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. joel.fishbein@bmo.com (212) Brett Fodero BMO Capital Markets Corp. brett.fodero@bmo.com (212) Edward Parker BMO Capital Markets Corp. edward.parker@bmo.com (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 / brett.fodero@bmo.com/edward.parker@bmo.com 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