July 2014
Refer to pages for Important Disclosures, including Analyst’s Certification.
For Important Disclosures on the stocks discussed in this report, please go to http://researchglobal.bmocapitalmarkets.com/Company_Disclosure_Public.asp. Joel P. Fishbein, Jr
BMO Capital Markets Corp. joel.fishbein@bmo.com (212) 885-4159
Brett Fodero
BMO Capital Markets Corp. brett.fodero@bmo.com (212) 885-4019
Edward Parker
BMO Capital Markets Corp. edward.parker@bmo.com (212) 885-4095
Data as a Strategic Currency
Financial Group
Financial Group
A member ofBMO 3 July 17, 2014
Technology - Software ...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 ...104 Tableau Software ...107 Investment Drivers...108 Company Background ...115 Market Backdrop...120
Balance Sheet and Capital Allocation...128
Current Outlook ...128 Valuation...129 Risks...130 Financial Models...132 Qlik Technologies...135 Investment Drivers...136 Company Background ...141 Market Backdrop...145
Balance Sheet and Capital Allocation...154
Current Outlook ...154
Valuation...155
Risks...156
Financial Models...158
Splunk ...161
Details & Analysis ...162
Industry Backdrop...169
Product ...171
Pricing: Near-Term Noise Offset by Long-Term Accretion...176
Distribution: Capacity Constraints a Hurdle and Opportunity...178
Operational Intelligence Market ...179
Current Outlook ...180
Balance Sheet ...182
Valuation...182
Risks...183
Financial Group
Page 5 July 17, 2014
Industry Rating: Market Perform
BMO Capital Markets Corp. joel.fishbein@bmo.com Brett Fodero / Edward Parker BMO Capital Markets Corp. 212-885-4019 / 212-885-4095
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 2020. Despite organizations investments, only 0.05% of digital data created is being captured for use in the real time reporting and analytics process.
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).
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.
Page 6 July 17, 2014
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 7 July 17, 2014
SCORECARD Variables Technology TAM Strength Execution Total Notes:
Weight 20% 30% 20% 30% 100%
Tableau Software Inc DATA 4 4 2 5 3.9
- 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 4 4 3 4 3.8
- 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 3 3 2 2 2.5
- 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 4 4 4 3 3.7
- 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 3 4 3 2 2.8
- 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 2 2 2 4 2.7
- 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 3.8
- 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 SPLK PerformMarket 11.1x 8.4x 3.3 - 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.
Qlik Technologies Inc QLIK PerformMarket 3.0x 2.6x 2.6
- 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 8 July 17, 2014
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 2010-2020
0.1 1.2 2.8 8.5 40 0 5 10 15 20 25 30 35 40 45 2005 2010 2012 2015 2020 Ze tta byte (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)
Page 9 July 17, 2014
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 2020.
According to Gartner, unstructured data doubles every three months and seven million Web pages are added every day.
Page 10 July 17, 2014
to 2013.
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 2012.
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 11 July 17, 2014
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 12 July 17, 2014
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 rearward-looking centralized reporting of the past to forward-rearward-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 2013. 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 2008-2012, 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 13 July 17, 2014
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 14 July 17, 2014
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 15 July 17, 2014
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, Email, 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, emails, 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
Page 16 July 17, 2014
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 email, mobile, social media, and the Web. ExactTarget can now
Page 17 July 17, 2014
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 18 July 17, 2014
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 10. 20 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 19 July 17, 2014
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 20 July 17, 2014
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 21 July 17, 2014
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 22 July 17, 2014
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 open-source 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 front-end Big Data applications. Marked growth rates reflect these trfront-ends. 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 23 July 17, 2014
Market Size Growth
2013 2017 2013 CAGR Vendors
Analytics
Business Intelligence $ 14,055 $ 18,604 7.0% 7.3% IBM, ORCL, SAP, MicroStrategy, SAS
Data Discovery $ 1,091 $ 2,866 27.9% 27.3% Tableau, Qlik
Data Management
Relational DBMS (RDBMS) $ 27,927 $ 39,225 6.8% 8.9% Oracle, IBM, Microsoft
Data Integration Tools $ 2,241 $ 3,270 9.4% 9.9% Informatica, IBM, Tibco
Data Quality Tools $ 1,091 $ 1,948 13.7% 15.6% Informatica, IBM, SAS, SAP
Big Data Enablers
Hadoop $ 495 $ 1,650 93.4% 35.1% Cloudera, Hortonworks
NoSQL $ 525 $ 1,825 83.6% 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 2013. 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 24 July 17, 2014
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
Page 25 July 17, 2014
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
Traditional Big Data Architecture
ERP CRM SCM Files Web Logs E‐mail Sensors Machine Data Social Media S t a g i n g
Data Management Systems
Data Mart OLAP Cube NoSQL Reporting Dashboards Excel Data Discovery Custom Applications Enterprise Applications E T L
Source: BMO Capital Markets Research.
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 26 July 17, 2014
framework.
Exhibit 17. Data Vendor Landscape
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 27 July 17, 2014
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 real-time visibility and insight into business and IT operations, enabling people to make better, faster decisions. OI automates processes for responding to events by using business rules and incoming event information. Contrary to Business Intelligence, which is data centric, OI is primarily activity-centric.
Page 28 July 17, 2014
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 29 July 17, 2014
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 30 July 17, 2014
Share
Market Share Business Intelligence
($M) Revs ‐ '13 Share ‐ '12 Share ‐ '13 y/y growth
Total $14,354 7.9% 2013‐2017 CAGR 7.3% Top 3 share 50% 48% SAP $3,057 21.8% 21.3% 5.3% Oracle $1,994 14.7% 13.9% 2.1% IBM $1,820 13.0% 12.7% 4.9% SAS $1,696 12.0% 11.8% 6.0% Microsoft $1,379 8.9% 9.6% 15.9% Qliktech $431 2.7% 3.0% 20.1% MicroStrategy $420 3.0% 2.9% 4.9% FICO $369 2.5% 2.6% 8.8% Tableau $213 0.9% 1.5% 80.5% Information Builders $188 1.4% 1.3% 0.3% Other vendors $2,787 19.0% 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 31 July 17, 2014
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-drivenMain Sellers Megavendors, large independents
Fast-growing independents
Approach Top-down, IT-modeled (semantic layers), query existing repositories
Bottom-up, business-user-mapped (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 32 July 17, 2014
respondents noted a willingness to put mission-critical BI in the cloud, compared to 33% in 2013. As with most applications, the promise of increased collaboration with customers and partners and mobility are drivers of Cloud BI. Over time, we expect the proliferation of connected devices (the “Internet of Things”) and continued growth in cloud services (SaaS, PaaS, IaaS) will create more cloud based data and drive the adoption of cloud BI solutions.
Cloud Business Intelligence vendors including Birst, GoodData, Looker, Domo, Adaptive Insights have all garnered significant amounts of venture funding. GoodData’s focus on building an end-to-end system from visual interface to customer data management to perform analytics-as-a-service is gaining good market traction. 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 2012. 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 business-led rather than IT business-led.
