consistent with both our estimates and consensus. Page 180 July 17, 2014

181 BMO Capital Markets Splunk Exhibit 14. Our Model Calls for Accelerating Growth 100% 90% 80% y/y growth 70% 60% 50% 40% 30% 20% 10% License Growth Deferred Revenue Growth Billings Growth Source: BMO Research, Company Data The mix of enterprise/term agreements in 2H15 and a reacceleration in $100K deals are likely the key drivers of the shares in the near term. Deals over $100K decelerated to 27% y/y growth from 69% in 4Q14 and 81% in 1Q14. 1Q is a seasonally weak quarter and experimentation with new pricing structures could push deals toward the back half of the year; however, this is the first time this metric has been below 30% growth. Billings growth accelerated but by less than we would have considered normal seasonality despite the fact that 25% of bookings were from term/enterprise agreements. In the near term, slower-than-expected growth likely keeps the shares range bound despite our view that competition concerns are overblown. Exhibit 15. BMO vs. Consensus 2Q 2015E 2015E 2016E ($M, except EPS) BMO Guidance Street BMO Guidance Street BMO Street Revenues $93.8 $92 94 $93.7 $411.0 $ $411.1 $546.2 $547.6 y/y 40.2% 40.1% 35.8% 35.8% 32.9% 33.2% Operating Profit (non GAAP) $2.9 $2.6 $0.3 $2.2 $10.2 $19.4 % Margin 3.1% (2% 4%) 2.7% 0.1% 0% 0.5% 1.9% 3.5% Non GAAP EPS $0.02 $0.02 $0.00 $0.00 $0.08 $0.12 CFO $14.2 $89.7 $114.9 Source: BMO Capital Markets Research, Thomson Reuters. Margins and Cash Flow Splunk s business model lends itself to increased margins over time as the increase in longer term contracts is absorbed in the model and as the company scales. Over time we see leverage in the business from an increasing percentage of revenue coming off the balance sheet from enterprise/term agreements as well as high-margin maintenance revenue, lower opex (predominantly S&M), and higher margins on upselling new products and capacity. We don t expect any leverage for the foreseeable future due to capacity constraints, the need to build out Page 181 July 17, 2014

182 BMO Capital Markets Splunk an overlay sales force to go after new markets, and sustained R&D spending. The model is efficient, with more than 20% CFO margins, leading to what we expect to be solid FCF growth. Elevated capex of about $20 million this FY, up from $9.3 million in FY2014, hurts FCF this year, but we expect an acceleration in FY2016 FCF from $69.8 million in 2015 and $94.9 million in Balance Sheet The balance sheet remains solid, with net cash and investments of $897 million, or $7.65 per share as of March 31, Valuation Our 10-year DCF analysis makes the following key assumptions, resulting in our target price of $51, slightly above current levels: Revenue CAGR of 25%; EBIT margin slowly increasing to 22%; 11.0% WACC; and 17x terminal cash flow multiple. Exhibit 16.DCF Analysis DCF Analysis ($in millions, except per share) FY2015E FY2016E FY2017E FY2018E FY2019E FY2020E FY2021E FY2022E FY2023E FY2024E TV CAGR Revenues $411 $546 $712 $911 $1,146 $1,420 $1,745 $2,127 $2,571 $3,083 $2,829 25% Growth 35.8% 32.9% 30.4% 27.9% 25.9% 23.9% 22.9% 21.9% 20.9% 19.9% 10.0% EBIT $0 $10 $20 $40 $73 $119 $181 $263 $370 $505 $622 EBIT Margin 0% 2% 3% 4% 6% 8% 10% 12% 14% 16% 22% Tax Rate 0% 0% 0% 0% 0% 0% 0% 35% 35% 35% 35% Taxed EBIT $0 $10 $20 $40 $73 $119 $181 $171 $240 $328 $404 Depreciation $10 $14 $18 $23 $29 $36 $44 $54 $66 $79 $72 CapEx ($20) ($20) ($25) ($32) ($39) ($46) ($55) ($65) ($76) ($88) ($78) Change in Working Capital $84 $91 $111 $133 $156 $179 $211 $247 $286 $327 $286 Free Cash Flow $75 $95 $124 $165 $220 $288 $382 $407 $515 $646 $685 27% Growth 27% 31% 32% 34% 31% 33% 7% 27% 25% 6% Discounted FCF $67 $77 $91 $108 $130 $154 $184 $177 $201 $227 Cumulative cash flow $1,418 28% Tax Rate 35.0% Terminal Value $3,693 72% WACC 11.0% Total DCF value $5,110 Cash Flow 17x Debt $0 Multiple Cash $897 $7.65 Market Value of Equity $6,008 Shares Outstanding 117 Share Price $51.22 Current Price $46.54 upside/(downside) 10% Source: BMO Capital Markets Research, company documents. Spunk is generating positive FCF but given the early-cycle nature of the market we don't anticipate significant margin expansion from current breakeven levels. EV/sales is the primary metric for comparable analysis, and our $51 price target is based on 9.4x our 2016 EV/sales estimate. We believe a premium valuation is reasonable, given Splunk s leadership in the nascent operational intelligence market. Page 182 July 17, 2014

183 BMO Capital Markets Splunk Exhibit 17. Peer Analysis (in Millions, Except Per Share Data) Recent Cash/ EV/Sales Revenue Growth Ticker Rating Price EV Share FY1E FY2E FY0-'FY1E FY1E-'FY2E Splunk Inc SPLK MP $46.54 $4,561 $ x 8.4x 35.8% 32.9% vs On-Demand: High Growth Subscription 1% 3% vs Software: High Growth License 15% 20% vs Average 4% 8% vs Data Infrastructure 219% 182% On-Demand: High Growth Subscription Cornerstone Ondemand Inc CSOD NC $40.46 $2,053 $ x 5.6x 45.6% 36.9% Demandware Inc DWRE OP $58.99 $1,776 $ x 8.3x 43.1% 43.6% Marketo Inc MKTO NC $26.01 $912 $ x 4.9x 47.3% 31.9% Netsuite Inc N MP $80.79 $5,886 $ x 8.5x 30.9% 27.9% Servicenow Inc NOW NC $56.32 $7,767 $ x 8.6x 54.5% 38.4% Veeva Systems Inc VEEV NC $23.97 $3,079 $ x 8.8x 34.2% 24.2% Workday Inc WDAY MP $79.28 $13,123 $ x 12.0x 59.5% 45.8% Average 11.0x 8.1x 45.0% 35.5% Software: High Growth License Fireeye Inc FEYE NC $34.57 $4,365 $ x 7.2x 155.1% 46.6% Palo Alto Networks Inc PANW NC $77.59 $5,449 $ x 7.0x 46.7% 34.1% Tableau Software Inc DATA OP $61.00 $3,254 $ x 6.7x 53.7% 35.8% Average 9.7x 7.0x 85.2% 38.8% Average 10.6x 7.8x 57.1% 36.5% Software: Data Infrastructure Commvault Systems Inc CVLT OP $48.59 $1,918 $ x 2.5x 15.0% 14.1% Tableau Software Inc DATA OP $61.00 $3,254 $ x 6.7x 53.7% 35.8% Informatica Corp INFA NC $33.59 $3,028 $ x 2.6x 11.9% 11.9% Microstrategy Inc MSTR NC $ $1,211 $ x 1.9x 6.2% 7.0% Qlik Technologies Inc QLIK MP $21.28 $1,645 $ x 2.6x 15.9% 15.5% Teradata Corp TDC MP $41.78 $6,040 $ x 2.1x 3.4% 5.5% Tibco Software Inc TIBX NC $19.06 $3,147 $ x 2.7x 2.5% 6.3% Verint Systems Inc VRNT NC $47.96 $3,423 -$ x 2.8x 25.4% 9.0% Average 3.5x 3.0x 16.8% 13.1% (Fishbein: CRM, CVLT, DATA, DWRE, ECOM, QLIK, N, SPLK, WDAY) (Bachman: TDC) (NC = Not covered. Thomson data for not covered companies) (OP= Outperform)(MP = Marketperform) *Estimates reflect latest complete and forward fiscal years Source: BMO Capital Markets Research, Thomson Reuters. Stock prices as of the close July 16, Risks Emerging market. Products are targeted solutions for specific use cases and as an enterprise solution for machine data. Growth depends on expanded functionality of products to increase their acceptance and use by the broader market as well as to develop new products. Back-end-loaded quarter and license business. The majority of business is closed in the final weeks of the quarter. This can result in variability in quarterly results. Indexing capacity expansion. Growth depends in part on existing customers expanding the usage of their products and purchasing new products. Customers must agree to higher license fees for products or limit the amount of data indexed to stay within the limits of their existing licenses. A failure by users to accept this could result in lower license growth or increased churn. Page 183 July 17, 2014

184 BMO Capital Markets Splunk Sales capacity is a large driver of growth. Effective hiring, training, and retention of its sales force is a key determinant of new business. A significant portion of revenues come from sales through channel, particularly in the Europe, Middle East and Africa (EMEA) and Asia Pacific (APAC) regions and for sales to government agencies. Maintaining these relationships factors into the ability to grow. Other risks include competition, data privacy regulations, hosting failures, security breaches, acquisition integration, degradation in renewal rates, pricing pressure, extended sales cycles, changes to pricing models, and legal risk. Page 184 July 17, 2014

185 BMO Capital Markets Splunk Financial Models Exhibit 18.Income Statement Splunk Income Statement FY2013 FY2014 FY2014 FY2015E FY2015E FY2016E ($ in millions except per share) FY2013 Q1-Apr-13 Q2-Jul-13 Q3-Oct-13 Q4-Jan-14 FY2014 Q1-Apr-14 Q2-Jul-14E Q3-Oct-14E Q4-Jan-15E FY2015E FY2016E Products and license revenues $ $ $ $ $ $ $ $ $ $ $ $ Maintenance/services revenues $ $ $ $ $ $ $ $ $ $ $ $ Total Revenue $ $ $ $ $ $ $ $ $ $ $ $ Cost of revenue: product $ $ $ $ $ Cost of revenue: services $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Gross Profit $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Expenses R&D $ $ $ $ $ $ $ $ $ $ $ $ Sales & Marketing $ $ $ $ $ $ $ $ $ $ $ $ General & Administrative $ $ $ $ $ $ $ $ $ $ $ $ Total Non-GAAP Operating Expenses $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP Operating Income $ (1.398) $ (5.282) $ (0.761) $ $ $ (1.212) $ (3.573) $ (2.919) $ $ $ $ (+) Depreciation $ $ $ $ $ $ $ $ $ $ $ $ Non-GAAP EBITDA $ $ (3.857) $ $ $ $ $ (0.922) $ (0.692) $ $ $ $ Other income $ $ (0.033) $ (0.024) $ (0.975) $ (0.837) (1.869) $ $ (0.090) $ - $ - - $ $ (0.090) $ - Non-GAAP Earnings Bef.Taxes $ (1.246) $ (5.315) $ (0.785) $ (0.102) $ $ (3.081) $ (3.663) $ (2.919) $ $ $ $ Provision for Income Taxes $ $ $ $ (0.500) $ (0.263) $ $ $ - $ - $ - $ $ - Non-GAAP Tax Rate -57.2% -7.6% -46.5% 490.2% -8.4% -0.2% -15.3% 0.0% 0.0% 0.0% 337.6% 0.0% Non-GAAP Net Income (1) $ (1.959) $ (5.719) $ (1.150) $ $ $ (3.087) $ (4.225) $ (2.919) $ $ $ (0.396) $ Non-GAAP EPS $ (0.02) $ (0.06) $ (0.01) $ 0.00 $ 0.03 $ (0.03) $ (0.04) $ (0.02) $ 0.01 $ 0.04 $ (0.00) $ 0.08 Avg. Diluted Shares Outstanding (1) Non-GAAP excludes: amortization, employeer payroll tax, and stock-based comp. Expense Analysis (non-gaap): Cost of Revenues 89.8% 89.6% 90.2% 91.0% 92.0% 90.9% 88.9% 89.3% 90.4% 91.5% 90.2% 90.0% R&D 17.8% 19.7% 18.9% 17.9% 15.5% 17.7% 18.9% 18.4% 17.4% 15.0% 17.2% 16.7% Sales & Marketing 58.6% 64.2% 58.6% 59.5% 60.9% 60.6% 59.3% 60.6% 60.2% 61.6% 60.5% 60.0% General & Administrative 14.1% 14.9% 13.9% 12.5% 11.6% 13.0% 14.8% 13.5% 11.5% 10.6% 12.4% 11.4% Depreciation 2.3% 2.5% 2.2% 2.1% 2.2% 2.2% 3.1% 2.4% 2.3% 2.4% 2.5% 2.5% Margin Analysis (non-gaap): Product gross margin 99.5% 99.8% 99.8% 99.8% 99.9% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% 99.8% Maintenance/Services gross margin 69.1% 72.0% 72.7% 74.7% 74.6% 73.7% 72.6% 72.7% 74.7% 74.6% 73.7% 73.7% Gross Margin 89.8% 89.6% 90.2% 91.0% 92.0% 90.9% 88.9% 89.3% 90.4% 91.5% 90.2% 90.0% Operating Margin (total) -0.7% -9.2% -1.1% 1.1% 4.0% -0.4% -4.2% -3.1% 1.3% 4.2% 0.1% 1.9% EBITDA Margin 1.6% (6.7%) 1.0% 3.2% 6.2% 1.8% (1.1%) (.7%) 3.6% 6.6% 2.6% 4.4% Tax Rate (57.2%) (7.6%) (46.5%) 490.2% (8.4%) (.2%) (15.3%).0%.0%.0% 337.6%.0% Net Margin (1.0%) (10.0%) (1.7%).5% 3.4% (1.0%) (4.9%) (3.1%) 1.3% 4.2% (.1%) 1.9% Sequential Growth Rates (non-gaap): Total Revenue (12.3%) 16.9% 17.6% 27.1% (14.0%) 9.1% 11.5% 21.4% Gross Profit (13.3%) 17.7% 18.6% 28.5% (16.9%) 9.7% 12.9% 22.8% Operating Margin % (85.6%) (214.7%) 353.4% % (18.3%) (147.3%) 288.3% Net Income (291.4%) (79.9%) (134.6%) 750.3% (224.9%) (30.9%) (147.3%) 288.3% Year-Over-Year Growth (non-gaap): Total Revenue 64.5% 53.8% 50.3% 51.1% 53.2% 52.1% 50.2% 40.2% 32.9% 27.0% 35.8% 32.9% Gross Profit 63.3% 55.2% 50.5% 53.9% 55.5% 53.9% 49.0% 38.8% 32.1% 26.3% 34.8% 32.6% Sales & Marketing 58.9% 57.5% 49.3% 52.2% 67.8% 57.5% 38.7% 45.0% 34.6% 28.5% 35.6% 31.8% Operating expenses 57.4% 54.7% 49.8% 50.6% 57.3% 53.3% 41.4% 41.8% 31.8% 25.9% 34.1% 29.9% Operating Income (71.6%) 50.2% 12.9% (301.6%) 22.7% (13.3%) (32.4%) 283.6% 58.3% 35.6% (121.2%) % Net Income (62.3%) 54.1% 62.2% (175.5%) 13.3% 57.6% (26.1%) 153.8% 247.2% 58.6% (87.2%) (2683.7%) EPS (90.3%) 42.9% 48.8% (161.8%) 10.3% 14.2% (35.7%) 122.4% 241.0% 54.6% (88.1%) (2560.2%) Source: BMO Capital Markets Research, company documents. Page 185 July 17, 2014

186 BMO Capital Markets Splunk Exhibit 19.Balance Sheet ($ in millions) Q4-Jan-11 Q4-Jan-12 Q4-Jan-13 Q1-Apr-13 Q2-Jul-13 Q3-Oct-13 Q4-Jan-14 Q1-Apr-14 Q2-Jul-14E Q3-Oct-14E Q4-Jan-15E Q4-Jan-16E Assets FY2011 FY2012 FY2013 FY2014 FY2015E FY2016E Cash & Cash Equivalents $ $ $ $ $ $ $ $ $ $ $ $ Investments, current portion $ Accounts Receivable $ $ $ $ $ $ $ $ $ $ $ $ Prepaid expenses $ $ $ $ $ $ $ $ $ $ $ $ Total Current Assets $ $ $ $ $ $ $ $ $ $ $ $ 1, Investments, non-current portion $ $ $ $ $ PP&E, net $ $ $ $ $ $ $ $ $ $ $ $ Intangible assets, net $ $ $ $ $ $ $ Goodwill $ $ $ $ $ $ $ Other assets $ $ $ $ $ $ $ $ $ $ $ $ Total Assets $ $ $ $ $ $ $ 1, $ 1, $ 1, $ 1, $ 1, $ 1, Liabilities Accounts payable $ $ $ $ $ $ $ $ $ $ $ $ Accrued comp $ $ $ $ $ $ $ $ $ $ $ $ Accrued other $ $ $ $ $ $ $ $ $ $ $ $ Deferred Revenues $ $ $ $ $ $ $ $ $ $ $ $ Total Current Liabilities $ $ $ $ $ $ $ $ $ $ $ $ LT Deferred Revenues $ $ $ $ $ $ $ $ $ $ $ $ Other $ $ $ $ $ $ $ $ $ $ $ $ Total Liabilities $ $ $ $ $ $ $ $ $ $ $ $ Stockholders' equity $ $ (0.733) $ $ $ $ $ $ $ $ $ $ Total Liabilities + Stockholder's Equity $ $ $ $ $ $ $ 1, $ 1, $ 1, $ 1, $ 1, $ 1, Balance Sheet Summary Current Ratio Book Value Per Share ($0.03) $2.05 $2.33 $2.38 $2.12 $6.61 $6.68 $6.57 $6.50 $6.47 $6.24 Cash Per Share $2.97 $2.65 $3.25 $3.33 $2.98 $7.56 $5.69 $7.04 $6.99 $7.24 $7.65 Net Cash Per Share $1.43 $2.65 $3.25 $3.33 $2.98 $7.56 $5.69 $7.04 $6.99 $7.24 $7.65 Return On Equity 709.1% -0.9% -1.7% -1.9% -1.4% -0.8% -0.3% -0.5% -0.3% -0.1% 1.3% Return on Assets -25.3% -0.6% -1.1% -1.2% -0.9% -0.5% -0.2% -0.4% -0.2% 0.0% 0.9% Avg. Diluted Shares Outstanding Model Assumptions DSO (excluding deferred revenue) DSO (billings) Accounts Payable Days (off COGS) Accrued Expenses (as % of Sales) 37% 43% 37% 33% 39% 44% 35% 35% 35% 40% 40% Source: BMO Capital Markets Research, company documents. Page 186 July 17, 2014

187 BMO Capital Markets Splunk Exhibit 20.Cash Flow Statement FY2013 FY2014 FY2014 FY2015E FY2015E FY2016E in millions) FY2013 Q1-Apr-13 Q2-Jul-13 Q3-Oct-13 Q4-Jan-14 FY2014 Q1-Apr-14 Q2-Jul-14E Q3-Oct-14E Q4-Jan-15E FY2015E FY2016E ($ perating Activities non-gaap non-gaap non-gaap non-gaap non-gaap O t income $ (36.681) $ (16.134) $ (13.693) $ (16.550) $ (32.631) $ (79.008) $ (50.755) $ (2.919) $ $ $ (46.926) $ Ne epreciation and amortization $ $ $ $ $ $ $ $ $ $ $ $ D pairment of long-lived asset $ - $ $ - $ Im hange in fair value of preferred $ $ - $ - $ - tock-based comp $ $ $ $ $ $ $ $ $ - eferred income taxes $ - $ - $ (0.285) $ (0.285) $ - xcess tax benefit from stock options $ (0.462) $ (0.111) $ (0.157) $ (0.271) $ $ (0.351) $ (0.479) $ (0.479) $ - et change in assets and liabilitites, excl. acquisitions I C S D E N Accounts receivable, net $ (29.453) $ $ (2.830) $ (13.249) $ (29.353) $ (19.400) $ $ (1.386) $ (13.362) $ (37.597) $ (21.110) $ (31.859) Prepaid expenses $ (2.658) $ (0.298) $ (3.299) $ $ (0.558) $ (1.380) $ $ $ - Accounts payable and other liabilities $ $ $ (1.065) $ $ (0.096) $ $ $ $ $ $ $ Source: BMO Capital Markets Research, company documents. Accrued payroll and compensation $ $ (6.932) $ $ $ $ $ (13.757) $ (13.757) $ - Accrued expenses and other liabilities $ $ $ $ (0.159) $ (2.766) $ $ $ $ - Deferred revenue $ $ $ $ $ $ $ $ $ $ $ $ Other $ - $ - $ - $ - et Cash from Operations $ $ $ $ $ $ $ $ $ $ $ $ N vesting Activities In cquisitions, net of cash acquired $ - $ (8.958) $ (20.780) $ (29.738) $ - $ - hange in restricted cash $ - $ - $ - $ - $ - $ - urchases of investments $ - $ - $ ( ) $ ( ) $ - urchases of property and equipment $ (9.077) $ (1.263) $ (1.967) $ (4.035) $ (2.043) $ (9.308) $ (4.238) $ (5.000) $ (5.000) $ (5.500) $ (19.738) $ (20.000) A C P P Other $ $ - $ - $ - et Cash from Investing $ (8.563) $ (1.263) $ (1.967) $ (12.993) $ (22.823) $ (39.046) $ ( ) $ (5.000) $ (5.000) $ (5.500) $ ( ) $ (20.000) inancing Activities epayment of debt $ (2.289) $ - $ - $ - $ - $ - $ - N F R PO and follow on proceeds (net) $ $ - $ $ $ - $ - Proceeds from issuances of common stock $ $ $ $ $ $ - $ - Excess tax benefit from stock options $ $ $ $ $ (0.188) $ $ $ $ - Proceeds from exercise of stock options $ $ $ $ - $ $ $ $ $ - Taxes paid related to net share settlement of equity award s $ - $ (0.513) $ (2.239) $ (15.404) $ (18.156) $ - $ - Proceeds from restricted stock $ $ - $ - $ - $ - Net Cash from Financings $ $ $ $ $ $ $ $ - $ - $ - $ $ - Foreign Currency Impact $ $ $ (0.058) $ $ (0.019) $ $ $ $ - Net Increase / Decrease in Cash $ $ $ $ $ $ $ ( ) $ $ $ $ ( ) $ FCFE $ 37.6 $ 18.6 $ 4.3 $ 9.3 $ 32.4 $ 64.5 $ 14.7 $ 9.2 $ 4.4 $ 41.7 $ 70.0 $ 94.9 y-o-y 92% 91% 121% 51% 72% -21% 116% -53% 29% 8% 36% FCFF $ 37.3 $ 18.6 $ 4.3 $ 5.5 $ 33.3 $ 66.4 $ 14.8 $ 9.2 $ 4.4 $ 41.7 $ 69.8 $ 94.9 y-o-y 92% 104% 31% 56% 78% -21% 114% -20% 25% 5% 36% FCFF/Share $0.47 $0.18 $0.04 $0.05 $0.28 $0.60 $0.13 $0.08 $0.04 $0.34 $0.58 $0.76 FCFF Margin 19% 22% 17% 17% Page 187 July 17, 2014

188 BMO Capital Markets Splunk Other companies mentioned (priced as of the close on July 16, 2014) Amazon (AMZN, $355.90, Market Perform rated by Edward Williams) CommVault (CVLT, $48.59, Outperform) Demandware (DWRE, $60.17, Outperform) EMC (EMC, $27.01, Outperform rated by Keith Bachman) Google (GOOG, $582.66, Market Perform rated by Dan Salmon) Informatica (INFA, $33.59, Not Rated) International Business Machines (IBM, $192.99, Market Perform rated by Keith Bachman) Microsoft (MSFT, $44.08, Not Rated) Microstrategy (MSTR, $139.27, Not Rated) Oracle (ORCL, $40.26, Outperform) Qlik Technologies (QLIK, $21.28, Market Perform) SAP (SAP, $79.64, Market Perform) salesforce.com (CRM, $53.94, Outperform) Splunk (SPLK, $26.54, Market Perform) Tableau Software (DATA, $61.00, Not Rated) TIBCO Software (TIBX, $19.06, Not Rated) VMware (VMW, $94.05, Market Perform rated by Keith Bachman) Workday, Inc. (WDAY, $79.28, Market Perform) Page 188 July 17, 2014

189 BMO Capital Markets Splunk Tableau Software (DATA) Quarterly Price (US$) Target Price(US$) Share Price(US$) ) Mkt 2) NR DATA Relative to S&P 500 DATA Relative to Software DATA Relative to S&P 500 DATA Relative to Software Revenue / Share - (US$) Price / Revenue BMO 2014FY EPS ( Jun 14 = NA US$) First Call 2014FY Cons.EPS ( Jun 14 = US$) EPS (4 Qtr Trailing) - (US$) Price / Earnings BMO 2015FY EPS ( Jun 14 = NA US$) First Call 2015FY Cons.EPS ( Jun 14 = 0.11 US$) FYE EPS P/E DPS Yield% Payout BV P/B ROE (Dec.) US$ Hi - Lo US$ Hi - Lo % US$ Hi - Lo % Range*: na na NC >15 >15 Current* ND na na NA NA na DATA - Rating as of 16-May-13 = NR Date Rating Change Share Price 1 10-Jun-13 NR to Mkt $ Sep-13 Mkt to NR $72.10 * Current EPS is the 4 Quarter Trailing to Q2/2013. * Valuation metrics are based on high and low for the fiscal year. * Range indicates the valuation range for the period presented above. Last Price ( July 11, 2014): $61.02 Sources: IHS Global Insight, Thomson Reuters, BMO Capital Markets. Page 189 July 17, 2014

190 BMO Capital Markets Splunk QLIK TECHNOLOGIES INC (QLIK) Quarterly Price (US$) 45 Target Price(US$) Share Price(US$) ) OP 2) NR QLIK Relative to S&P 500 QLIK Relative to Software 140 QLIK Relative to S&P 500 QLIK Relative to Software Revenue / Share - (US$) Price / Revenue BMO 2014FY EPS ( Mar 14 = NA US$) First Call 2014FY Cons.EPS ( Mar 14 = 0.25 US$) EPS (4 Qtr Trailing) - (US$) Price / Earnings BMO 2015FY EPS ( Mar 14 = NA US$) First Call 2015FY Cons.EPS ( Mar 14 = 0.41 US$) FYE EPS P/E DPS Yield% Payout BV P/B ROE (Dec.) US$ Hi - Lo US$ Hi - Lo % US$ Hi - Lo % > > > > Range*: > > Current* NA NA na QLIK - Rating as of 9-May-11 = Mkt Date Rating Change Share Price 1 14-Jul-11 Mkt to OP $ Sep-13 OP to NR $34.84 * Current EPS is the 4 Quarter Trailing to Q2/2013. * Valuation metrics are based on high and low for the fiscal year. * Range indicates the valuation range for the period presented above. Last Price ( April 17, 2014): $25.48 Sources: IHS Global Insight, Thomson Reuters, BMO Capital Markets. Page 190 July 17, 2014

191 BMO Capital Markets Splunk Splunk Inc (SPLK) 100 Quarterly Price (US$) Target Price(US$) Share Price(US$) ) NR ) OP SPLK Relative to S&P 500 SPLK Relative to Software SPLK Relative to S&P 500 SPLK Relative to Software Revenue / Share - (US$) Price / Revenue BMO 2015FY EPS ( Apr 14 = NA US$) First Call 2015FY Cons.EPS ( Apr 14 = 0.00 US$) EPS (4 Qtr Trailing) - (US$) Price / Earnings BMO 2016FY EPS ( Apr 14 = NA US$) First Call 2016FY Cons.EPS ( Apr 14 = 0.14 US$) FYE EPS P/E DPS Yield% Payout BV P/B ROE (Jan.) US$ Hi - Lo US$ Hi - Lo % US$ Hi - Lo % 2012 na na 0.00 ND ND na 2.4 >15 > na na > na Range*: na na > Current* na NA NA na SPLK - Rating as of 18-Apr-12 = NR Date Rating Change Share Price 1 23-Jan-13 NR to OP $ Sep-13 OP to NR $59.37 * Current EPS is the 4 Quarter Trailing to Q2/2014. * Valuation metrics are based on high and low for the fiscal year. * Range indicates the valuation range for the period presented above. Last Price ( May 16, 2014): $43.76 Sources: IHS Global Insight, Thomson Reuters, BMO Capital Markets. Page 191 July 17, 2014

192 BMO Capital Markets Splunk IMPORTANT DISCLOSURES Analyst's Certification I, Joel P. Fishbein, Jr, hereby certify that the views expressed in this report accurately reflect my personal views about the subject securities or issuers. I also certify that no part of my compensation was, is, or will be, directly or indirectly, related to the specific recommendations or views expressed in this report. Analysts who prepared this report are compensated based upon (among other factors) the overall profitability of BMO Capital Markets and their affiliates, which includes the overall profitability of investment banking services. Compensation for research is based on effectiveness in generating new ideas and in communication of ideas to clients, performance of recommendations, accuracy of earnings estimates, and service to clients. Analysts employed by BMO Nesbitt Burns Inc. and/or BMO Capital Markets Ltd. are not registered as research analysts with FINRA. These analysts may not be associated persons of BMO Capital Markets Corp. and therefore may not be subject to the NASD Rule 2711 and NYSE Rule 472 restrictions on communications with a subject company, public appearances and trading securities held by a research analyst account. Company Specific Disclosure for DATA Methodology and Risks to Price Target/Valuation Methodology: Our price target is based on an assumed 8-10x EV/sales multiple on our out year estimates. Risks: Risks to our target include overly optimistic revenue expectations, lack of traction from new product initiatives, competition, and a muted margin expansion outlook. Company Specific Disclosure for QLIK Disclosure 9: BMO Capital Markets makes a market in this security. Methodology and Risks to Price Target/Valuation Methodology: Our price target is based on our DCF and comparative multiple analyses and equates to 2.6x our 2015 EV/sales estimate. Risks: Risks to our target include an unanticipated decline in worldwide IT spending and overly optimistic expectations for the BI software market. Company Specific Disclosure for SPLK Methodology and Risks to Price Target/Valuation Methodology: Our price target is based on an assumed 8-10x EV/sales multiple on our out year estimates. Risks: Risks to our target include an unanticipated decline in IT spending and overly optimistic market opportunity expectations. Distribution of Ratings (June 30, 2014) Rating BMOCM US BMOCM US BMOCM US BMOCM BMOCM Starmine Category BMO Rating Universe* IB Clients** IB Clients*** Universe**** IB Clients***** Universe Buy Outperform 44.1% 21.1% 67.5% 43.3% 58.6% 55.4% Hold Market Perform 50.9% 8.4% 31.3% 51.2% 39.9% 39.5% Sell Underperform 5.0% 3.4% 1.3% 5.5% 1.5% 5.1% * Reflects rating distribution of all companies covered by BMO Capital Markets Corp. equity research analysts. ** Reflects rating distribution of all companies from which BMO Capital Markets Corp. has received compensation for Investment Banking services as percentage within ratings category. *** Reflects rating distribution of all companies from which BMO Capital Markets Corp. has received compensation for Investment Banking services as percentage of Investment Banking clients. **** Reflects rating distribution of all companies covered by BMO Capital Markets equity research analysts. ***** Reflects rating distribution of all companies from which BMO Capital Markets has received compensation for Investment Banking services as percentage of Investment Banking clients. Rating and Sector Key (as of April 5, 2013): We use the following ratings system definitions: OP = Outperform - Forecast to outperform the analyst s coverage universe on a total return basis Mkt = Market Perform - Forecast to perform roughly in line with the analyst s coverage universe on a total return basis Und = Underperform - Forecast to underperform the analyst s coverage universe on a total return basis on a total return basis (S) = speculative investment; NR = No rating at this time; R = Restricted Dissemination of research is currently restricted. BMO Capital Markets' seven Top 15 lists guide investors to our best ideas according to different objectives (CDN Large Cap, CDN Small Cap, US Large Cap, US Small cap, Income, CDN Quant, and US Quant have replaced the Top Pick rating). Prior BMO Capital Markets Ratings System (January 4, 2010 April 4, 2013): Page 192 July 17, 2014

193 BMO Capital Markets Splunk Other Important Disclosures For Other Important Disclosures on the stocks discussed in this report, please go to or write to Editorial Department, BMO Capital Markets, 3 Times Square, New York, NY or Editorial Department, BMO Capital Markets, 1 First Canadian Place, Toronto, Ontario, M5X 1H3. Dissemination of Research BMO Capital Markets Equity Research is available via our website Institutional clients may also receive our research via Thomson Reuters, Bloomberg, FactSet, and Capital IQ. Research reports and other commentary are required to be simultaneously disseminated internally and externally to our clients. General Disclaimer BMO Capital Markets is a trade name used by the BMO Investment Banking Group, which includes the wholesale arm of Bank of Montreal and its subsidiaries BMO Nesbitt Burns Inc., BMO Capital Markets Ltd. in the U.K. and BMO Capital Markets Corp. in the U.S. BMO Nesbitt Burns Inc., BMO Capital Markets Ltd. and BMO Capital Markets Corp are affiliates. Bank of Montreal or its subsidiaries ( BMO Financial Group ) has lending arrangements with, or provide other remunerated services to, many issuers covered by BMO Capital Markets. The opinions, estimates and projections contained in this report are those of BMO Capital Markets as of the date of this report and are subject to change without notice. BMO Capital Markets endeavours to ensure that the contents have been compiled or derived from sources that we believe are reliable and contain information and opinions that are accurate and complete. However, BMO Capital Markets makes no representation or warranty, express or implied, in respect thereof, takes no responsibility for any errors and omissions contained herein and accepts no liability whatsoever for any loss arising from any use of, or reliance on, this report or its contents. Information may be available to BMO Capital Markets or its affiliates that is not reflected in this report. The information in this report is not intended to be used as the primary basis of investment decisions, and because of individual client objectives, should not be construed as advice designed to meet the particular investment needs of any investor. This material is for information purposes only and is not an offer to sell or the solicitation of an offer to buy any security. BMO Capital Markets or its affiliates will buy from or sell to customers the securities of issuers mentioned in this report on a principal basis. BMO Capital Markets or its affiliates, officers, directors or employees have a long or short position in many of the securities discussed herein, related securities or in options, futures or other derivative instruments based thereon. The reader should assume that BMO Capital Markets or its affiliates may have a conflict of interest and should not rely solely on this report in evaluating whether or not to buy or sell securities of issuers discussed herein. Additional Matters To Canadian Residents: BMO Nesbitt Burns Inc. furnishes this report to Canadian residents and accepts responsibility for the contents herein subject to the terms set out above. Any Canadian person wishing to effect transactions in any of the securities included in this report should do so through BMO Nesbitt Burns Inc. The following applies if this research was prepared in whole or in part by Andrew Breichmanas, Iain Reid, Tony Robson, David Round, Edward Sterck or Brendan Warn: This research is not prepared subject to Canadian disclosure requirements. This research is prepared by BMO Capital Markets Limited and subject to the regulations of the Financial Conduct Authority (FCA) in the United Kingdom. FCA regulations require that a firm providing research disclose its ownership interest in the issuer that is the subject of the research if it and its affiliates own 5% or more of the equity of the issuer. Canadian regulations require that a firm providing research disclose its ownership interest in the issuer that is the subject of the research if it and its affiliates own 1% or more of the equity of the issuer that is the subject of the research. Therefore BMO Capital Markets Limited will only disclose its and its affiliates ownership interest in the subject issuer if such ownership exceeds 5% of the equity of the issuer. To U.S. Residents: BMO Capital Markets Corp. furnishes this report to U.S. residents and accepts responsibility for the contents herein, except to the extent that it refers to securities of Bank of Montreal. Any U.S. person wishing to effect transactions in any security discussed herein should do so through BMO Capital Markets Corp. To U.K. Residents: In the UK this document is published by BMO Capital Markets Limited which is authorised and regulated by the Financial Conduct Authority. The contents hereof are intended solely for the use of, and may only be issued or passed on to, (I) persons who have professional experience in matters relating to investments falling within Article 19(5) of the Financial Services and Markets Act 2000 (Financial Promotion) Order 2005 (the Order ) or (II) high net worth entities falling within Article 49(2)(a) to (d) of the Order (all such persons together referred to as relevant persons ). The contents hereof are not intended for the use of and may not be issued or passed on to, retail clients. Unauthorized reproduction, distribution, transmission or publication without the prior written consent of BMO Capital Markets is strictly prohibited. Click here for data vendor disclosures when referenced within a BMO Capital Markets research document. Page 193 July 17, 2014

194 BMO Capital Markets Splunk ADDITIONAL INFORMATION IS AVAILABLE UPON REQUEST BMO Financial Group (NYSE, TSX: BMO) is an integrated financial services provider offering a range of retail banking, wealth management, and investment and corporate banking products. BMO serves Canadian retail clients through BMO Bank of Montreal and BMO Nesbitt Burns. In the United States, personal and commercial banking clients are served by BMO Harris Bank N.A. (Member FDIC). Investment and corporate banking services are provided in Canada and the US through BMO Capital Markets. BMO Capital Markets is a trade name used by BMO Financial Group for the wholesale banking businesses of Bank of Montreal, BMO Harris Bank N.A. (Member FDIC), BMO Ireland Plc, and Bank of Montreal (China) Co. Ltd. and the institutional broker dealer businesses of BMO Capital Markets Corp. (Member SIPC) and BMO Capital Markets GKST Inc. (Member SIPC) in the U.S., BMO Nesbitt Burns Inc. (Member Canadian Investor Protection Fund) in Canada, Europe and Asia, BMO Capital Markets Limited in Europe and Australia, and BMO Advisors Private Limited in India. Nesbitt Burns is a registered trademark of BMO Nesbitt Burns Corporation Limited, used under license. BMO Capital Markets is a trademark of Bank of Montreal, used under license. "BMO (M-Bar roundel symbol)" is a registered trademark of Bank of Montreal, used under license. Registered trademark of Bank of Montreal in the United States, Canada and elsewhere. TM Trademark Bank of Montreal COPYRIGHT 2014 BMO CAPITAL MARKETS CORP A member of BMO Financial Group Page 194 July 17, 2014

195 BMO Capital Markets Splunk Page 195 July 17, 2014

196 Equity Research Director of Canadian Equity Research Aine O Flynn, CFA Director of US Equity Research Christine Farkas, CFA Financials Canadian Banks Sohrab Movahedi US Banks Lana Chan Peter Winter Insurance Tom MacKinnon, FSA, FCIA Insurance/Non-Life Charles J. Sebaski Diversified Financials Atul Shah David J. Chiaverini, CFA Trading Venues/Exchanges Jillian Miller Financial Technology Paul Condra Real Estate Investment Trusts Heather C. Kirk, CFA Troy MacLean, CFA Paul E. Adornato, CFA Consumer Retail - Broadlines/Hardlines Wayne Hood Retail - Apparel & Specialty John D. Morris Staples/Discretionary Peter Sklar, CPA, CA Food Retail Kelly Bania Food & Ag Products Kenneth B. Zaslow, CFA Food & Beverage Amit Sharma, CFA Household & Personal Care Products Connie M. Maneaty Toys & Leisure Gerrick L. Johnson Auto Parts Peter Sklar, CPA, CA Services - Education Jeffrey M. Silber Special Situations Stephen MacLeod, CFA Materials Base Metals & Mining Tony Robson +44 (0) Aleksandra Bukacheva, CFA, MSc Edward Sterck +44 (0) Precious Metals & Minerals David Haughton Andrew Breichmanas, P.Eng. +44 (0) Andrew Kaip, P. Geo Brian Quast, P. Eng., JD Commodity Strategy Jessica Fung Chemicals & Fertilizers Joel Jackson, P.Eng., CFA Industrials Transportation & Aerospace Fadi Chamoun, CFA Diversified Industrials Charles D. Brady Bert Powell, CFA Machinery Joel Tiss Services - Equipment Distribution & E&C Bert Powell, CFA Services - Business Services Jeffrey M. Silber Tech / media / Telecom IT Hardware & IT Services Keith Bachman, CFA Communications Equipment Tim Long Information Technology Thanos Moschopoulos, CFA Semiconductors Ambrish Srivastava, Ph.D Software Joel P. Fishbein, Jr Telecom/Media/Cable Tim Casey, CFA Telecom Services Kevin Manning Media - Digital Entertainment and Internet ecommerce Edward S. Williams Media and Internet Marketing Daniel Salmon Energy & Utilities Integrateds Randy Ollenberger Iain Reid +44 (0) E&P Jim Byrne, P. Eng., CFA Jared Dziuba, CFA Phillip Jungwirth, CFA Dan McSpirit David Round +44 (0) Gordon Tait, CFA Brendan Warn +44 (0) Oil & Gas: Services & Equipment Alan Laws, CFA Mike Mazar, CPA, CA, CFA Electric Utilities & Independent Power Michael S. Worms Ben Pham, CFA Pipelines Carl Kirst, CFA Healthcare Biotechnology Jim Birchenough, M.D Medical Technology Joanne K. Wuensch Managed Care/Facilities Jennifer Lynch Pharmaceuticals Alex Arfaei Specialty Pharmaceuticals David Maris Macro Investment Strategy Brian G. Belski Economics Douglas Porter, CFA Michael Gregory, CFA Earl Sweet Quantitative/Technical Mark Steele Ken Hartviksen, CFA Qin Lu Small Caps Willa Hoffmann, CFA First Canadian Place, P.O. Box 150, Toronto, ON M5X 1H Tour McGill College, 1501 McGill College Ave., Suite 2800, Montreal, PQ H3A 3M8 900, 525-8th Avenue S.W., Calgary, AB. T2P 1G1 95 Queen Victoria Street, London, U.K., EC4V 4HG 3 Times Square, 7th Floor, New York, NY Tower Place, 3348 Peachtree Road, NE, Suite 1430, Atlanta, GA High Street, 26th Floor, Boston, MA th Street, Suite 1704S, South Tower, Denver, CO Louisiana Street, Suite 2100, Houston, TX One Market, Spear Tower, Suite 1515, San Francisco, CA S. LaSalle Street, Chicago, IL 60603