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1 INDEPENDENT TECHNOLOGY RESEARCH SECTOR UPDATE NOV 2013 SOFTWARE Big Data Analytics EXTRACTING INSIGHTS FROM EXABYTES Analytics is entering a new era Amidst the hype surrounding Big Data, a perfect storm of major trends is allowing organisations across all industries to combine advanced Analytics with new sources of external data to extract valuable insights capable of monetisation. We believe the Analytics market is entering a new era, where technology is capable of supporting data-driven business, in real-time. Big Data technologies reaching enterprise-readiness A wave of innovative technology is powering a new generation of Analytics solutions. This wave spans software, hardware, and even entire computing paradigms, all of which are now reaching enterprise-readiness, and hence creating the conditions for mainstream adoption. Hadoop, in-memory computing, and cloud Infrastructure-as-a-Service are key Analytics enablers where enterprise adoption is currently limited but the potential is dramatic. Predictive Analytics and Visualisation taking centre stage We see several vendors in Europe well positioned in the Big Data software market which is forecast to grow at a 56% CAGR ( ) to reach $7.4bn by Our interviews with over 30 technology vendors, their customers, and investors reveal that Predictive Analytics and Visualisation are the top two emerging sub-segments. $1.4bn of funding in the last 12 months and growing Big Data Analytics is one of the hottest sectors globally for VC and growth capital investment. Investment activity has grown over 200% year-over-year with $1.4bn of capital deployed into the sector in the last twelve months alone. Another phase of Analytics M&A is underway We believe the market is entering a new phase of consolidation as major Business Intelligence vendors pursue inorganic growth and seek to acquire Big Data Analytics capabilities. HUGH CAMPBELL London: ALEXIS SCORER London: WILL SHELDON London: Important disclosures appear at the back of this report GP Bullhound LLP is authorised and regulated by the Financial Conduct Authority and the Prudential Regulation Authority

2 Table of Contents The Evolution of Business Intelligence and Analytics... 2 Introduction... 2 Defining Big Data... 3 First Phase of Analytics: Business Intelligence... 3 Comparing BI to Big Data... 5 Second Phase of Analytics: Big Data comes of age... 6 How big is Big?... 7 Big Data expected to drive big value... 8 Market Size and Growth Analytics Enters a New Era Third phase of Analytics: Data-driven business Big Data Infrastructure: Now ready for large scale adoption Visualisation: Unlocking Analytics for business users Predictive Analytics: Analytics for the future Industry Landscape Investment and Acquisition Dynamics Investment activity reaching new heights M&A: Poised for a new wave of consolidation Selected Private Placements Selected M&A transactions Selected Company Profiles Analytics, Visualisation and Big Data Predictive Analytics Service Providers

3 THE EVOLUTION OF BUSINESS INTELLIGENCE AND ANALYTICS INTR OD UCTION The march of quantification, made possible by enormous new sources of data, will sweep through academia, business, and government. There is no area that is going to be untouched. Professor Gary King, Institute for Quantitative Social Science, Harvard University 1 While the concept of analysing data to derive insights in business is nothing intrinsically new, we believe a convergence of new technologies has recently ignited an Analytics revolution. Today s emerging wave of Analytics technology is widely expected to have a dramatic impact not only on businesses across all industries, but also has the potential to revolutionise healthcare and the significantly impact the public sector. We see several companies ideally poised to execute on this Analytics revolution. Analytics as an area of technology encompasses hardware, to physically store data and power computations, software, to intelligently store and analyse data on given hardware, and services, to help users leverage the former two. This report focuses on the software segment of the market; while much of the buzz around Big Data software has been on the solutions for storing data (e.g. Hadoop, NoSQL), we believe that software to analyse Big Data, particularly to give predictive insights in real-time, will become a key area of growth and investment. The Analytics software market is entering its third phase. The initial phase, running from the 1950s to the mid 00s, saw the emergence of Business Intelligence solutions designed to deliver reports based on internal business data. The second phase, starting around 2008, was when Big Data as a concept entered the corporate mind-set, and saw companies predominantly concerned with capturing and analysing more data than ever before. The third phase, starting now, is about driving predictive insights in real-time. 2 E X H I B I T 1 A N A L Y T I C S : E N T E R I N G I T S T H I R D P H A S E 1 Lohr S. The Age of Big Data New York Times (February 2012) 2 Davenport T. Preparing for Analytics 3.0 The Wall Street Journal (February 2013) 2 GP Bullhound LLP

4 Source: GP Bullhound, Tom Davenport DEFIN IN G BIG DA TA Big Data has now become a ubiquitous term but remains ambiguous and no universally agreed upon definition currently exists. Most definitions refer to the dimensions of data volume, velocity, and variety first outlined in 2001 paper by META Group (now Gartner) analyst Doug Laney. 3 Sufficient magnitude in any one of these dimensions sheer amount of data; speed with which it arrives; and in particular, data types beyond traditional structured data can create a Big Data challenge, requiring advanced technology. Companies have been managing large volumes of data for some time so Big Data challenges typically relate to more than just raw data volume. Analytics is a critical component of Big Data, and our preferred definition was advanced by researchers at the University of St Andrews, who surveyed and distilled the many Big Data definitions to have gained traction into: The storage and analysis of large and or complex data sets using a series of techniques including, but not limited to: NoSQL, MapReduce and machine learning. 4 Whilst Big Data will continue to mean different things to different people and evolve over time, the fixation on the volume aspect of Big Data helps explain why much of the initial hype has focused on the technologies relating to data storage and processing. We are now seeing a major shift in emphasis towards the analysis aspects of Big Data. To understand current trends and the possible future direction in Big Data Analytics, it is helpful to explore a brief history of Business Intelligence and computing as a decision making tool within business. FIRST PHASE OF ANALYTICS: B USINESS INTELLI GENCE Analytics have been used in business since the late 19th century when American industrialist Frederick Winslow Taylor began conducting experiments on metal-cutting machinery to improve efficiency at the Midvale Steel Company in Pennsylvania. 5 Henry Ford is said to have carried out time management analysis on the Model T assembly line when it was first produced in It was not until the middle of the twentieth century and the advent of computing that Analytics began to command more attention. Decision Support Systems evolved as a field of study from research at the Carnegie Institute of Technology and MIT in the late 1950s and early 1960s. 6 In the mid 1960 s, Scott Morton a Scottish engineering student at Harvard Business School built what is thought to be the first model-driven Decision Support System to help managers make business planning decisions. The research coincided with the development of second generation computing technology in the form of powerful mainframe computers such as the IBM 360 which made it cost-effective and practical to develop Management Information Systems (MIS) for large businesses. 7 3 Laney D. 3-D Data Management: Controlling Data Volume, Velocity and Variety Meta (2001) 4 Ward J. and Barker A Undefined By Data: A Survey of Big Data Definitions (2013) 5 Hounshell D. The Same Old Principles in the New Manufacturing Harvard Business Review (November 1988) 6 Power, D.J. A Brief History of Decision Support Systems (March 2007) 7 Ibid. 3 GP Bullhound LLP

5 Westinghouse Electric Company recognised the potential of the system to improve profitability and began experimenting with the system to coordinate production planning for laundry equipment. 8 Other pioneering projects in the 1960s included the Semi-Automated Business Research Environment (SABRE), a flight booking and tracking system built by IBM for American Airlines. The system led to the creation of the first modern customer loyalty programme and remains in operation to this day. Despite advances in computer technology in the 1970s and 1980s, processing power was limited and computer hardware remained expensive. It was common for medium-sized business to operate numerous large mainframe-based application systems with reporting programs that offered limited flexibility and required a computer programmer. 9 E X H I B I T 2 F I R S T P H A S E O F A N A L Y T I C S ( ) A L P H A B E T S O U P MIS Management Information Systems DSS Decision Support Systems ESS Executive Support Systems EDW Enterprise Data Warehouse BI Business Intelligence 60s 70s 80s 90s 00s Source: GP Bullhound By the early 1980s Decision Support Systems, and closely related Executive Support Systems, began to gain recognition as a new class of information system which could support managers across a variety of business functions and at different levels within organisations. Important and related technology evolution during this period included query and reporting tools, a move away from mainframes to client servers and PCs, and the adoption of Online Analytical Processing (OLAP) which empowered users to slice and dice data in meaningful ways. Business Objects and Hyperion were among companies offering data Analytics capabilities in response to demand from business users increasingly seeking direct access to data and user friendly tools for ad hoc queries. It was not until 1989 however that Gartner Analyst Howard Dresner, proposed Business Intelligence (BI) as an umbrella term to describe the concepts and methods to improve business decisionmaking by using fact-based support systems. It was another decade before the adoption of Business Intelligence became widespread across large companies, giving rise to an industry which has undergone significant consolidation and is estimated by Gartner to be worth $13.8 billion in Morton M. Reflections of Decision Support Pioneers (September 2007) 9 10 Gartner, (February 2013) 4 GP Bullhound LLP

6 COMPA RIN G BI TO B IG DA TA Analytics is a broad term so it is useful to draw a distinction between the Analytics associated with traditional BI, and the more advanced Big Data Analytics which we see as a fundamentally different approach. BI systems typically focus on answering the What, Where and When of business performance by providing information based on internally focused, operational data (e.g. ERP and CRM) in the form of reports and dashboards. A key limitation of traditional BI solutions is that they only allow users to analyse structured data, which significantly limits the amount and kinds of analysis that can be performed. Whilst standard business reports and OLAP-based BI Analytics can be very useful, they are reactive in that they inform users about past performance. By contrast, Big Data Analytics typically involves the combination of more advanced data mining and machine learning algorithms (e.g. optimisation and predictive modelling), data distributed over a cluster of computing nodes, and Data Visualisation tools which encourage data discovery. Big Data Analytics is forward looking and more concerned with answering the Why and How questions, and indeed revealing questions that were not previously considered relevant. Where BI deals in known unknowns, Big Data Analytics is better placed to reveal unknown unknowns. Whereas BI requires the data schema (essentially the column and row headings of a table) to be predefined to allow for queries along certain dimensions, Big Data Analytics can be performed on all types of raw data, and schemas are automatically generated as the data is read. Big Data Analytics also involves leaving the data where it resides and bringing the Analytics processing to the data rather than the other way round this becomes significant with large data volumes, where transporting data between systems can become very expensive. E X H I B I T 3 B I G D A T A V S. B U S I N E S S I N T E L L I G E N C E Source: Wikibon, GP Bullhound We expect that whilst BI and traditional database technologies will remain important components of enterprise IT infrastructure, Big Data technologies will capture a growing proportion of incremental IT spending and become an increasingly strategic focus for organisations across all industries. 5 GP Bullhound LLP

7 SECON D PHASE OF ANALYTICS: BIG DATA COMES OF A GE The explosion of unstructured data (captured from system logs, multimedia files, smart phones, sensors etc.) has exposed the limitations of traditional database technologies and Analytics tools which were designed to handle structured enterprise data. Companies seeking to gain valuable insights from this growing torrent of data are increasingly investing in new technologies such as Hadoop which allows large volumes of unstructured data to be stored and analysed at a fraction of the cost of traditional systems. The combination of these new technologies and advanced Analytics capable of providing broader and deeper insights is ushering in an era of Big Data Analytics. E X H I B I T 4 B I G D A T A A N A L Y T I C S A B L E T O H A N D L E U N S T R U C T U R E D D A T A Source: GP Bullhound Awareness of the Big Data phenomenon can be traced back to a team of computer scientists at Silicon Graphics (SGI) in the mid-1990s, and the first significant academic references emerged in the late 1990s. 11 Google released two landmark papers in 2003 and 2004 describing their distributed file system called GFS, and MapReduce, their distributed data processing platform which together underpinned the Google search engine. 12 The concepts described in these papers helped inspire two part-time developers to create a file system and processing framework that would serve as the basis for Hadoop, an open source software framework that changed the economics of large scale data Analytics by bringing massively parallel computing to commodity hardware. Hadoop really took off when one of these developers, Doug Cutting (after whose son s toy elephant, Hadoop is named), joined Yahoo! in 2006 (For more information on Hadoop, see p.13.) In April 2008, Hadoop became the fastest system for sorting a terabyte of data, breaking the world record to sort a terabyte of data in just under three and a half minutes using a 910-node cluster. 13 In the last decade, Hadoop has evolved from a research project to become a widely adopted open industry standard for distributed data processing which now underpins many of the world s largest internet businesses. The term Big Data appeared in The Economist for the first time in early 2010 and McKinsey published an influential report on the subject in May Google search trends indicate that Big Data gained popularity at around the same time, and the concept entered the mainstream in 2012, featuring as a topic at the World Economic Forum in Davos that year Diebold F. On the Origin(s) and Development of the Term Big Data Penn Institute for Economic Research (September 2012) 12 Dean J. and Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters Google Inc. (December 2004) 13 O Malley O. TeraByte Sort on Apache Hadoop Yahoo! (May 2008) 14 Manyika, et al., Big data: The next frontier for innovation, competition, and productivity McKinsey Global Institute (May 2011) 15 Lohr S. How Big Data Became so Big" New York Times (August 2012) 6 GP Bullhound LLP

8 HOW BIG IS BIG? Every day, 3 times per second, we produce the equivalent amount of data that the Library of Congress has in its entire print collection. Most of it is...irrelevant noise. So unless you have good techniques for filtering and processing the information, you re going to get into trouble. Nate Silver, Statistician At the time of writing, Facebook claims to have the largest Hadoop cluster in the world based on storage capacity, with over 100 PB of storage over 2,000 nodes, growing by roughly half a PB per day. 16 To put this in context, a single petabyte (PB) can store enough mp3 music files to play continuously for 2,000 years. Every minute, 208,300 photos are uploaded to Facebook, bringing the total number of photos uploaded to the platform to 240 billion. E X H I B I T 5 A M I N U T E I N T H E L I F E O F T H E I N T E R N E T 208,000 photos uploaded 350,000 tweets 100 hours of video uploaded 120 new accounts 3.5 million search queries $118,000 in revenue Source: GP Bullhound Every day an average of 500 million messages are posted on Twitter and over 1 billion files are uploaded to Dropbox. IBM estimates that we are now creating 2.5 billion gigabytes of data every day; as much as 90% of the data which currently exists, ranging from digital pictures and videos, to social media posts and remote sensor data, was created in the last two years alone. 17 The digital universe is doubling every two years and IDC estimate that by 2020 the digital universe will be 40,000 exabytes or 40 trillion gigabytes. 18 The vast majority of new data being generated is unstructured data and IDC estimate that only 0.5% is currently being analysed. The rapid adoption of smartphones and connected devices is having a major impact on the volume of data traffic crossing the network. Intel estimates that the number of networked devices equalled the world s population in This number is expected to reach double the world s population by 2015, by which point it would take an estimated five years to view all the video content crossing IP networks every second. 19 The range of connected devices is beginning to extend beyond phones and tablets to cars and a wide range of connected devices this phenomenon is termed the internet of things and will contribute to the ever growing quantities of data IBM Big Data Success Stories (Oct 2011) 18 Gantz J. The Digital Universe in 2020 IDC (December 2012) 19 Temple K. What Happens in an Internet Minute, Intel (March 2012) 7 GP Bullhound LLP

9 E X H I B I T 6 T H E S C A L E O F B IG D A T A (1 E X A B Y T E = 1 T R I L L I O N M E G A B Y T E S ) 40,000 (Exabytes) 30,000 20, CAGR: 45% Unstructured data 30,000 Exabytes by ,000 0 Structured data 10,000 Exabytes by 2020 Source: GP Bullhound, IDC BIG DA TA E XPECTED TO DRIV E BIG VALUE Many industries will be significantly impacted by Big Data Analytics. Although this technology revolution remains in its early stages, we believe that Big Data Analytics has the potential to unleash a wave of productivity and efficiency gains across virtually all industries, and have a significant impact on the economy and society as a whole. In a recent report exploring game changers for the US economy, the McKinsey Global Institute (MGI) identified Big Data Analytics as one of the top five catalysts for both economic growth and increasing competitiveness and productivity beyond The report estimates that the widespread adoption of Big Data Analytics in retail and manufacturing alone could contribute an additional $325 billion to US GDP by 2020, whilst delivering $285 billion in productivity gains in the health care and government sectors. 20 Major retailers have been handling large volumes of data for many years and the industry has been an early adopter of Big Data technologies. In a well-publicised and controversial case from 2012, the US retail group Target successfully used historical transaction data to assign each shopper a pregnancy prediction score in order to offer baby product promotions to the right customers at the right time. 21 US retailer Walmart handles over a million customer transactions an hour which are stored in databases estimated to contain more than 2.5 petabytes of data. The company was able to use Big Data Analytics to drive an estimated 10-15% increase in completed online sales equating to over $1 billion in incremental revenue. 22 Dunnhumby, the marketing services firm majority owned by Tesco, analyses data drawn from over 350 million people including Tesco Clubcard holders, to help the retail giant target promotions more effectively and improve customers' retail and brand experience. 20 Lund S. et al MGI, Game changers: Five opportunities for US growth and renewal McKinsey Global Institute (July 2013) 21 Duhigg C. How Companies Learn Your Secrets New York Times (February 2012) 22 Romanov A. Putting a Dollar Value on Big Data Insights Wired (July 2013) 8 GP Bullhound LLP

10 E X H I B I T 7 B I G D A T A A N A L Y T I C S I N A C T I O N Marketing Analytics Customer Analytics Web Analytics Fraud & Risk Analytics Operational Analytics Optimising campaigns by analysing higher volumes of granular data like clickstream and weather data Product & Pricing optimisation by analysing large volumes of data to assess likely impact of changes Online retailers use Hadoop to recommend products and services based on user profile analysis and behavioural Analytics Big Data technologies optimise the entire customer experience based on insights from analysing data across a variety of channels Machine Learning algorithms are used to identify and rank individuals with most influence for a particular topic Advanced text Analytics tools analyse unstructured data from sites such as Twitter and Facebook to determine sentiment Businesses are analysing terabytes of data from forums associated with hackers to predict and detect financial fraud and identity theft Financial institutions analyse large volumes of transaction data to determine exposure of financial assets and score potential customers for risk Companies are analysing the wealth of information collected by business systems and external sources to optimise business processes Process modelling and simulation allows companies to assess the impact of complex operational changes before they are implemented Source: Big Data Partnership, GP Bullhound Although the travel industry has also had access to large volumes of structured transactional data for many years, online travel companies are now using Big Data Analytics to improve the performance of complex product searches. Edinburgh-based travel search site Skyscanner, which recently received investment from Sequoia Capital, uses Big Data Analytics to process over 1 billion flight prices each day, generating $3.5 billion in flights bookings in last 12 months alone. Recent advances in genomics are highlighting how Big Data Analytics could transform the treatment of cancers by pinpointing critical gene mutations in order to develop more effective and targeted therapies. The cost of sequencing the first human genome was approximately $3 billion and the project took several international institutions, hundreds of researchers and 13 years to complete. Over the last decade, the cost of sequencing a human genome has dropped from close to $100 million dollars in 2001 to just a few thousand dollars and can now be performed in a matter of days. 23 Genome sequencing costs are now dropping at four times the rate of Moore's law and we are rapidly approaching the $1,000 genome (Exhibit 8). A key challenge to the development of these more advanced treatments is the need to store and analyse the enormous volumes of sequencing data which is now being created at an astonishing rate 24. A number of start-ups are developing Big Data platforms to offer scalable and high performance genomic data analysis, including Bina Technologies which recently launched an on-demand solution that can analyse a whole human genome in a little less than four hours Resnick R. Implications of exponential growth of global whole genome sequencing capacity Genomequest (July 2010) 9 GP Bullhound LLP

11 In the public sector, the Open Data Institute founded by Sir Tim Berners-Lee and Artificial Intelligence pioneer Sir Nigel Shadbolt, is promoting innovative uses of Open Data to help address social, environmental and economic challenges. By democratising key data sets (e.g. maps, national surveys), we believe such initiatives can play an important role in reducing barriers to entry and catalysing innovation around Analytics. A recently published report by the McKinsey Global Institute estimates that Open data can help unlock $3.2 per year in economic value across seven domains which include education, transportation and healthcare. 25 E X H I B I T 8 C O S T P E R G E N O M E ( L O G A R I T H M I C S C A L E ) $100,000,000 $10,000,000 $1,000,000 $100,000 $10,000 $1, Source: GP Bullhound, NHGRI Ultimately, we believe the last 50 years of BI history, and more recently the emergence of the Big Data phenomenon, has been a prelude to the era the world is now entering, where Big Data Analytics will emerge as a critical part of the decision making process for leading businesses across all sectors. 25 Manyika J. et al Open data: Unlocking innovation and performance with liquid information McKinsey Global Institute (October 2013) 10 GP Bullhound LLP

12 US$ bn GP BULLHOUND BIG DATA ANALYTICS EXTRACTING INSIGHTS FROM EXABYTES MARKET SIZE AND GROWTH The Big Data market is showing strong growth across software, services and hardware. Industry analysts are forecasting an aggregate compound annual growth rate (CAGR) of between 30-40% over the next 5 years, with the software sub-sector at the high end of that range. Within this growth, the Big Data software sector including both applications (e.g. visualisation and predictive tools) and infrastructure software (e.g. databases and middleware) is forecast to quintuple in size by 2017, resulting in a Big Data software market worth $12.2bn in 2017, up from $2.2bn in This quantum of $12bn is approximately equal to the entire Business Intelligence software market today, giving a startling sense of how pervasive Big Data software is likely to become in the medium term. E X H I B I T 9 B I G D A T A A $ 5 0 BN M A R K E T B Y Software Hardware Services Source: Wikibon, GP Bullhound Drilling into the software segment of the Big Data market, we expect that applications (analytical and transactional), as opposed to infrastructure software and databases, will see the highest levels of growth. While applications currently represent 44% of the Big Data software segment, Wikibon expects this share to increase to 60%+ by 2017, growing at a 56% CAGR ( ) to $7.4bn in E X H I B I T 10 B IG D ATA S O F T W A R E A P P L I C A T I O N S T O B E C O M E T H E L A R G E S T S E G M E N T $1.0bn $7.4bn (US$bn) Infrastructure Software SQL Databases NoSQL Databases Applications Source: Wikibon, GP Bullhound 26 Kelly, J, et al Big Data Market Size and Vendor Revenues Wikibon (February 2013) 11 GP Bullhound LLP

13 ANALYTICS ENTERS A NEW ERA T HIRD PHASE OF ANALYTICS: DATA-DRIV EN BUSINESS Organizational judgment is in the midst of a fundamental change from a reliance on a leader s gut instinct to increasingly data-based Analytics Erik Brynjolfsson, Director at the MIT Centre for Digital Business Organisations across a number of industries are increasingly investing in the technologies and capabilities required to leverage the growing volume and variety of data to make better decisions. According to a recent survey by Gartner, 64% of organisations are investing or planning to invest in Big Data technology compared with 58% in Although companies have been capturing growing volumes of data for many years, it is only relatively recently that a confluence of trends has made it economically viable to store and analyse large volumes of unstructured data in meaningful ways. These trends include the emergence of innovative storage and processing architectures such as Hadoop, and next generation non-relational and often opensource databases which are commonly referred to as NoSQL. Other trends include the exponential increases in computational power, declining costs of memory, and cloud computing (e.g. Amazon Elastic MapReduce). MIT research suggests that companies embedding data-driven decision making within their operations demonstrate higher productivity and Return on Equity than their peers. 28 Whilst much of the early Big Data investments and media focus has been on Big Data storage and processing technologies, attention is now gravitating rapidly toward the analytical software tools and platforms required to drive value creation from Big Data. As Professor Gary King at the Harvard Institute for Quantitative Social Science recently noted: "Big data isn't about the data. It's about Analytics." 29 BIG DA TA INFRAS TR UCTURE: NOW READY FOR LARGE S CALE ADOP TION A wave of innovative technology is powering the new generation of Analytics solutions. This wave spans software, hardware, and even entire computing paradigms, all of which are now reaching enterprisereadiness, and hence creating a perfect storm for mainstream adoption. Software Innovations: Hadoop; In-memory databases; NoSQL databases Hardware Innovations: Solid-state drives and DRAM; Multicore CPUs; Fibre optics Computing Paradigm Shifts: Cloud computing (IaaS/PaaS/SaaS); Open source 27 Kart L. et al Big Data Adoption in 2013 Shows Substance Behind the Hype Gartner (Sep 2013) 28 Bryjolfsson E. et al Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? MIT (January 2012) 29 King G Big Data is Not About the Data! NEAI (May ) 12 GP Bullhound LLP

14 Key Players Why it s important What it is GP BULLHOUND BIG DATA ANALYTICS EXTRACTING INSIGHTS FROM EXABYTES The following table picks out our Top 5 Big Data Technologies and summarises what they are, why they are important, and who the key players are globally. E X H I B I T 11 T OP 5 B IG D A T A T E C H N O L O G I E S Hadoop Cloud Computing Open Source NoSQL In-memory Databases Software framework for distributed data storage and computing Computing paradigm to leverage economies of scale in computing infrastructure Software development paradigm which advocates cooperation and openness Software for storing data; a different approach to traditional relational databases Software for storing data in DRAM rather than on mechanical HDDs Big Data infrastructure solution: highly scalable, and accommodates unstructured data Reduces capex requirements for enterprises wanting to analyse Big Data; can scale up/down fast Pools the brainpower of more developers for the creation and testing of software Allows for more data types (including unstructured ), and large data volumes Leverages speed advantage (c100,00x) of DRAM over HDDs much faster analytics Cloudera, HortonWorks, MapR, Amazon Web Services Amazon Web Services, Microsoft Azure, Google, Rackspace, IBM Apache Software Foundation, Open Source Software Institute Cassandra, Mongo DB, HBase, Riak, Couchbase, Neo4j SAP HANA, VoltDB, MemSQL, GridGain, Apache Spark Source: GP Bullhound Of all of the innovations listed above, Hadoop is perhaps the most important for the on-going revolution in Big Data Analytics. Drawing inspiration from two research papers published by Google referred to in the previous section, Hadoop s creators sought to tackle the problem of how to store and analyse large volumes of internet data. Their solution to the problem of Big Data was to scale out the size of the computer system horizontally using many commodity computers and develop software to harness the combined compute power and storage. Specifically, Hadoop combines a file storage system ( HDFS ) to store data (including unstructured data) in a distributed fashion across all the machines (or nodes ) in the system (or cluster ), with a method for mapping out the computing operations necessary for conducting data analysis across those machines ( Map Reduce ). This combination of HDFS and Map Reduce is the backbone of the Hadoop framework the scalability and resilience of the solution is evidenced by companies like Yahoo!, whose biggest single Hadoop cluster comprises over 4,500 nodes. We expect Hadoop adoption to increase and total cost of ownership (TCO) to continue to decline. Hadoop is part of the Apache Software Foundation and is hence open source. This means that anyone can contribute to developing the code base and any enterprise can download the code under a free licence to implement Hadoop for their own data. This has also fuelled adoption, since the combination of free software licences and commodity hardware requirements potentially results in significantly lower TCO than 13 GP Bullhound LLP

15 proprietary Big Data systems such as HP Vertica or the Oracle Big Data Appliance. Indeed, at the Hadoop Summit in San Jose in June 2013, MapR, a leading Hadoop software and services provider, asserted that Hadoop can be up to 50x cheaper than alternatives for storing large amounts of data 30. We believe that incumbent vendors are responding, and that the cost per TB benefit of a Hadoop data management system has narrowed to around 10x. E X H I B I T 12 H A D O O P P R I C I N G A D V A N T A G E O N A P E R T E R A B Y T E B A S I S Hadoop Netezza Exadata Extreme Data Appliance (1650) Cost / Terabyte $333 $10,000 $14,000 $16,500* Hadoop Benefit 30x saving 42x saving 50x saving Source: MapR (June 2013); * Teradata has since launched a new Extreme Data Appliance (1700) at $2,000/TB in October 2013 Despite this advantage, early versions of Hadoop have required PhD-levels of computing knowledge, often necessitating costly new hires or consultants to bring the necessary skill set into the organisation. However, we are already starting to see increased ease of use come through with the latest iterations of Hadoop. For example, Hadoop 2.0, which has just been released, allows for other processing algorithms besides MapReduce, which has to date been the most challenging aspect of Hadoop to develop code for. Given the immaturity of the Big Data infrastructure market, fragmentation is high. For instance, the Apache wiki site lists 29 different companies offering their own twist on (or distribution ) of Hadoop 31. Companies like MapR, Cloudera and Hortonworks are focused on developing enterprise-grade Hadoop distributions, with features including data protection, disaster recovery, and support for heterogeneous hardware clustering. In addition, beyond the core Hadoop framework, there is a broad array of NoSQL databases ranging from Cassandra to Voldemort offering an alternative means of storing and analysing Big Data, particularly for real-time analysis, for which Hadoop is not designed. We see graph databases (e.g. Neo4j from Swedish company Neo Technologies) as a particularly interesting subset of these, which have the potential to drive new applications based on alternative data relationships. E X H I B I T 1 3 A F R A G M E N T E D S P E C T R U M O F N O S Q L D A T A B A S E S Source: GP Bullhound GP Bullhound LLP

16 We expect the Big Data infrastructure technology segment to remain one of the most vibrant and innovative areas in tech over the next three years. Three themes we see as likely are: 1. Open source infrastructure solutions becoming increasingly ubiquitous This standardisation places greater emphasis on the Analytics software as the locus of competitive advantage for enterprises. Leading companies in the Hadoop community such as Cloudera, HortonWorks, and MapR are spearheading improvements in the platform. 2. SQL arriving on Hadoop: Most analytic queries today are written in SQL, a coding language ideally suited to analysing data stored in traditional databases. There are 30+ products and open-source projects underway to bring SQL, or SQL-esque coding to Big Data sets in Hadoop 32 (e.g. Hive, Drill, Teradata Aster), but we expect that this technology will mature over the next 18 months, helping enterprises to transition away from the traditional data warehouse world. 3. Convergence on interactive Big Data Analytics (Exhibit 14): There are currently at least three significant projects underway to improve the speed of Hadoop analytical querying to interactive (<1 minute) levels: Hortonworks Stinger Initiative, Cloudera s Impala, and the Apache Drill framework, sponsored by MapR. Similarly, there is a move from some real-time NoSQL databases (e.g. Cassandra) to integrate more deeply with Hadoop for deep-dive interactive workloads. Separately, Twitter has just open-sourced a technology called Summingbird for streaming MapReduce in real time using a hybrid system of Hadoop and a NoSQL real-time data store 33, which we believe could see widespread adoption over time. There are also some non-hadoop based efforts towards interactive Big Data Analytics, such as Germany-based Parstream, which leverages GPUs to power massively parallel processing within a columnar data store. E X H I B I T 14 I N T E R A C T I V E B IG D A T A A N A L Y T I C S I S A K E Y N E W F R O N T I E R NoSQL Batch Interactive Real-time Timeframe Hours <1 minute <1 second User Data Scientist Data/Business/Operational Operational Analyst Use case Discovery/Reporting Very Broad Operational Intelligence Challenge Data Variety Deep Analysis at Speed Data Velocity Source: GP Bullhound 15 GP Bullhound LLP

17 VISUALISATI ON: UNL OCKING ANALYTICS FOR BUSINES S USERS In order to allow more users to extract actionable insights from large datasets, a number of businesses are combining these new infrastructure technologies with advanced Analytics. Visualisation, or Data Discovery, is a hot subsector of Analytics software which is democratising data Analytics through intuitive graphical user interfaces and rapid time to insights. Rather than having to write code to query the data, as was the case in last generation s business intelligence tools, Visualisation solutions allow users to filter and drill-down into large data sets through intuitive interfaces that even the C-Suite can use. E X H I B I T 1 5 D A T A V I S U A L I S A T I O N : T H E N A N D N O W Then Now Source: GP Bullhound, Ayasdi Data Visualisation is not new; the concept of displaying data in insightful ways has been around for several generations of Business Intelligence software over the last four decades. However, several emerging Visualisation solutions are taking usability and functionality to such new heights that we expect a dramatic uplift in the number of users of Analytics software globally. Forrester Research expect the number of information workers people who use a computer for at least one hour at work every day to increase from 615 million in 2013 to 865 million in 2016 (12% CAGR) 34. However, the number of information workers currently using Analytics tools as part of their job stands at only 17% today 35. We estimate this proportion could rise to one third over the next three years, thereby implying a 172% increase (40% CAGR) in the number of users of business intelligence tools like Visualisation to 285 million in With Visualisation tools typically charged for on a per user basis, this growth rate implies a compelling market opportunity (Exhibit 16). Tableau Software typifies what many Visualisation solutions are aiming for, by combining intuitive drag-anddrop graphical interfaces and easy to use data filters, with powerful back-end infrastructure technology for integrating different data sources. Individual pieces of analysis can then be combined into live dashboards, tracking the evolution of key metrics and trends. Tableau has garnered over 12,000 customers globally (adding over 2,000 year to date), via a multi-dimensional go-to-market strategy, which has led to viral 34 Gillett F. et al Info Workers Will Erase The Boundary Between Enterprise and Consumer Technologies Forrester (August 2012) 35 Forrsights Workforce Employee Survey, Q4 2012, Forrester (October 2012) 16 GP Bullhound LLP

18 adoption at the individual business line level. The long-term business model, with a normalised operating margin target of 20%, is also attractive for investors, as evidenced by Tableau s recent $250m IPO. E X H I B I T 16 W E E X P E C T A 172% I N C R E A S E I N U S E R S O F A N A L Y T I C S T O O L S B Y , Millions of People Information Workers Users of Analytics Tools Increase in Information Workers Increase in Analytics Penetration 2016 Source: GP Bullhound estimates, Forrester The leading startups in today s Visualisation segment are integrating advanced mathematics, such as machine learning and topology, with interactive and intuitive data visualisations. Moreover they seamlessly leverage the Big Data infrastructure innovations discussed in the previous section in order to accommodate dynamic and diverse Big Data sets. For instance, Ayasdi, a Palo Alto based Visualisation company founded by Stamford mathematicians, has developed a graphical visualisation platform for deep analysis in diverse verticals such as medical research, genomics and investment banking. One notable case study is the Netherlands Cancer Institute, who were able to identify, within minutes, new patient subtypes never before noticed in a 13 year old, well-studied dataset. Visualisation is also increasingly important in the context of the world embracing real-time data. Acunu, a London-based software company focussed on real-time Analytics, has developed an Analytics software platform which features live dashboards and instant query results. It is built around the open-source Cassandra NoSQL database, which is particularly suited for real-time applications with high data velocity, on top of which Acunu has created software to leverage Cassandra s data and power for Analytics. For example Acunu powers the Hailo taxi app s decision engine for which taxi driver to allocate to which prospective passenger, and also music streaming service MixCloud s cloud-based Analytics for understanding its user base and infrastructure demands. We expect Visualisation solutions to grow in importance within enterprises Analytics stack, to become the key front-end through which most other Analytics is channelled and interacted with. For example, Tableau lists over 40 leading Analytics companies as technology partners, including IBM, Google, Amazon Web Services, Hortonworks, and Teradata. QlikView, a competitor to Tableau boasts a similar list. These 17 GP Bullhound LLP

19 partnerships offer a win-win situation, in our view: Visualisation vendors get more users and increased stickiness from becoming the front end of choice for multiple Analytics sources (ERP data, social media data, CRM data, predictive models, website activity data, etc.); Analytics providers don not have to expend development resources competing in Visualisation in addition to their core offering, and potentially also gain new customers via the Visualisation providers large user base. For example, two companies we interviewed, Saplo (text Analytics) and DataSift (social media data), both spoke very positively of their partnerships with QlikView and Tableau, respectively, which involves configuring their data to output into their partners Visualisation environments. Three other trends we expect to emerge in the Visualisation segment include: 1. Continued move to mobile consumption. In practice this is likely to mean cloud-based Analytics solutions, with mobile front-end interfaces optimised for touch 2. Increased focus on collaboration functionality. We see greater scope for collaboration around visual Analytics, especially as the proportion of intelligence workers using Analytics tools increases; we expect this to result from leading Visualisation vendors broadening out their collaborative capabilities, rather than pure-play collaboration vendors taking share 3. Embedded Visualisation. We believe leading enterprise software vendors will increasingly embed their own Visualisation offerings into their existing platforms (e.g. embedded visualisations within SAP ERP), to attempt to stem the market share gains by independent Visualisation vendors like Tableau and QlikView Ultimately, we see little room for new entrants into the pure visualisation sector now, given the successful land-grab, both in terms of users and also technology partners, already effected by Tableau and others. PREDIC TIVE ANALYTI CS: ANALYTICS FOR THE FUT URE We believe the single most exciting frontier within Analytics software today is Predictive Analytics, the ability to infer likely future scenarios from data and therefore the optimal course of action today. While Predictive Analytics software has been available for 40 years thanks to companies such as SAS and SPSS (acquired by IBM in 2009), our discussions with vendors and investors revealed an emerging wave of solutions with the potential to dramatically boost the share of enterprises using Predictive Analytics from current estimated levels of circa 33%. 36 Specifically, five underlying factors we perceive within the Predictive buzz in the tech community are: 1. Real-time Predictive Analytics we believe one of the most compelling features of today s Predictive Analytics solutions is the ability to give real-time insights. As discussed above, several technologies are enabling this, including elastic cloud computing, in-memory databases, and NoSQL data stores GP Bullhound LLP

20 2. Democratisation new software packages to make Predictive Analytics easier than ever, and no longer the preserve of PhD data scientists. We would highlight Blue Yonder, a Germany-based Analytics software company, as an emerging European leader 3. Integration legacy BI vendors aiming to integrate predictive capabilities within their existing backwards-looking Analytics platforms, and indeed also within other applications (such as ERP); SAP s recently acquisition of KXEN stands as evidence for this 4. New mathematics Predictive Analytics was for decades a matter of non-linear regression. However, we see increasing interest in Predictive Analytics solutions leveraging cutting-edge mathematics such as fuzzy logic and neural networks, which promise better predictive capabilities 5. Big Data sets the performance of Predictive Analytics generally improves with larger historical data sets. Also, some new Big Data sets (e.g. twitter feeds) open up totally novel use cases. These five factors are enabling a vast array of potential applications for Predictive Analytics. Indeed, industry analysts estimate that the Predictive Analytics segment of the market is now worth $2bn, but will grow to over $5bn over the next 5 years 37. Our discussions with data scientists and leading vendors revealed that machine learning, a key part of Predictive Analytics, is witnessing less significant innovation than is happening in infrastructure solutions and front-end applications. Machine learning is a subset of Artificial Intelligence it is concerned with algorithms whose performance at a particular task improves as it gains experience at that task. For example, a telecoms company will have an algorithm for predicting which customers are likely to churn, based on several data inputs (e.g. remaining contract duration, number of calls to call centre etc.), which improves itself over time thanks to additional data points. E X H I B I T 17 P R E D I C T I V E A N A L Y T I C S I N T H E C O N T E X T O F O T H E R A N A L Y T I C S S O L U T I O N S Reporting low complexity limited business value Analysis Monitoring Prediction high complexity high business value What occurred? Why did it occur? What s occurring? What might occur? Key tech Query, reporting, & search tools OLAP & visualisation tools Dashboards and scorecards Predictive analytics Output example Revenue by region on a quarterly basis shows lower than expected revenue in one particular region Digging into the data reveals a low number of outbound sales calls were made in the underperforming region Live dashboard to show how many calls are being made by each operative in all call centres today Analysing past data, together with other data sets, predicts the best type of targets, and optimal time of day to call Source: GP Bullhound 37 Analysis /RPT GP Bullhound LLP

21 While some Predictive Analytics companies claim better results from more advanced algorithms (e.g. neural networks or fuzzy logic), most predictive applications today use a simple naïve Bayes probabilistic classifier. Most of the work to apply such algorithms to Big Data Hadoop environments has now been open sourced in the form of the Mahout code library. In our view, the key innovation for Predictive Analytics over the next few years will be democratising existing algorithms for business users in real-time, rather than creating ever more complicated mathematics. The following Exhibit outlines some examples of Predictive Analytics in action. E X H I B I T 18 P R E D I C T I V E A N A L Y T I C S I N P R A C T I C E Politics ecommerce Crime Prevention Networking Financial Risk Retail Automotive Insurance Manufacturing Gaming Obama's 2012 campaign hired over 50 analytics experts to predict what voters would be positively influenced by political campaigning by different methods (door knocking, calls, flyers etc.) Amazon uses Predictive Analytics on customer data to identify patterns in shopping behaviour to drive the "customers who bought this also bought " recommendation engine. 35% of Amazon s sales come from product recommendations Memphis PD employs Predictive Analytics to provide insights on where and when to deploy policing resources. Since Operation Blue CRUSH (Crime Reduction Utilizing Statistical History) was deployed citywide incidents of serious crime have fallen by 30% LinkedIn's predictive contact recommendation engine - "people you may know" - is considered internally to be "the most important data product we built UK-based Logical Glue's SmartLoan system helps Credit Referencing Agencies to improve default spotting by 9.7% when compared to older predictive models, thanks to cutting-edge mathematics such as fuzzy logic and neural networks Germany-based Blue Yonder's Predictive Analytics Suite is used by leading European retailers (e.g. Next) to evaluate customer data and identify optimum selections for forthcoming collections / catalogues, based on multivariate predictive demand analysis UK-based We Predict's Predictive Analytics platform, Indico, is being used by leading automotive OEM's to save c10% of warranty spend, by better predicting which claims are fraudulent and which auto components will cause most of the claims Intel uses Predictive Analytics to both optimise the manufacturing process for their CPUs and also to improve sales and marketing. Internal expectations are for a $30m manufacturing cost reduction and a $20m boost in revenue from optimised distribution in As the video games industry embraces a Free-to-Play (F2P) model for mobile and increasingly console games, publishers are turning to Predictive Analytics to discover how to drive in-game purchases and retention. Edinburgh-based Games Analytics provides an advanced real-time predictive solution used by several leading publishers Source: GP Bullhound, Predictive Analytics (Eric Siegel) 20 GP Bullhound LLP

22 Adoption of Predictive Analytics is not without challenges. Firstly, Predictive Analytics solutions need fuel, in the form of data, to deliver results. Some organisations lack sufficient data quality, for example disjointed data sets between different regional offices, websites and back office systems. For many, the main data problem at hand is one of volume, struggling with the Big Data issues of capturing and storing all the data that will inform insightful predictive analysis, including across social and geospatial sources. Secondly, even with data of sufficient quality and quantity, the Predictive Analytics solutions themselves often lead to adoption hurdles. Models quickly become so complex that agility and utility suffer, and many of the existing Predictive Analytics software tools used to derive the models are still the preserve of computing PhDs, rather than business analysts. Most data scientists we spoke to in data-centric technology companies are relying on open source offerings to code their own predictive models and machine learning algorithms. These open source tools have the benefit of being free (especially relevant given the high prices of proprietary Predictive Analytics solutions like SAS) and include R, a programming language well suited to Analytics, and Mahout, a code library for Analytics on the Apache Hadoop framework. We believe Predictive Analytics processes are currently highly labour-intensive, and consequently expect the next raft of software in this segment to target ease of use and time to value, especially with open source tools. Four examples we see already in the market today are: Blue Yonder, based in Germany, which just launched a non-labour-intensive SaaS Predictive Analytics platform, Forward Demand, for real-time stock optimisation in the retail vertical i4c Analytics, headquartered in Italy, which has developed a predictive platform which sits between the underlying algorithmic mechanics (either R, SPSS, SAS, or Matlab) and the end user. Inspired by challenges faced by customers in their previous business, a SPSS consultancy, i4c Analytics founders have developed a product set focussed on industry verticals, to allow for easier Predictive Analytics, driven by business users RapidMiner (formerly Rapid-I), founded in Germany, which produces the RapidMiner solution for predictive modelling in R with a fully graphical user interface (GUI) to allow for programming-free Predictive Analytics. RapidMiner was voted the most widely used Analytics tool in a 2013 survey on KDNuggets, the leading forum for the data mining community. We also spoke to Radoop, a Hungary-based start-up focussed on enabling RapidMiner for Hadoop-based implementations Revolution Analytics, which is based on R and claims to achieve the same enterprise-grade performance as SAS but at only 2% of the cost 38 thanks, in part, to its open source roots In summary, we believe that conditions are now in place for accelerating adoption of Predictive Analytics; the next few years are likely to see a more pronounced transition to data-driven decision making, and notably in real time, thanks to several key technological innovations. There are a host of start-ups, mostly in the US, jostling to capture this growing demand, and we believe that Predictive Analytics will be at the core of the next wave of consolidation in the ever-evolving BI market GP Bullhound LLP

23 INDUSTRY LANDSCAPE The Big Data Analytics market is fragmented with over 100 credible players. While most of the larger vendors are based in the US, we have identified several European companies capable of challenging on a global scale. E X H I B I T 19 E U R O P E A N B IG D A T A A N A L Y T I C S C H A L L E N G E R S Analytics and Visualisation Predictive Analytics Infrastructure Services / Other Source: GP Bullhound We have segmented the global market at a high level, shown in the following Market Map, which whilst not exhaustive, aims to capture most of the relevant companies globally. The companies featured are predominantly vendors offering a purchasable software solution to be used by others, rather than selling Analytics results derived using other vendors software. The different categories are defined as follows: Consolidators are the industry giants who have a history of acquiring key emerging technologies to maintain their position as one-stop-shops capable of meeting all demands from the world s largest enterprise customers. IDC estimate that the five largest vendors constituted 61% of the Analytics software market revenue in 2012 (Oracle, SAP, IBM, Microsoft, SAS) Ad/Media Applications are those Analytics vendors specifically targeting online advertising/retargeting Verticals are those vendors offering Analytics solutions mainly targeted at specific verticals, other than Advertising/Media Infrastructure are those infrastructure software vendors or open source projects which enable the infrastructure layer of data Analytics, e.g. storage, persistence, data warehousing, coding etc. Analytics and Visualisation is the core bucket for Analytics software applications which could be described as horizontal in that they offer a solution suitable for multiple industry verticals. Within this bucket there are some vendors more focused on Predictive Analytics (e.g. Blue Yonder), Visualisation (e.g. Tableau), and Big Data (e.g. Splunk), but many offer a range of services Service Providers are the companies offering a service around Big Data enablement essentially teams of advanced Analytics experts who work on a consultancy basis for organisations who lack Analytics skills in house 22 GP Bullhound LLP

24 Consolidators Ad/Media Applications Verticals Data Management Analytics and Visualisation Service Providers BIG DATA ANALYTICS GLOBAL MARKET MAP 23

25 INVESTMENT AND ACQUISITION DYNAMICS INVESTMENT ACTIVITY REA CHI NG NEW HEIGH TS We believe Big Data Analytics is one of the hottest sectors globally for VC investment. We have tracked $1.37bn of funding in the last twelve months alone, which represents an increase of 217% increase in capital invested over the previous period. Activity is increasing, with 19 deals in the last quarter. E X H I B I T 20 D A T A A N A L Y T I C S F U N D R A I S I N G B Y Q U A R T E R $1.37bn Funding in LTM 12 Avg Deals per Quarter +217% YoY Funding Growth ($ amount) $246m Avg Funding per Quarter 67 Deals in LTM Q2 13 Biggest Quarter ($ Funding) +181% YoY Deal Growth (Number of deals) Q3 13 Biggest Quarter (# of deals) Q3 11 Q4 11 Q1 12 Q2 12 Q3 12 Q4 12 Q1 13 Q2 13 Q3 13 Funding ($m) Deals Source: GP Bullhound Digging into the data reveals that US start-ups are typically much better funded than their European counterparts at every stage other than the seed round. Examining the total amount raised from seed to E rounds reveals that US start-ups are able to secure an average of 87% more funding than their European counterparts. Unsurprisingly, several initially European firms are relocating their headquarters to the US. E X H I B I T 21 A V E R A G E S I Z E O F D I F F E R E N T F U N D I N G R O U N D S E U R O P E V S. US (US$m) Europe US Seed A B C D E Source: GP Bullhound 24 GP Bullhound LLP

26 There is also clear appetite for large, later stage rounds, with recent examples include Mongo DB ($150m, Series F), and Palantir ($197m, Series H). Consequently, there are several start-ups which have now received over $100m in funding. Interestingly, these companies span different ways to play the Big Data Analytics theme, including pure services (Mu Sigma, and Opera Solutions), software/services (Palantir, Cloudera), infrastructure software (Mongo DB), and pure Analytics software (APT). E X H I B I T 22 B EST- F U N D E D B IG D A T A A N A L Y T I C S C O M P A N I E S ( T O T A L F U N D I N G, U S $ M ) Palantir MongoDB Mu Sigma Applied Predictive Technologies Cloudera Opera Solutions, LLC Source: GP Bullhound Some of the world s leading funds have been the most active investors in Big Data Analytics companies. Notable among the most active investors include Accel Partners which recently launched a $100m fund dedicated to data-driven software and In-Q-Tel, the venture capital arm of the CIA. In addition to VCs and Growth Equity funds, a number of Private Equity firms have also invested in the sector. Applied Predictive Technologies (APT) is backed by Accel-KKR and recently received a $100 million minority investment from the Merchant Banking Division of Goldman Sachs. E X H I B I T 23 T OP 10 M O S T A CTIVE VCS I N B I G D A T A A N A L Y T I C S (# O F D E A L S, L A S T 24 M O N T H S ) Source: GP Bullhound We are also seeing the emergence of early stage funds focused specifically on Big Data and Analytics startups such as Data Collective and DataElite. The latter is initially looking to invest in 5 or 10 companies and is offering mentorship from leading Silicon Valley data scientists and entrepreneurs including cofounder Tasso Argyros who previously cofounded Aster Data. 25 GP Bullhound LLP

27 M&A: POISED FOR A NEW W AVE OF C ONS OLIDA TI ON Business Intelligence software has been a hotbed of acquisition activity for almost twenty years, as market leaders have sought to acquire disruptive emerging companies to remain competitive. The following diagram shows over 100 acquisitions made since the mid-90s by the market s key consolidators, with IBM, Oracle and SAP each having made over 30 acquisitions in this market. E X H I B I T 24 A N A L Y T I C S S O F T W A R E C O N S O L I D A T I O N ( ) Nsite Software Blue Edge Software Sybase Business Planning Solution KXEN SAF DWL Alphabox Purisma Acta Technology Medience Infommersion Coremetrics Cognos Openpages PSS Highdea ALG SQL Solutions SRC SoftwareSmartOps Ascential Unicorn Frango Varicent Vivisimo Analytics Firstlogic Inxight Software SPSS Informix StoredIQ Cartesis Cundus Netezza Clarity Business Objects Solonde Callixa YASU Technologies OutlookSoft Softa Group Algorithmics Starbelly Productions Crystal Decisions Pilot Software Edgewing Alphabox NuTech Databeacon Celequest i2 Fuzzy! Informatik Sunopsis UpStream Software SQRIBE Technologies Cast Iron DataMirror Applix Star Analytics Luminary Solutions Ndevr Siebel Systems Sapling Vitrue Trigo Adaytum Software Quiterian ParAccel Tizor Systems TeaLeaf QiQ Solutions Oracle s Crystal Ball FatWire Tekelec Skywire Software Zoomix 90 Degree Software Eloqua InQuira ProClarity Silver Creek Systems PerformanceSoft Veridiem Relsys International DataRaker Netsure Telecom Xenos Group Brio Technology Oprisk Analytics Sigma Dynamics Alcar Group Hyperion Beatware ActiveViews Panorama Secerno Teragram Razza Solutions Collective Intellect Maximal Innovative Intelligence Dataflux Risk Advisory Software Market Max Arbor Software Datanomic Endeca Technologies Source: GP Bullhound. Colour of company name indicates acquirer We believe the market is entering a new wave of consolidation around Big Data and Predictive Analytics, likely to mirror the Business Intelligence wave seen in the last decade. This last wave saw a small number of BI leaders e.g. Business Objects, Cognos, Hyperion gain scale in the early 2000s via a series of acquisitions, before, in 2007, the three incumbent Enterprise Software market leaders registered the strategic importance of BI, and all announced mega transactions: Oracle-Hyperion (March, $3.3bn), SAP- Business Objects (October, $6.8bn), IBM-Cognos (November, $4.9bn). Another recent round of competitive M&A between these three giants was for cloud-based HCM software (2011/2), in which SAP bought SuccessFactors, Oracle bought Taleo, and IBM bought Kenexa. The last few years has seen a shift in M&A focus away from traditional Data Warehousing and BI segments towards higher growth Big Data. In the eight month period from July 2010 to March 2011, four Big Data companies Greenplum, Netezza, Vertica Systems and Aster Data Systems were acquired by EMC, IBM, HP and Teradata respectively for a combined $2.6 billion. The four companies received total venture funding of $218m, highlighting the opportunity for significant financial returns in the sector. With several Big Data Analytics companies now reaching extremely high levels of funding and growth, we believe it will be a matter of time before the largest are acquired by the major vendors such as SAP, IBM and Oracle. IBM has led this trend, spending over $16 billion on 36 Analytics businesses since 2005, as part of a long-term strategy to exploit growth in Big Data and Analytics, an area which IBM expects will generate $20 billion for the business in revenue by Frier S. IBM Chief Rometty Boosts Analytics Sales Goal to $20 Billion Bloomberg (Feb 2013) 26 GP Bullhound LLP

28 Significant IBM acquisitions in this area include the $1.2 billion acquisition of SPSS, a Predictive Analytics software business in 2009, and the first move into the Big Data appliance sector with the $1.7 billion acquisition Netezza in IBM recently announced the acquisition of Dublin based The Now Factory, a Big Data Analytics provider targeting the telecoms sector. Further acquisitions are anticipated by IBM as it approaches its 2011 stated goal of spending $20 billion on acquisitions by We expect to see an acceleration of deal activity as incumbent Analytics vendors such as Oracle, Microsoft, SAP and SAS look to acquire capabilities and talent, particularly within the area of Predictive Analytics. This next wave of acquisitions has already begun with SAP s acquisition of KXEN announced in September IBM Annual Report (2011) 27 GP Bullhound LLP

29 SELECTED PRIVATE PLACEMENTS Transaction Date Value Announced Target Target Country Investor (US$m) Commentary Oct-13 MongoDB US EMC, Red Hat, Intel, New Enterprise Associates, Sequoia Capital, Salesforce, Fidelity Investments, T. Rowe Price Leading NoSQL data store Sep-13 Loggly US Matrix Partners, Trinity Ventures, True Ventures, Cisco, Data Collective 10.5 Log management and data analysis Sep-13 Narrative Science US Jump Capital, SAP Ventures, Battery Ventures, Northwestern University 11.5 Business intelligence reporting services Sep-13 NGDATA Belgium Capricorn Venture Partners, Sniper Investments 3.3 Big Data management and machine learning Sep-13 Palantir US n.a Big Data analytics platform Aug-13 Antuit Singapore Zodius Capital 3.9 Big Data analytics solution Aug-13 Birst US Sequoia Capital, Northgate Capital 38.0 Cloud-based business analytics Jul-13 RealityMine UK The North West Fund, Sidecar Fund 0.9 Consumer behaviour analytics Jul-13 Context Relevant US Vulcan Capital, Geoff Entress, Madrona Venture Group, Bloomberg Beta 7.1 Predictive analytics Jul-13 DataStax US Scale Venture partners, draper Fisher Jurvetson, Next World Capita, Meritech Capital Partners, Lightspeed Venture Partners, Crosslink Capital Jul-13 GamesAnalytics UK Par Equity, STV Group, Scottish Enterprise Co-investment Fund 1.3 Jun-13 Applied Predictive Technologies 45.0 Leading enterprise distribution of Apache Cassandra NoSQL data store Data mining and monetization company serving the online videogames industry US Goldman Sachs Cloud-based predictive analytics Jun-13 Ayasdi US Citi Ventures, GE Ventures, Institutional Venture Partners 30.6 Data visualization Jun-13 Fractal Analytics US/UK/India TA Associates 25.1 Data analytics Jun-13 Hortonworks US Tenaya Capital, Dragoneer Investment Group, Benchmark, Index Ventures, Yahoo!, 50.0 Hadoop distribution and services May-13 Alteryx US SAP Ventures, Toba Capital 12.0 Data analytics May-13 BitSight US Globespan Capital Partners, Menlo Ventures, Flybridge Capital Partners, Commonwealth Capital Ventures 24.0 Security big data analytics May-13 Opera Solutions, LLC US Wipr Technologies, Enlight 30.0 Big Data services Apr-13 MarketShare US FTV Capital, Elevation Partners 38.0 Predictive analytics Apr-13 Skytree US Mar-13 MapR US US Venture Partners, Javelin Venture Partners, Osage Venture Partners, United Parcel Service Mayfield Fund, Lightspeed Venture Partners, New Enterprise Associates, Redpoint Ventures 18.0 Predictive analytics 32.0 Hadoop distribution focused on large enterprises Feb-13 Elasticsearch Netherlands Index Ventures, Benchmark, SV Angel 24.0 Big data search technology Feb-13 Mu Sigma US MasterCard Advisors 45.0 Big data services Jan-13 Semetric UK Imperial Innovations, Pentech Ventures 4.8 Online big data analytics, focused on music industry Dec-12 Cloudera US Accel Partners, Greylock Partners, Ignition Partners, In-Q-Tel, Meritech Capital Partners 65.0 Hadoop distribution and services Dec-12 Karmasphere US Hummer Winblad Venture Partners, U.S. Venture Partners 3.5 Big Data analytics Dec-12 Revolution Analytics US n.a. 7.8 Predictive analytics Nov-12 Datasift UK Scale Venture Partners, GRP Partners, IA Ventures, Northgate Capital, Daher Capital 15.3 Social media analytics Nov-12 Neo Technology Sweden Fidelity Growth Partners Europe, Sunstone Capital, Conor Venture Partners 11.0 Graph database Nov-12 Qubit UK Balderton Capital 7.5 Web analytics Nov-12 Sumo Logic US Accel Partners, Greylock Partners, Sutter Hill Ventures 30.0 Log management and data analysis Oct-12 Hadapt US Atlas Venture 6.7 Big Data analytics Sep-12 Acunu UK Imperial Innovations, Eden Ventures, Pentech Ventures, Oxford Technology Management 5.8 Platform for low-latency, continuous analytics on Big Data Sep-12 Capillary Singapore Sequoia Capital, Norwest Venture Partners, Qualcomm Ventures 15.5 Cloud-based analytics for CRM Sep-12 Datameer US/Germany Redpoint Ventures, Kleiner Perkins Caufield & Byers 6.0 Big Data analytics platform Aug-12 ParStream Germany Khosla Ventures, Baker Capital, CrunchFund, Data Collective, Tola Capital 5.6 Big Data analytic database Jun-12 MemSQL US IA Ventures, Digital Sky Technologies, Data Collective, Raymond Tonsing 3.0 Real-time database for analytics May-12 Recorded Future US/Sweden Balderton Capital, Google Ventures, Atlas Venture, IA Ventures, In-Q-Tel 12.0 Big Data analytics Jan-12 VisualDNA UK Svyaznoy Group 10.0 Real time understanding Dec-11 Mu Sigma US General Atlantic LLC, Sequoia Capital Big Data services Dec-11 Platfora US Battery Ventures, Andreessen Horowitz, Sutter Hill Ventures, In-Q-Tel, Data Collective 20.0 Big Data analytics platform Average 30.1 Median GP Bullhound LLP

30 SELECTED M&A TRANSACTIONS Transaction Date Value LTM EV/ Announced Target Target Country Buyer (US$m) Revenue EBITDA Commentary Oct-13 Onavo Israel Facebook c Mobile data analytics company Oct-13 The Climate Corporation US Monsanto Real-time pricing and purchasing of customizable weather insurance Oct-13 The Now Factory IRL IBM Customer and network analytics for communications service providers Sep-13 BugSense US Splunk Mobile analytics platform Sep-13 KXEN US SAP Predictive analytics Aug-13 JackBe US Software AG Real-time operational intelligence software Jul-13 Kapow Software US Kofax x - Big Data integration platform Jul-13 Myrrix UK Cloudera Real-time, scalable clustering and recommender system Jul-13 Visual Analytics US Raytheon Data analytics, decision support, and information sharing software Jul-13 Xtremeinsights US Intel Big data analytics consulting company Jun-13 Spindle US Twitter Geo-restricted real-time stream Jun-13 Apama UK Progress Software Corp Event processing platform Jun-13 EdgeSpring US Salesforce Data driven business decisions Jun-13 Inkiru US Walmart Real-time predictive analytics to transactional big data challenges Jun-13 Panopticon Sweden Datawatch x - Data visualization and analytics Jun-13 ScaleIO Israel EMC Data-integration software Jun-13 Standard Analytics UK Dunnhumby Machine learning and analytics focused on sales forecasting May-13 Lucky Sort US Twitter Big Data visualization and navigation Apr-13 ParAccel US Actian Analytic database Mar-13 Atlas US Facebook Ad analytics company and management Mar-13 Enqio Belgium NGDATA Software developed for in-depth analyses Feb-13 Bluefin Labs US Twitter Social TV analytics company Jan-13 (m)phasize US Sapient Cross-channel marketing analytics Oct-12 igodigital US ExactTarget Customer data analytics Oct-12 Quiterian US Actuate Visual data mining, social media analytics and predictive analytics Feb-12 Outerthough Belgium NGDATA Technology provider of scalable data storage, search and analytics Mar-11 Aster Data Systems US Teradata Feb-11 Vertica US HP Analytic database Big data management and big data analysis for data-driven applications Sep-10 Netezza US IBM 1, x 80.8x Data warehousing and analytics Jul-10 Greenplum US EMC Data warehousing and analytics Jul-09 SPSS US IBM 1, x 10.0x Worldwide provider of predictive analytics software and solutions Average x 45.4x Median x 45.4x 29 GP Bullhound LLP

31 SELECTED COMPANY PROFILES ANALYTICS, VISUALI SATION AND BIG DA TA Acunu was founded in 2009 and is headquartered in London. Acunu's solution enables real-time monitoring and Analytics applications with low latency, thanks to being powered by Apache Cassandra (an industry leading NoSQL database, able to handle high data velocity). Additionally, Acucu Analytics allows users to aggregate analyses into dashboards, install alerts, embed visualisations into web apps, and drive Analytics queries directly from apps. Founded in 2008 by Stanford University mathematicians, Ayasdi provides a visualisation solution for Big Data sets based on topology (the mathematical study of shapes and spaces). The Ayasdi platform is currently available both on-premise and in a cloud environment and in addition to this Ayasdi offers professional services to let users take full advantage of the data at hand. Customers include GE, Citi, Merck and Anadarko. Founded in 2002 and headquartered in Paris, Augure provides a suite of Enterprise Reputation Management tools to help clients manage their Corporate Communications, Product Communications and Public Affairs more effectively. Clients rely on Agure s SaaS solutions to listen to what people are saying about their brand on social networks, identify key influencers and brand advocates, and carry out measurable multi-channel communications campaigns. The Augure AIR (Augure Influencers Ranking) product measures influence based on analysis of influencer publications in addition to quantitative criteria. Clients include Nissan, HSBC, General Electric, Cartier, and the European Parliament. Founded in 2009, with offices in Gothenburg, Stockholm and New York City, Burt provides a Big Data integration and Analytics platform that helps digital publishers track, connect and analyze disparate data sources across multiple screens, channels and revenue models. A core Analytics partner to Google, Burt is the only company that provides out-of-the-box, real-time integration of detailed audience, advertising and revenue data from Doubleclick, Open Adstream, Ad tech etc. to leading web Analytics tools such as Adobe Site Catalyst and Google Analytics, giving customers the insights and reporting they need to grow their digital revenues. The company s award winning Big Data platform process tens of billions of media transactions each month for media companies such as Bonnier, Schibsted, Axel Springer, IDG, Stampen, Aller and Thomson Reuters. DataSift was founded in 2007 and is headquartered in Reading, UK. DataSift is one of only two companies to be a licensed distributor of Twitter's complete data set, the 'firehose'. Its advanced data-filtering technology helps customers capture the most relevant data from sources such as Twitter, Facebook and Amazon. Additionally, DataSift also offers real-time Analytics and visualisation tools in partnership with Splunk and Tableau, among others. DataSift's customers include news organisations, financial services companies, retailers and political organisations. / 30 GP Bullhound LLP

32 Datawatch was founded in 1985 and is headquartered in Chelmsford, MA. Datawatch offers data optimisation solutions that help companies fully capture the available data, particularly semi-structured data (static reports, EDI streams and PDF files). In June 2013, Datawatch acquired Stockholm-based Panopticon, a leading real-time data discovery solutions provider, to enable users to visually analyse the full scope of data in real-time. Datawatch's solutions are used across a wide range of sectors such as healthcare, financial services and retail. Elasticsearch was founded in 2012 and is based in London. The company offers support for users of Elasticsearch, an advanced open-source analytics and search engine with the same name, created in the late 2000s by the company s founders. Elasticsearch has been downloaded over 2 million times, and is one of the fastest growing open source projects with c.200k downloads per month as of February Founded in 2010, Games Analytics is an Edinburgh-based provider of in-game analytics and marketing solutions to the video game and casino sectors. Using proprietary event-based data structures, and leveraging Predictive models and cloud-based in-memory Big Data processing, Games Analytics delivers a real-time solution for maximising in-game revenue. Clients include tier-one international games publishers and broadcasters. Growth Intelligence was founded in 2011 and is headquartered in Canary Wharf, London. Growth Intelligence offers a B2B SaaS solution for generating sales leads; the technology involves web-crawling and machine learning to determine optimal targets by company type, stage of growth, and recent events. The underlying technology was also used in a report commissioned by Google to estimate the number of UK technology companies the findings revealed a 40% higher number than could be discerned from SIC codes. The company was recently joined by Hal Varian and JP Rangaswami ('s Chief Scientist) as advisors. MapR was founded in 2009 and is headquartered in San Jose, CA. MapR is an enterprise software company that offers a leading Hadoop distribution and related services (main competitors are Cloudera and Hortonworks). MapR is focused on enterprise-grade Hadoop and its solutions prioritise features such as disaster recovery and data protection. Partners include Amazon Web Services, Google, EMC, and Cisco. 31 GP Bullhound LLP

33 / Neo Technology was founded in 2000 and is headquartered in San Mateo, CA. Neo Technology operates the NoSQL graph database Neo4j, which from company information is the largest ecosystem of partners and tens of thousands of successful deployments. Neo Technology's Neo4j Graph Database helps put data into context and allows the visualisation of relationships. Amongst selected customers are Cisco, HP, Accenture, Deutsche Telekom, and Telenor. NGDATA was founded in 2009 and is headquartered in Zwijnaarde, Belgium. NGDATA offers both Big Data management and machine learning technologies. NGDATA's key software offering, Lily, has grown to attract international customers such as BNP Paribas, France Telecom, Toyota and Johnson & Johnson. It is built on algorithms, which allow users to gain access to customer insights and purchasing preferences by analysing and linking diverse data such as purchase history, social media activity, geographic location, and other brand interactions. In March 2013, NGDATA acquired ENQIO, a Belgium based data management and Analytics consultancy, to further extend their knowledge base. Founded in 2004 and headquartered in London, Onalytica offers a portfolio of SaaS and data solutions which unlock the value of online data. Voice of the Market (VoM) solution allow customers to harness the power of observed online, real time conversations to surface brand, PR, product and sales opportunities and drive actionable insight across the organisation. Their Influencer Relationship Management (IRM) solution allows customers to reach the right people at the right time with the right message at scale by building relationships with key stakeholders and managing them over time. Clients include Coca Cola, Ford, Microsoft, Samsung, HP and Ministry of Justice. Palantir Technologies was founded in 2003 by Peter Thiel, Alexander Karp and Joe Lonsdale and is headquartered in Palo Alto. Palantir's software allows users to consolidate data from various databases - ranging across structured, unstructured, relational, temporal and geospatial data - and to perform analysis by means of data visualisation. Palantir's offering is split into two parts: Palantir Government and Palantir Finance. Palantir Government is used across all public sector interests such as defense and intelligence as well as cyber security and healthcare. Palantir Finance is used by various kinds of financial institutions to analyse mission-critical data. Parstream was founded in 2007 and is headquartered in Cologne, Germany. It offers a cutting edge database solution, which enables real-time Analytics on constantly-evolving Big Data sets. Parstream s innovate technology can employ GPUs (graphics processors) rather than CPUs (standard computer processors) to achieve greater parallelism and speed of calculation. Other key technologies leveraged include in-memory massively parallel processing, a columnar data store, and a shared-nothing architecture. The company was recognised as a Cool Vendor in Gartner s 2012 review of Advanced Data Management companies. 32 GP Bullhound LLP

34 PeerIndex was founded in 2009 and is headquartered on London's Silicon Roundabout. Peerindex builds the Influence Graph, which identifies influential users on social media sites and identifying these as opportunities to turn social capital into real capital. PeerIndex is the main competitor of Klout and Kred. Customers include Ford, Guiness, Samsung and Virgin Media. Platfora was founded in 2011 and is headquartered in Palo Alto. Platfora offers extremely fast Hadoopbased access through the use of in-memory caching technology, coupled with an intuitive HTML5 front-end without per-user licensing limits. Platfora allows users an interactive experience in terms of data handling and visualisation with the ability to quickly communicate analysis through internal messaging. Quartet FS was founded in 2005 and is headquartered in London. Quartet FS is the provider of the ActivePivot, an in-memory Analytics database management system which primarily aims to service companies with time consuming and data-intensive processes. It particularly supports Predictive Analytics, management by exception, impact analysis, resource optimisation, crisis management and collaboration. Quartet FS's customer base is spread across capital markets, logistics, transportation, market exchanges and retail. In 2013, Quartet FS was named a 'cool vendor' by Gartner. RealityMine was founded in 2012 and is headquartered in Manchester. RealityMine is the provider of a SaaS platform for analysing mobile activity. The on-device mobile metering system helps companies to better understand the mobile consumer by gaining access to the opted-in consumers' mobile activity, ranging from call activity to online searches. Rosslyn Analytics was founded in 2007 and is headquartered in London. The company was founded by two former finance executives and offers RAPid, a cloud-based Analytics platform combining data extraction, cleansing, enrichment and visualisation. RAPid targets procurement and cost efficiency with its 'spend data management' solution, aggregating data from multiple sources into a single online view of the total organizational spend. Semetric was founded in 2008 and is headquartered in London. Semetric s main offering, Musicmetric, launched in 2009, provides insights into customer behaviour online and trend forecasting in the music industry. Machine learning algorithms allow Musicmetric to analyse user comments on websites, social networks, peer-to-peer music trading websites and general activity connected with currently more than 700,000 artists; the company provides this information as a service to labels, product managers, band managers, marketers and promoters. 33 GP Bullhound LLP

35 Splunk was founded in 2005 and is headquartered in San Francisco. Splunk provides real-time machine data analysis with powerful visualisation tools. Machine data is highly complex unstructured data, which is the fastest growing and most valuable data segment according to the company. Recently Splunk launched Hunk, an Analytics platform to offer interactive data exploration, analysis and visualisation of Hadoop data. / Founded in 2006 and headquartered in London, VisualDNA provides pschographic audience insight solutions and powers highly-targeted advertising in real-time. Using patented technology, VisualDNA specialises in personality profiling data, bringing a new way of understanding the motivations, aspirations, attitudes and values of customers. The company s WHYanalytics solution provides new analytical insights around who is visiting a website, and why, via a real-time dashboard. PREDIC TIVE ANALYTI CS Blue Yonder was founded in 2008 as a spin out from CERN by Prof. Dr. Michael Feindt and is headquartered in Karlsruhe, Germany. Blue Yonder offers a real-time and cloud-based predictive Analytics solution, which uses machine learning techniques to automatically adapt its models and detect data patterns. Key target areas include demand planning, dynamic pricing, customer analysis and churn management, predictive maintenance and risk management. Selected customers include Next, Vodafone, Axel Springer, OTTO and dm. The company has nearly 100 employees and opened a UK office in January i4c Analytics was founded in 2009, and is headquartered in Milan, Italy. i4c Analytics offers a Predictive Analytics software solution, based on R, to enable business end-users to leverage Predictive Analytics insights without the complexity of traditional Predictive Analytics tools. The company s roots are from an SPSS consultancy business founded in 2003 the founders saw that many enterprises were unable to leverage Predictive Analytics tools like SPSS, SAS, and others, so decided to create a more accessible alternative, with strong industry vertical specialisation from the outset. Clients include Shell, ENI, Santander, Generali and Ducati. 34 GP Bullhound LLP

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