Wisdom of Crowds Business Intelligence Market Study

Transcription

1 May 31, 2016 Dresner Advisory Services, LLC 2016 Edition Wisdom of Crowds Business Intelligence Market Study Licensed to TIBCO

2 Disclaimer This report should be used for informational purposes only. Vendor and product selections should be made based on multiple information sources, face-to-face meetings, customer reference checking, product demonstrations, and proof-of-concept applications. The information contained in all Wisdom of Crowds Market Study Reports reflects the opinions expressed in the online responses of individuals who chose to respond to our online questionnaire and does not represent a scientific sampling of any kind. Dresner Advisory Services, LLC shall not be liable for the content of reports, study results, or for any damages incurred or alleged to be incurred by any of the companies included in the reports as a result of the content. Reproduction and distribution of this publication in any form without prior written permission is forbidden. 2

3 Business Intelligence: A Definition Business intelligence (BI) is knowledge gained through the access and analysis of business information. Business intelligence tools and technologies include query and reporting, OLAP (online analytical processing), data mining and advanced analytics, end-user tools for ad hoc query and analysis, and dashboards for performance monitoring. Howard Dresner, The Performance Management Revolution: Business Results Through Insight and Action (John Wiley & Sons, 2007) 3

4 Introduction This year we celebrate the ninth anniversary of Dresner Advisory Services! Our thanks to all of you for your continued support and ongoing encouragement. Since our founding in 2007, we have worked hard to set the bar high challenging ourselves to innovate and lead the market offering ever greater value with each successive year. Our first market report in 2010 set the stage for where we are today. Since that time we have expanded our agenda and have added new research topics every year since. For 2016 we re on track to release 15 major reports, including, this, our BI flagship report in its seventh year of publication! In addition to our ongoing coverage of key topics such as embedded BI, big data analytics and advanced and predictive analytics, we have added new topics including Collective Insights TM (blending collaboration and governance) and systems integrators. This latest installment of our flagship business intelligence market study also continues to evolve. This year we have added a new section examining the chief analytics and chief data officer roles. And, as with every year, we have begun tracking additional technologies and initiatives including governance, streaming data analysis, and data storytelling. We hope you enjoy this report! Best, Howard Dresner Chief Research Officer Dresner Advisory Services 4

5 Contents Business Intelligence: A Definition... 3 Introduction... 4 Benefits of the Study Consumer Guide Supplier Tool External Awareness Internal Planning About Howard Dresner and Dresner Advisory Services About Jim Ericson Survey Method and Data Collection Data Collection Data Quality New for Executive Summary Study Demographics Geography Functions Vertical Industries Organization Size Analysis and Trends Departments/Functions Driving Business Intelligence Functions Driving Business Intelligence by Major Geography Functions Driving Business Intelligence by Vertical Industry Functions Driving Business Intelligence by Organization Size User Roles Targeted for Business Intelligence Targeted Users for Business Intelligence by Geography User Targets for Business Intelligence by Organization Size User Targets for Business Intelligence by Vertical Industries Objectives for Business Intelligence

6 Business Intelligence Objectives by Geography Business Intelligence Objectives by Function Business Intelligence Objectives by Vertical Industry Business Intelligence Objectives by Organization Size Penetration of Business Intelligence Solutions Expansion Plans for Business Intelligence Through Current Business Intelligence Penetration by Geography Planned Business Intelligence Penetration by Geography Business Intelligence Penetration by Function Current Business Intelligence Penetration by Vertical Industry Planned Business Intelligence Penetration by Vertical Industry Current Business Intelligence Penetration by Organization Size Planned Business Intelligence Penetration by Organization Size Chief Data and Chief Analytics Officers Enterprises with Chief Data or Chief Analytics Officers Enterprises with Chief Data or Chief Analytics Officers by Geography Enterprises with Chief Data or Chief Analytics Officers by Industry Enterprises with Chief Data or Chief Analytics Officers by Organization Size Enterprises with Chief Data and Chief Analytics Officers Reporting Structure Chief Data Officer and Success with BI by Reporting Structure Chief Analytics Officer and Success with BI by Reporting Structure Number of Business Intelligence Tools in Use Number of Business Intelligence Tools in Use 2014 to Number of Business Intelligence Tools by Geography Number of Business Intelligence Tools by Function Number of Business Intelligence Tools by Vertical Industry Number of Business Intelligence Tools by Organization Size Technologies and Initiatives Strategic to Business Intelligence Technology Priority Changes from Technologies and Initiatives Strategic to Business Intelligence by Geography

7 Technologies and Initiatives Strategic to Business Intelligence by Function Technologies and Initiatives Strategic to Business Intelligence by Vertical Industry Technologies and Initiatives Strategic to Business Intelligence by Organization Size Business Intelligence and the State of Data Business Intelligence and the State of Data by Geography Business Intelligence and the State of Data by Function Business Intelligence and the State of Data by Vertical Industry Business Intelligence and the State of Data by Organization Size Business Intelligence and Action on Insight Business Intelligence and Action on Insight by Geography Business Intelligence and Action on Insight by Function Business Intelligence and Action on Insight by Vertical Industry Business Intelligence and Action on Insight by Organization Size Success with Business Intelligence Reasons Why Business Intelligence Succeeds Reasons Why Business Intelligence Fails Success with Business Intelligence by Organization Size Success with Business Intelligence by BI Objectives Success with Business Intelligence by Targeted Users Success with Business Intelligence and Technology Priorities Success with Business Intelligence and Number of BI Tools Success with Business Intelligence and the State of Data Success with Business Intelligence and Action on Insight Success with Business Intelligence and Penetration of Users Industry and Vendor Analysis Scoring Criteria Industry Performance Sales/Acquisition Experience Value

8 Quality and Usefulness of Product Technical Support Consulting Integrity Recommended Performance Improvements Vendor Ratings Business Intelligence Market Models Customer Experience Model Vendor Credibility Model Detailed Vendor Ratings Birst Detailed Score Dell Detailed Score Dimensional Insight Detailed Score Domo Detailed Score Dundas Detailed Score GoodData Detailed Score IBM Detailed Score Infor Detailed Score Information Builders Detailed Score Jedox Detailed Score Klipfolio Detailed Score Logi Analytics Detailed Score Looker Detailed Score Microsoft Detailed Score MicroStrategy Detailed Score OpenText Detailed Score Oracle Detailed Score Pentaho Detailed Score Pyramid Analytics Detailed Score

9 Qlik Detailed Score RapidMiner Detailed Score Salesforce Detailed Score SAP Detailed Score SAS Detailed Score SiSense Detailed Score Tableau Software Detailed Score TIBCO Software Detailed Score Yellowfin Detailed Score Other Dresner Advisory Services Research Reports Dresner Advisory Services Wisdom of Crowds Survey

10 Benefits of the Study The Wisdom of Crowds Business Intelligence Market Study provides a wealth of information and analysis offering value to both consumers and producers of Business Intelligence technology and services. Consumer Guide As an objective source of industry research, consumers use the Wisdom of Crowds Business Intelligence Market Study to understand how their peers leverage and invest in business intelligence and related technologies. Using our trademark 33-criteria vendor performance measurement system, users glean key insights into BI software supplier performance, enabling: Comparisons of current vendor performance to industry norms Identification and selection of new vendors Supplier Tool Vendor Licensees use the Wisdom of Crowds Business Intelligence Market Study in several important ways such as: External Awareness - Build awareness for the business intelligence market and supplier brand, citing Wisdom of Crowds business intelligence Market Study trends and vendor performance - Create lead and demand-generation for supplier offerings through association with Wisdom of Crowds Business Intelligence Market Study brand, findings, webinars, etc. Internal Planning - Refine internal product plans and align with market priorities and realities as identified in Wisdom of Crowds Business Intelligence Market Study - Better understand customer priorities, concerns, and issues - Identify competitive pressures and opportunities 10

11 About Howard Dresner and Dresner Advisory Services The Wisdom of Crowds Business Intelligence Market Study was conceived, designed and executed by Dresner Advisory Services, LLC an independent advisory firm and Howard Dresner, its President, Founder and Chief Research Officer. Howard Dresner is one of the foremost thought leaders in business intelligence and performance management, having coined the term Business Intelligence in He has published two books on the subject, The Performance Management Revolution Business Results through Insight and Action (John Wiley & Sons, Nov. 2007) and Profiles in Performance Business Intelligence Journeys and the Roadmap for Change (John Wiley & Sons, Nov. 2009). He lectures at forums around the world and is often cited by the business and trade press. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its business intelligence research practice for 13 years. Howard has conducted and directed numerous in-depth primary research studies over the past two decades and is an expert in analyzing these markets. Through the Wisdom of Crowds Business Intelligence market research reports, we engage with a global community to redefine how research is created and shared. Other research reports include: - Advanced and Predictive Analytics - Big Data Analytics - Business Intelligence Competency Center - Cloud Computing and Business Intelligence - Collective Insights TM - Embedded Business Intelligence - End User Data Preparation - Internet of Things and Business Intelligence - Location Intelligence Howard ( conducts a weekly Twitter tweetchat on Fridays at 1:00 p.m. ET. The hashtag is #BIWisdom. During these live events the #BIWisdom community discusses a wide range of business intelligence topics. You can find more information about Dresner Advisory Services at 11

12 About Jim Ericson Jim Ericson is a research director with Dresner Advisory Services. Jim has served as a consultant and journalist who studies end-user management practices and industry trending in the data and information management fields. From 2004 to 2013 he was the editorial director at Information Management magazine (formerly DM Review), where he created architectures for user and industry coverage for hundreds of contributors across the breadth of the data and information management industry. writing. As lead writer he interviewed and profiled more than 100 CIOs, CTOs, and program directors in a program called 25 Top Information Managers. His related feature articles earned ASBPE national bronze and multiple Mid-Atlantic region gold and silver awards for Technical Article and for Case History feature A panelist, interviewer, blogger, community liaison, conference co-chair, and speaker in the data-management community, he also sponsored and co-hosted a weekly podcast in continuous production for more than five years. Jim s earlier background as senior morning news producer at NBC/Mutual Radio Networks and as managing editor of MSNBC s first Washington, D.C. online news bureau cemented his understanding of fact-finding, topical reporting, and serving broad audiences. 12

13 Survey Method and Data Collection As in our original Wisdom of Crowds Business Intelligence Market Study, we constructed a survey instrument to collect data and used social media and crowdsourcing techniques to recruit participants. We also include our own research community of over 3,500 organizations (versus 3,000 in 2015) as well as vendors customer communities. Data Collection Our survey base in 2016 includes 1,524 respondents who offered their time to complete our extensive annual survey. In the years since our first report, interest in the study in the form of completed surveys has more than tripled Numbers of Survey Respondents 2010 to Figure 1 - Number of survey respondents 2010 to

14 Data Quality We carefully scrutinized and verified all respondent entries to ensure that the study includes only qualified participants. New for 2016 For 2016, we again expanded our research objectives substantially. As with 2015, this is particularly evident in the area of user trending. This year s study adds: Five additional technologies and initiatives strategic to business intelligence (reporting, governance, streaming data analysis, cognitive BI and edge computing) to extend our study to a total of 30 areas. New polling of the penetration of chief data officers (CDOs) and chief analytics officers (CAOs) within organizations along with cross-tabular analysis (maturity, geography, organization size, success, etc.) 14

15 Executive Summary 15

16 Executive Summary User Analysis: - For the first time, operations moved ahead of executives as the leading driver of BI. BICC and strategic planning lost the most influence (pp ). - Executives and middle managers remain the most-targeted audiences for BI though both have seen declines over time (pp ). - "Making better decisions" remains the top BI objective, followed by operational, revenue and customer service improvements (pp ). - Penetration of BI has grown noticeably in 2016 and near-term penetration growth appears achievable, with small organizations leading the way (pp ). - The current uptake and maturity of chief data and chief analytics officers is modest, with fewer than 15 percent of (mostly large) organizations adopting the title(s). Most report to the CEO, fewer to IT and finance (pp ). - Most organizations continue to employ one, two or three BI tools. Manufacturing uses the fewest, healthcare the most (pp ). - Fundamental BI technologies reporting, dashboards and self-service top the 2016 list; cognitive BI and IoT are "fringe" priorities (pp ). - Organizations say their "state of data" organization is very good: 64 percent report either "data as truth" or a common view of data (pp ). - Organizational ability to take "action on insight" is very high: 83 percent of respondents report either "closed loop" or ad hoc capabilities (pp.73-78). - The core measure of success with business intelligence showed a small net gain. Small organizations are most likely to be "completely" successful. Successful organizations are most broadly involved and penetrated with executive leadership and with fewer tools (pp ). - Advanced "states of data" (views/capabilities), ability to "act on insight," and high employee penetration correlate profoundly with levels of success with business intelligence (pp ). Industry Analysis: - Overall industry performance reflects vendor attention to weaknesses, yielding improved customer reviews (pp ). Professionalism / product knowledge keep the highest and most consistent scores in sales/acquisition, while responsiveness, flexibility, business practices, and follow-up have improved (p. 92). End users believe they are getting high and improving value from industry vendors over time (p. 93). Product quality and usefulness have improved across most measures (p. 94). Technical support performed well in the most important parameters (p. 95). Vendor consulting trailed only one area (p. 96). 16

17 Study Demographics Our 2016 survey base provides a cross-section of data across geographies, functions, organization sizes, and vertical industries. We believe that, unlike other industry research, this supports a more representative sample and better indicator of true market dynamics. We have constructed cross-tab analyses using these demographics to identify and illustrate important industry trends. Geography Slightly more than one-half of respondents work at North America-based organizations (including the United States, Canada, and Puerto Rico). EMEA accounts for about 30 percent of respondents; the remainder are distributed across Asia Pacific and Latin America (fig. 2). Geographies Represented 60.0% 56.4% 50.0% 40.0% 30.0% 29.1% 20.0% 10.0% 10.4% 4.1% 0.0% North America Europe, Middle East and Africa Asia Pacific Latin America Figure 2 Geographies represented 17

18 Functions In 2016, our functional base is somewhat more balanced than in previous years with more respondents outside information technology roles. IT accounts for the largest group but is well offset by an almost equal number of executive respondents. BICC, finance and marketing and sales are the next largest groups taking part in this year s study Tabulating results across functions helps us develop analyses that reflect the differences and influence of different departments within organizations. Functions Represented 30% 29% 28% 25% 20% 15% 10% 5% 9% 8% 6% 4% 4% 3% 2% 8% 0% Figure 3 - Functions represented 18

19 Vertical Industries In 2016, vertical industry distribution is similar to prior studies, led by consulting, technology, and healthcare (fig. 4). Higher education, financial services, business services, and manufacturing are the next most represented. Tabulating results across industries helps us develop analyses that reflect the maturity and direction of different business sectors. 20.0% 18.0% 16.0% Vertical Industries Represented 19% 14.0% 12.0% 12% 10.0% 10% 9% 8.0% 6.0% 4.0% 7% 6% 6% 5% 4% 2.0% 3% 3% 2% 2% 2% 1% 1% 1% 1% 1% 1% 1% 1% 1% 0.0% Figure 4 - Vertical industries represented 19

20 Organization Size Participation by organizations of different sizes (global employee head count) is well balanced in Small organizations (1-100 employees) represent about 30 percent of respondents, mid-size organizations (101-1,000 employees) represent 29 percent, and large organizations (>1,000 employees) account for the remaining 40 percent (fig. 5). Tabulating results by organization size reveals important differences in practices, planning, and maturity. 35% Organization Sizes Represented 30% 30% 29% 25% 20% 17% 15% 10% 5% 8% 8% 6% 0% ,000 1,001-2,000 2,001-5,000 5,001-10,000 More than 10,000 Figure 5 - Organization sizes represented 20

21 Analysis and Trends 21

22 Analysis and Trends Departments/Functions Driving Business Intelligence We asked respondents which functional areas drive business intelligence always, often, sometimes, rarely, or never (fig. 6). Our results show a breadth of influence; but for the first time, operations is the leading choice among survey respondents. (We also note that departmental functions, e.g., marketing, sales, supply chain, all have operational activities within.) By this measure, we might conclude that business operations are becoming more engaged with BI or that BI is more of a baked in or dayto-day tactical activity within operations. Executive management, traditionally the leading driver, is the second-most cited functional driver in Finance and sales are also strong drivers that always or often drive BI in more than half of organizations responding. Functions Driving Business Intelligence Operations Executive management Finance Sales Information Technology (IT) Marketing Strategic planning function Research and development (R&D) Business Intelligence Competency Center Supply chain Human resources Faculty (education) Manufacturing Clinical (Healthcare) 0% 20% 40% 60% 80% 100% Always Often Sometimes Rarely Never Figure 6 - Functions driving business intelligence 22

23 As noted in fig. 6, operations has, over time, supplanted executive management as the leading driver of business intelligence in 2016 (fig. 7). Saturation of BI could account for some of this shift, but we note generally that frontline business users appear to be taking more active responsibility among themselves as influential drivers (as IT and strategic planning taper somewhat). R&D and faculty in higher education also sustained interest, possibly hinting at future spreading or democratization of business intelligence Functions Driving Business Intelligence 2013 to Figure 7 - Functions driving business intelligence 2013 to

24 Another instructive view of year-over-year office and departmental influence is depicted in fig. 8. Along with an industry-specific gain in education, operations emerged as a stronger driver of business intelligence in 2016 (countered by a similar decrease in executive management influence). BICC and strategic planning were the functions that lost the most influence, another indication that business might be taking greater ownership and responsibility for business intelligence. R&D, sales, marketing, and finance were flat or saw small decreases. Change in Functions Driving BI Operations 6% Faculty (Education) 4% Research and development (R&D) Sales -1% 0% Clinical (Healthcare) Marketing Finance Executive management Information Technology (IT) -3% -3% -4% -5% -5% Manufacturing Human resources Supply chain Strategic planning function -7% -8% -8% -9% Business Intelligence Competency Center -12% -14% -12% -10% -8% -6% -4% -2% 0% 2% 4% 6% 8% Figure 8 - Change in functions driving BI 24

25 Functions Driving Business Intelligence by Major Geography Operations is the strongest functional driver of business intelligence in all regions, most notably in Latin America (fig. 9). Executive management is the second choice in all geographies. Sales and IT are close to equally influential as executive management as a BI driver in Asia Pacific. Along with Asia Pacific, EMEA appears to have the best functional balance in BI drivers and, along with North America, the highest influence from finance. BICC is the weakest driver of BI among functions studied across all geographies. Functions Driving Business Intelligence by Geography North America Asia Pacific Latin America Europe, Middle East and Africa Operations Finance Information Technology (IT) Strategic planning function Executive management Sales Marketing Business Intelligence Competency Center Figure 9 - Functions driving business intelligence by geography 25

26 Functions Driving Business Intelligence by Vertical Industry The importance of functional drivers of business intelligence is somewhat predictable across industries (fig. 10). The influence of sales and supply chain are strongest in manufacturing, and marketing leads the advertising industry. Industries are well clustered around operations influence in the range of 3.4 to 3.9 ("sometimes" to "often" drivers). The influence of executive management is more distributed: highest in insurance and healthcare and lowest in advertising and higher education. Functions Driving Business Intelligence by Vertical Industry Clinical (Healthcare) Manufacturing Faculty (education) Human resources Operations Executive management Finance Sales Information Technology (IT) Supply chain Marketing Business Intelligence Strategic planning function Competency Center Research and development (R&D) Manufacturing Healthcare Education (Higher Ed) Business services Financial services Advertising Insurance Figure 10 - Functions driving business intelligence by vertical industry 26

27 Functions Driving Business Intelligence by Organization Size Overall, executive management and operations are the most likely drivers of business intelligence across organizations of different sizes (fig. 11). In small (1-100 employees) organizations, sales is the third-strongest influencer, while finance ranks second at organizations with 1,001-10,000 employees and third in midsized (101-1,001 employees) organizations. BICC is less influential among small and midsized responding organizations, though we would not normally expect to see a business intelligence competency center (BICC) in smaller businesses and institutions. Functional drivers are more diversified at very large organizations of 10,000 or more employees where departmental oversight and separate budgets are common. Marketing holds above-average sway in both small and very large (> 10,000 employees) organizations Functions Driving Business Intelligence by Organization Size ,000 1,001-10,000 More than 10,000 Executive management Sales Information Technology (IT) Strategic planning function Business Intelligence Competency Center Human resources Manufacturing Finance Operations Marketing Research and development (R&D) Supply chain Faculty (education) Clinical (Healthcare) Figure 11 - Functions driving business intelligence by organization size 27

28 User Roles Targeted for Business Intelligence In 2016, executives and middle managers are about 93 percent likely to be targeted as primary or secondary users of business intelligence (fig. 12). Middle managers are less often seen as primary users but are as likely overall as executives to be targeted. Line managers are targeted as primary or secondary users at three-quarters of respondent organizations and individual contributors draw only slightly less attention. Fewer than half of respondent organizations (26 percent primary, 21 percent secondary) currently target customers. Targeted Users for Business Intelligence 0% 20% 40% 60% 80% 100% Executives Middle managers Line managers Individual contributors & professionals Customers Suppliers Primary Secondary Not targeted Figure 12 - Targeted users for business intelligence 28

29 Across three years of data, executives and middle managers have remained the mosttargeted audiences for business intelligence, though both have seen year-over-year declines of 3 to 4 percent, possibly due to saturation (fig. 13). Targeting of line managers saw a one-year decrease of 4 percent. Individual contributors, customers, and suppliers have remained mostly flat year over year. 80% Targeted Users for Business Intelligence 2014 to % 60% 50% 40% 30% 20% % 0% Executives Middle Managers Line Managers Individual Contributors and Professionals Customers Suppliers Figure 13 - Targeted users for business intelligence 2014 to

30 Targeted Users for Business Intelligence by Geography Executives are the most likely targets for business intelligence across all geographies, especially Latin America, where targeting is 80 percent compared to 60-plus percent in other regions (fig. 14). Middle managers are most targeted in North America (56 percent) followed by EMEA (54 percent) and Latin America (54 percent). Middle and line managers are less so but more equally targeted in Asia-Pacific organizations (37 to 42 percent). Latin American respondents in our 2016 sample rarely or never report targeting individual contributors and professionals. Targeted Users for Business Intelligence by Geography 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% North America Asia Pacific Latin America Europe, Middle East and Africa Executives Line managers Customers Middle managers Individual contributors & professionals Suppliers Figure 14 - Targeted users for business intelligence by geography 30

31 User Targets for Business Intelligence by Organization Size Large organizations (>1,000 employees) are somewhat less likely to target executive management than smaller organizations, which might simply reflect overall headcount (fig. 15). Large organizations of 1,001 to 10,000 employees are most likely to target middle managers, and at a rate equal or greater than executives. Very large organizations (> 10,000 employees) are slightly more likely to target line managers and individual contributors and professionals. Small organizations of one to 100 employees are most likely to target customers (31 percent). Targeted Users for Business Intelligence by Organization Size 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% ,000 1,001-10,000 More than 10,000 Executives Line managers Customers Middle managers Individual contributors & professionals Suppliers Figure 15 - Targeted business intelligence users by organization size 31

32 User Targets for Business Intelligence by Vertical Industries Our 2016 sample shows differences among vertical industries targeting users for business intelligence (fig. 16). Respondents in retail place the highest emphasis on executives and middle managers, paying less attention to rank-and-file managers and individual contributors and more-than-average attention to suppliers. Respondents in food and beverage, advertising, financial services, and insurance have the highest targeting of individual contributors. Business services, financial services, insurance, and food and beverage are more likely to emphasize customer enablement of BI (likely for services and applications addressing portfolio analysis, claims, and records). Suppliers are broadly underrepresented in every industry, even manufacturing. Targeted Users for Business Intelligence by Vertical Industry 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Executives Line managers Customers Middle managers Individual contributors & professionals Suppliers Figure 16 - Targeted business intelligence users by vertical industry 32

33 Objectives for Business Intelligence In 2016, the anecdotal and arguably philosophical goal of making better decisions again topped the list of business intelligence objectives (fig. 17). We have traditionally associated this goal with organizations seeking general improvements through the use of business intelligence wherever they are available. In perhaps another reflection of the 2016 emphasis on operations, improved operational efficiency was the second choice, followed by revenue growth, increased competitive advantage, and enhanced customer service. Just 7 percent or fewer respondents consider any of the offered BI objectives "unimportant." Business Intelligence Objectives 0% 20% 40% 60% 80% 100% Better decision-making Improved operational efficiency Growth in revenues Increased competitive advantage Enhanced customer service Critical Very important Important Somewhat important Unimportant Figure 17 - Business intelligence objectives 33

34 Business Intelligence Objectives by Geography Better decision making is the mantra for BI objectives across all geographical regions in 2016 and to a slightly greater extent in North America and Latin America (fig. 18). North American respondents also have the highest goals for improved operational efficiency, (the second most important objective in all regions except Asia Pacific where increased competitive advantage is slightly more important). Enhanced customer service is the trailing objective in all regions. 5 Business Intelligence Objectives by Geography North America Asia Pacific Latin America Europe, Middle East and Africa Better decision making Growth in revenues Enhanced customer service Improved operational efficiency Increased competitive advantage Figure 18 - Business intelligence objectives by geography 34

35 Business Intelligence Objectives by Function In 2015, emphasis toward business intelligence objectives varies by function (fig. 19). Not surprisingly, the greatest emphasis on revenue growth through the use of BI is highest among executives and is the clear standout objective for sales respondents. Finance respondents have high interest in better decision making and improved operational efficiency but below-average interest in revenues, competitive advantage, and customer service. Respondents in the BICC have high interest in all objectives and standout interest in the most neglected, in this case competitive advantage and customer service; this perhaps hints at future areas of expansion and investment. Interest in greater operational efficiency is a constant across all functions sampled. 5 Business Intelligence Objectives by Function Better decision-making Improved operational efficiency Growth in revenues Increased competitive advantage Enhanced customer service Executive Management Information Technology (IT) Business Intelligence Competency Center Marketing and Sales Finance Mean Figure 19 - Business intelligence objectives by function 35

36 Business Intelligence Objectives by Vertical Industry Better decision making is the BI objective respondents most strongly identify with across all industries in 2016, particularly insurance, healthcare, retail, and advertising (fig. 20). Healthcare (followed by food and beverage and financial services) is the most interested in improving operational efficiency. Retail and wholesale, with traditionally thin margins, takes the highest interest in revenue growth. Competitive advertising and churn-sensitive financial services organizations have the highest interest in increased competitive advantage. Customer service is most emphasized by business services and financial services. 5.0 Business Intelligence Objectives by Vertical Industry Better decision-making Improved operational efficiency Insurance Healthcare Retail & wholesale Advertising Growth in revenues Increased competitive advantage Enhanced customer service Financial services Business services Food, beverage and tobacco Mean Figure 20 - Business intelligence objectives by vertical industry 36

37 Business Intelligence Objectives by Organization Size Organizations of different sizes all place the highest emphasis on better decision making with similar mean importance of 4.2 to 4.4 (above "very important") (fig. 21). Improved operational efficiency is the next most important to all organizations and increases noticeably as organization headcount increases. Small organizations (1-100 employees) have a positive attitude toward multiple objectives and above-average interest in (customer-oriented) increased revenues and competitive advantage. Very large (> 10,000 employees) organizations place a similar above-average emphasis on revenues and competitive advantage and the strongest emphasis on enhanced customer services. 5 Business Intelligence Objectives by Organization Size ,000 1,001-10,000 More than 10,000 Improved operational efficiency Increased competitive advantage Enhanced customer service Growth in revenues Better decision-making Figure 21 - Business intelligence objectives by organization size 37

38 Penetration of Business Intelligence Solutions In a positive development, penetration of business intelligence (as a percentage of total employees) appears to have grown noticeably between 2015 and 2016 (fig. 22). Percentages of lower penetration (< 10 percent, percent, 21-40, 41-60) all declined while the highest levels (61-80 percent, > 81 percent) improved by similar amounts. The linear trending of this finding gives us some confidence that BI enablement and democratization is showing improvement over time. 40% Business Intelligence Penetration 2015 to % 30% 25% 20% % 10% 5% 0% Under 10% 11-20% 21-40% 41-60% 61-80% 81% or more Figure 22 Business intelligence penetration 2015 to

39 Expansion Plans for Business Intelligence Through 2018 Respondents very often describe bullish plans for expanding BI in future timeframes, and we usually consider the 12-month period the most likely to be supportable and budgeted (fig. 23). In this context, 20 percent of respondents (compared to 18 percent current) expect greater than 81 percent of penetration in the coming year, and 8 percent (versus 4 percent current) expect 61 percent to 80 percent penetration. This may be achievable under current reported year-over-year growth. Longer timeframes predict slightly greater, if not wild rates of growth in BI penetration and a stubborn small minority (about 13 percent) with very low BI penetration of less than 10 percent. Expansion Plans for Business Intelligence through 2019 In 12 months In 24 months In 36 months 0% 20% 40% 60% 80% 100% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% Figure 23 - Expansion plans for business intelligence through

40 Current Business Intelligence Penetration by Geography Globally, the highest levels of current BI penetration are reported in North America and EMEA where solutions have generally been more available and widespread (fig. 24). In contrast, the highest level of > 81 percent penetration is 10 percent in Latin America and Asia Pacific (compared to 17 to18 percent in EMEA and North America). Latin America and Asia Pacific also report significantly larger segments of low penetration (< 10 percent, percent, 21-40, 41-60) than do respondents in North America and EMEA. 100% Penetration of Business Intelligence Solutions Today by Geography 90% 80% 70% 60% 50% 40% 30% 20% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% 10% 0% North America Europe, Middle East and Africa Asia Pacific Latin America Figure 24 - Business intelligence user penetration today by geography 40

41 In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months 2016 Wisdom of Crowds Business Intelligence Market Study Planned Business Intelligence Penetration by Geography A comparative view of future plans by geography supports the theme of somewhat steady and growing expectations for expanding business intelligence in 12, 24 and 36- month timeframes (fig. 25). Penetration of the highest rates (> 81 percent, percent) are likewise highest in North America, followed by EMEA, Latin America, and Asia Pacific. Thirty-six-month penetration of 40 percent or more is predicted to be between 66 and 77 percent in all geographies. 100% 90% 80% 70% 60% Expansion Plans for Business Intelligence through 2019 by Geography 50% 40% 30% 20% 10% 0% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% North America Asia Pacific Latin America Europe, Middle East and Africa Figure 25 - Planned business intelligence user penetration through 2019 by geography 41

42 Business Intelligence Penetration by Function Executives report the most aggressive levels of BI penetration by function (fig. 26). At the opposite end of this spectrum, finance is the most conservative when it comes to adopting BI solutions. Somewhat surprisingly, sales and marketing ranks toward the bottom of this order, behind IT and BICC, with only about 50 percent reporting 21 percent or more penetration. 100% 90% 80% 70% Penetration of Business Intelligence Solutions Today by Function 60% 50% 40% 30% 20% 10% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% 0% Executive Management Information Technology (IT) Business Intelligence Competency Center Marketing and Sales Finance Figure 26 Business intelligence penetration today by function 42

43 In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months 2016 Wisdom of Crowds Business Intelligence Market Study All functions expect to increase BI penetration over time (fig. 27). Executives report the most aggressive plans for coming timeframes, though the highest level of penetration (81 percent or greater) will grow only 2 to 3 percent per year. Growth expectations for very high penetration are similar for other functions, which place higher expectations for improvement on middle ranked or modestly penetrated audiences. Expansion Plans for Business Intelligence through 2019 by Function 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% Executive Management Marketing and Sales Information Technology (IT) Finance Business Intelligence Competency Center Figure 27 Expansion plans for business intelligence through 2019 by function 43

44 Current Business Intelligence Penetration by Vertical Industry Higher levels of BI penetration vary inconsistently across different vertical industries (ranked by descending mean value in fig. 28). In our 2016 sample, business services reports the highest levels of 81 percent or greater penetration while midlevel BI penetration is higher in financial services. Low levels of penetration may be more informative, as in the case of healthcare, education, and manufacturing where 30 percent or fewer organizations report BI penetration greater than 20 percent. 100% Penetration of Business Intelligence Solutions Today by Selected Vertical Industry 90% 80% 70% 60% 50% 40% 30% 20% 10% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% 0% Figure 28 Penetration of business intelligence solutions today by selected vertical industry 44

45 In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months 2016 Wisdom of Crowds Business Intelligence Market Study Planned Business Intelligence Penetration by Vertical Industry In our 2016 sample, financial services and business services are the industries expecting the highest BI expansion plans in future timeframes (fig. 29). Healthcare education and manufacturing are progressively less aggressive in 12, 24 and 36-month timeframes. In manufacturing and education for example, fewer than 40 percent of organizations expect greater than 20 percent penetration 12 months from now. 100% 90% 80% 70% 60% Expansion Plans for Business Intelligence through 2019 by Industry 50% 81% or more 40% 61-80% 30% 41-60% 20% 10% 0% 21-40% 11-20% Under 10% Healthcare Financial services Business services Education (Higher Ed) Manufacturing Figure 29 - Expansion plans for business intelligence through 2019 by industry 45

46 Current Business Intelligence Penetration by Organization Size As we have reported in every year of our study, small organizations of one to 100 employees have significantly higher BI penetration than larger peers (fig. 30). In the modern context, we might expect small organizations, likely to be newer and comprised of more information workers, would find fewer barriers of cost or deployment and more immediate benefits than larger and older companies. As we have also seen in earlier studies, very high penetration rates tend to decrease with organization size, though headcount would offset this difference. 100% Penetration of Business Intelligence Solutions Today by Organization Size 90% 80% 70% 60% 50% 40% 30% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% 20% 10% 0% ,000 1,001-10,000 More than 10,000 Figure 30 - Penetration of business intelligence solutions today by organization size 46

47 In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months 2016 Wisdom of Crowds Business Intelligence Market Study Planned Business Intelligence Penetration by Organization Size Along with being the most mature BI users today, small organizations (1-100) have the steepest expectations for high future penetration of business intelligence in coming timeframes (fig. 31). Midsized organizations (101-1,000 employees) expect the nexthighest number of highly penetrated (> 60 percent) user audiences. Large and very large (> 10,000 employees) organizations have somewhat lower expectations (which may be colored by overall rank and file headcount not considered audiences for business intelligence). 100% Expansion Plans for Business Intelligence through 2019 by Organization Size 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% ,000 1,001-10,000 More than 10,000 Figure 31 - Expansion plans for business intelligence through 2019 by organization size 47

48 Chief Data and Chief Analytics Officers For the first time, in 2016 we asked our audience whether their organization had appointed a chief data officer (CDO) or chief analytics officer (CAO). We understand that these appointments can represent significant evolutionary updates in the technology and business architecture of organizations and also that these roles and titles are relatively new, fluid by definition and evolving. Enterprises with Chief Data or Chief Analytics Officers The ongoing uptake and maturity of chief data and chief analytics officers is modest to date, with fewer than 15 percent of respondent organizations having adopted the title(s) (figs. 32, 33). Among those with one (or both) titles in the organization, maturity ranges fairly evenly between spans of less than one year to more than five years. We note that both titles have similar adoption and acceptance in organizations to date. 3.6% Enterprises with a Chief Data Officer 3.9% 2.0% 4.3% 3.4% Enterprises with a Chief Analytics Officer 3.6% 2.8% 3.6% 86.3% 86.5% Not applicable For less than 1 year 1-3 years 3-5 years More than 5 years Not applicable For less than 1 year 1-3 years 3-5 years More than 5 years Figures 32; 33 - Enterprises with chief data or chief analytics officers 48

49 Enterprises with Chief Data or Chief Analytics Officers by Geography The title of chief data officer has the greatest overall penetration and the most new activity in EMEA, followed by Asia Pacific (fig. 34). This finding might be surprising on the surface, though the (albeit minority) CDO audience does have the greatest longterm standing in North America. The title of chief analytics officer has the greatest overall penetration in Asia Pacific and the largest number of new appointments of oneyear or less. EMEA has similar adoption rates for both CDOs and CAOs, while North America is somewhat more likely to adopt the CDO versus the CAO. Latin American respondents have comparatively small adoption of either title. 20% 18% 16% 14% 12% Enterprises with Chief Data or Chief Analytics Officers by Geography 10% 8% 6% 4% More than 5 years 3-5 years 1-3 years For less than 1 year 2% 0% North America Asia Pacific Latin America Chief Data Officer Europe, Middle East and Africa North America Asia Pacific Latin America Chief Analytics Officer Europe, Middle East and Africa Figure 34 - Enterprises with chief data or chief analytics officers by geography 49

50 Technology Healthcare Education (Higher Ed) Financial services Business services Insurance Technology Healthcare Education (Higher Ed) Financial services Business services Insurance 2016 Wisdom of Crowds Business Intelligence Market Study Enterprises with Chief Data or Chief Analytics Officers by Industry The financial services industry plainly leads appointment rates for chief data officers at a rate approaching twice the overall average (fig. 35). This statistical finding resonates with anecdotal evidence in studies and presentations in the public domain so far. Most new activity occurs in insurance, business services, and technology. Conversely, the healthcare industry trails the vertical audience for CDOs, which may reflect the historical state of information fragmentation in healthcare (at the same time it presents a glaring business case). Longstanding appointments of chief analytics officers are predictably highest in the insurance industry, though most new CAO appointments appear in business services. 30% Enterprises with Chief Data or Chief Analytics Officers by Industry 25% 20% 15% 10% 5% 0% More than 5 years 3-5 years 1-3 years For less than 1 year Chief Data Officer Chief Analytics Officer Figure 35 - Enterprises with chief data or chief analytics officers by industry 50

51 Enterprises with Chief Data or Chief Analytics Officers by Organization Size Appointments of chief data officers and chief analytics officers are, not surprisingly, a mostly large-organization phenomenon (fig. 36). Small organizations of one to 100 employees, including startups, might selectively need the CDO title, though we expect this might be inclusive of other duties and not a dedicated position (again depending on definition). Chief analytics officer appears a more appropriate title at small organizations more likely to focus on core processes, though new activity is strongest for both titles at small organizations. Midsized organizations of 101 to 1,000 employees are less active than smaller and larger peers. 30% Enterprises with Chief Data or Chief Analytics Officers by Organization Size 25% 20% 15% 10% 5% More than 5 years 3-5 years 1-3 years For less than 1 year 0% ,000 1,001-10,000 More than 10, ,000 1,001-10,000 More than 10,000 Chief Data Officer Chief Analytics Officer Figure 36 - Enterprises with chief data or chief analytics officers by organization size 51

52 Enterprises with Chief Data and Chief Analytics Officers Reporting Structure Among organizations with a CDO or CAO, both titles are most likely to report in to the CEO (fig. 37). In the case of the CAO, this is especially the case an indication of the true or perceived business strategy implications of analytical expertise versus the traditional wrangling of data. In this regard, CAOs are more likely to report to finance than to IT, while CDOs have a pronounced IT reporting flavor. 35% Chief Data and Chief Analytics Officers Reporting Structure 30% 25% 20% 15% 10% 5% 0% Chief Data Officer Chief Analytics Officer CEO CFO CMO CIO Other Figure 37 - Chief data and chief analytics officer reporting structure 52

53 Chief Data Officer and Success with BI by Reporting Structure Organizations with a chief data officer that are also successful with business intelligence are most likely by far to report into the CEO or CIO, followed by finance (fig. 38). Organizations with a CDO that are unsuccessful with BI are most likely to report to IT or the chief marketing officer. By this measure, we might suggest that CDOs at most successful BI organizations are broadly connected to high-level business resources (senior executives, successful IT departments, and finance), and not dedicated to siloed programs or campaigns. 35% Chief Data Officer and Success with BI by Reporting Structure 30% 25% 20% 15% CEO CFO CMO CIO 10% 5% 0% Successful with BI Unsuccessful with BI Figure 38 - Chief data officer and success with BI by reporting structure 53

54 Chief Analytics Officer and Success with BI by Reporting Structure Like CDOs, chief analytics officers at organizations that are successful with business intelligence are most likely to report into the CEO (fig. 39). Notably, businesses and institutions with a chief analytics officer that reports to IT are considerably more likely to be organizations that perform poorly at business intelligence. Chief analytic officers in successful BI organizations that report to marketing are more likely to experience success than their CDO peers. 45% Chief Analytics Officer and Success with BI by Reporting Structure 40% 35% 30% 25% 20% 15% CEO CFO CMO CIO 10% 5% 0% Successful with BI Unsuccessful with BI Figure 39 - Chief analytics officer and success with BI by reporting structure 54

55 Number of Business Intelligence Tools in Use Number of Business Intelligence Tools in Use 2014 to 2016 Over time, we see some shifting in the number of business intelligence tools in use by organizations accompanied by somewhat improved awareness (fewer "don't know") in the organizational BI environment (fig. 40). It remains most likely (even more so in 2016) that organizations will continue to employ one, two, or three BI tools. The number of organizations using four, five, or more tools has flattened by comparison, though we would not yet suggest an obvious consolidation of tool use. 30% Numbers of Business Intelligence Tools in Use 2014 to % 20% 15% % 5% 0% Don't know More than 5 Figure 40 - Number of business intelligence tools in use 2014 to

56 Number of Business Intelligence Tools by Geography North America and EMEA organizations are more likely than respondents in other geographies to use multiple business intelligence tools (fig. 41). While geographical differences remain, they are less distinctive than in earlier studies. Asia-Pacific respondents now have high tool usage compared to other geographies, and a majority of Latin American respondents report using more than one BI tool. Number of Business Intelligence Tools in Use by Geography North America Europe, Middle East and Africa Asia Pacific Latin America 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% One Two Three to six Seven or more Don't know Figure 41 - Numbers of business intelligence tools in use by geography 56

57 Number of Business Intelligence Tools by Function Executive management is most likely to report one or two business intelligence tools in use and the most BI tool awareness (fig. 42). Marketing and sales is 80 percent likely to report one, two, or three to six tools in use. BICCs, IT, and even finance departments (perhaps in budgetary oversight) are likely to report high tool proliferation. Number of Business Intelligence Tools in Use by Function Marketing and Sales Business Intelligence Competency Center Research and Development (R&D) Executive Management Finance Information Technology (IT) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% One Two Three to six Seven or more Don't know Figure 42 - Numbers of business intelligence tools in use by function 57

58 Number of Business Intelligence Tools by Vertical Industry By function, manufacturing reports the lowest average number of business intelligence tools in use, likely tied to existing material resource planning and asset management investments (fig. 43). Business services is even more conservative in tool use, which may reflect sample organization size. Healthcare, with multiple fragmented systems and financial services report the highest number of business tools in use. Number of Business Intelligence Tools in Use by Vertical Industry Manufacturing Business services Education (Higher Ed) Financial services Healthcare 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% One Two Three to six Seven or more Don't know Figure 43 - Numbers of business intelligence tools in use by vertical industry 58

59 Number of Business Intelligence Tools by Organization Size Organization size historically and currently correlates to the number of business intelligence tools in use (fig. 44). Organizations of 100 or fewer employees (closely followed by midsized organizations of 101-1,000 employees) are most likely to report one or two BI tools in use. Just 9 percent of very large (>10,000 employees) organizations have one business intelligence tool in use and another 17 percent use only two tools. Awareness to the number of tools in use decreases predictably as organization size increases. Number of Business Intelligence Tools in Use by Organization Size ,000 1,001-10,000 More than 10,000 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% One Two Three to six Seven or more Don't know Figure 44 - Numbers of business intelligence tools in use by organization size 59

60 Technologies and Initiatives Strategic to Business Intelligence Fundamental business intelligence technologies reporting, dashboards, end-user selfservice, and advanced visualization top our 2016 list of technologies and initiatives among 30 topics currently under study (fig. 45). Enterprise data warehousing fell from the top five to sixth place behind data discovery. Second-tier initiatives include data mining, enterprise planning, mobile BI, and embedded BI. Compared to longstanding BI assets, some hot button topics including cognitive BI, social media analytics, and the Internet of Things still remain fringe priorities among respondents. Technologies and Initiatives Strategic to Business Intelligence 0% 20% 40% 60% 80% 100% Reporting Dashboards End-user "self-service" Advanced visualization Data discovery Data warehousing Data mining, advanced algorithms, predictive Integration with operational processes Data storytelling Enterprise planning/budgeting Mobile device support Embedded BI (contained within an application, Governance Collaborative support for group-based analysis End-user data preparation and blending Search-based interface Software-as-a-service and cloud computing In-memory analysis Ability to write to transactional applications Location intelligence/analytics Big Data (e.g., Hadoop) Pre-packaged vertical/functional analytical Text analytics Streaming data analysis Open source software Social media analysis (SocialBI) Cognitive BI (e.g., Artificial Intelligence-based BI) Complex event processing (CEP) Internet of things (IoT) Edge computing Critical Very important Important Somewhat important Not important Figure 45 - Technologies and initiatives strategic to business intelligence 60

61 Technology Priority Changes from 2013 Technology priority rankings have remained fairly consistent since 2013 (fig. 46). Reporting debuted atop priorities; dashboards and advanced visualization gained some momentum as data warehousing declined slightly as previously noted. Enterprise planning and embedded BI declined as did lower priorities of location intelligence, prepackaged applications, and the Internet of Things. If the past is precedent, these declines and lower priorities may come to rebound in future studies as more use cases and maturity arrive Technology Priorities 2014 to Figure 46 - Technology priorities 2014 to

62 Another instructive view of year-over-year changes in business intelligence priorities is shown in figure 47. In this view, ability to write to transactional applications grew most by percentage, while cognitive BI and data discovery made leapfrog improvements. Open source software and search-based interface gains are also worth noting. Sentiment declined most notably toward location intelligence and prepackaged analytical applications (for a second consecutive year), followed by operational integration. As noted, data warehousing and IoT declined, while SaaS/cloud flattened and reversed losses in prior years. Momentum adds color to our overall rankings, but short-term swings in sentiment may or may not confirm trends. Technology Priority Changes 2015 to 2016 Ability to write to transactional applications Cognitive BI Data discovery Open source software Search-based interface Collaborative support for group-based analysis Advanced visualization Dashboards Big data (e.g., Hadoop) Text analytics Software-as-a-service and cloud computing End-user "self-service" Social media analysis (Social BI) Data mining, advanced algorithms, predictive Enterprise planning End-user data preparation and blending Mobile device support Complex event processing (CEP) Data warehousing "Embedded" BI In-memory analysis Internet of Things Integration with operational processes Pre-packaged analytical applications Location intelligence/analytics -11% -6% -1% 4% 9% Figure 47 - Technology priority changes 2015 to

63 Technologies and Initiatives Strategic to Business Intelligence by Geography By region, North America has the greatest interest in reporting, dashboards, end-user self-service, data discovery, data storytelling, and software as a service/cloud (fig. 48). Asia Pacific shares equal or high interest in these same areas and leads current demand for data warehousing, integration with operational systems, governance, collaborative support, in-memory, and several lesser categories. Latin American respondents have comparatively high interest in mobile device support, governance, end-user data preparation, and search-based interest. EMEA respondents do not lead any categories and generally report average or less interest in specific objectives. Technologies and Initiatives Strategic to Business Intelligence Objectives by Geography Cognitive BI (e.g., Artificial Social media analysis (SocialBI) Open source software Streaming data analysis Text analytics Edge computing Internet of things (IoT) Complex event processing (CEP) Reporting Dashboards End-user "self-service" Advanced visualization Data discovery Data warehousing Data mining, advanced Integration with operational Data storytelling Pre-packaged Big Data (e.g., Hadoop) Location intelligence/analytics Enterprise planning/budgeting Mobile device support Embedded BI (contained within Ability to write to transactional Governance In-memory analysis Collaborative support for group- Software-as-a-service and cloud End-user data preparation and Search-based interface North America Asia Pacific Latin America Europe, Middle East and Africa Figure 48 - Technologies and initiatives strategic to business intelligence objectives by geography 63

64 Technologies and Initiatives Strategic to Business Intelligence by Function Functional attitudes toward BI technologies and initiatives generally relate to specific daily roles and responsibilities (fig. 49). The BICC, responsible for support services across BI objectives, leads prioritization of many or most categories. On-the-go marketing/sales, followed by executive management users depend on dashboards. Marketing and sales also shows high interest in data storytelling and search-based interface while finance expectedly leads interest in enterprise planning initiatives. Technologies and Initiatives Strategic to Business Intelligence by Function Cognitive BI (e.g., Artificial Social media analysis (SocialBI) Open source software Streaming data analysis Text analytics Edge computing Internet of things (IoT) Complex event processing (CEP) Reporting Dashboards End-user "self-service" Advanced visualization Data discovery Data warehousing Data mining, advanced Integration with operational Data storytelling Pre-packaged vertical/functional Big Data (e.g., Hadoop) Location intelligence/analytics Enterprise planning/budgeting Mobile device support Embedded BI (contained within Ability to write to transactional Governance In-memory analysis Collaborative support for group- Software-as-a-service and cloud End-user data preparation and Search-based interface Executive Management Information Technology (IT) Business Intelligence Competency Center Marketing and Sales Finance Figure 49 - Technologies and initiatives strategic to business intelligence by function 64

65 Technologies and Initiatives Strategic to Business Intelligence by Vertical Industry Vertical industries describe a range of interest in their different 2016 business intelligence initiatives and priorities (fig. 50). Manufacturing and healthcare show the most interest in reporting, while advertising and healthcare lead interest in dashboards. Manufacturing also reports high interest in advanced visualization, integration with operational systems, cloud, and location intelligence. Insurance predictably leads interest in data warehousing and data mining, while advertising has relatively singular interest in data storytelling. Financial services leads interest in mobile device support and governance. Business services shows only average interest in most or all initiatives, and higher education is least interested generally. Technologies and Initiatives Strategic to Business Intelligence by Vertical Industry Cognitive BI (e.g., Artificial Social media analysis (SocialBI) Open source software Streaming data analysis Text analytics Edge computing Internet of things (IoT) Complex event processing (CEP) Reporting Dashboards End-user "self-service" Advanced visualization Data discovery Data warehousing Data mining, advanced Integration with operational Data storytelling Pre-packaged vertical/functional Big Data (e.g., Hadoop) Location intelligence/analytics Enterprise planning/budgeting Mobile device support Embedded BI (contained within Ability to write to transactional Governance In-memory analysis Collaborative support for group- Software-as-a-service and cloud End-user data preparation and Search-based interface Financial services Advertising Manufacturing Business services Insurance Healthcare Education (Higher Ed) Figure 50 - Technologies and initiatives strategic to business intelligence by vertical industry 65

66 Technologies and Initiatives Strategic to Business Intelligence by Organization Size Business intelligence priorities vary by organization size, though generally, very large organizations lead interest in most technologies and initiatives under study in 2016 (fig. 51). That said, at the top of the list, midsized organizations of 101 to 1,000 employees actually lead demand for reporting, and small (1-100 employees) organizations slightly lead demand for dashboards. We note that though these top two responses are tightly grouped with comparatively high importance compared to all others. An exception to large organization leadership is found in software as a service and cloud, where small businesses lead adoption. At a lower level, small organizations are more likely to pursue open source software and social BI. Technologies and Initiatives Strategic to Business Intelligence by Organization Size Cognitive BI (e.g., Artificial Social media analysis (SocialBI) Open source software Streaming data analysis Text analytics Edge computing Internet of things (IoT) Complex event processing (CEP) Reporting Dashboards End-user "self-service" Advanced visualization Data discovery Data warehousing Data mining, advanced Integration with operational Data storytelling Pre-packaged vertical/functional Big Data (e.g., Hadoop) Location intelligence/analytics Enterprise planning/budgeting Mobile device support Embedded BI (contained within Ability to write to transactional Governance In-memory analysis Collaborative support for group- Software-as-a-service and cloud End-user data preparation and Search-based interface ,000 1,001-10,000 More than 10,000 Figure 51 - Technologies and initiatives strategic to business intelligence by organization size 66

67 Business Intelligence and the State of Data Beginning in 2014, we polled respondents for attitudes and behaviors reflective of the state of data in their organizations (fig. 52). As the choices describe, a good majority (64 percent) of organizations say they either see data as truth or maintain a common enterprise view of data limited by parochial views and semantics. Twenty-four percent report consistent department-level data and 12 percent report the worst state of data, multiple inconsistent data sources with conflicting semantics and data. Business Intelligence and the State of Data We have multiple, inconsistent data sources with conflicting semantics and data. Information is generally unreliable and distrusted 12% Consistent data is available at a departmental level. Conflicting, functional views of data causes confusion and disagreement 24% A common view of enterprise data is available. However, parochial views and semantics are used to support specific positions 38% Data as "truth" - A common view of enterprise data is available with common application of data, filters, rules, and semantics. 26% 0% 5% 10% 15% 20% 25% 30% 35% 40% Figure 52 - Business intelligence and the state of data 67

68 In the last two years, respondents opinions of their state of data has shifted to a slightly more skeptical point of view (fig. 53). While 2 percent more see the highest level of data as truth, 4 percent fewer describe a common view of data and 2 percent more respondents say consistent data is available at a department level. The percentage of organizations with multiple inconsistent sources of data is unchanged. 45% Business Intelligence and The State of Data 2015 to % 35% 30% 25% 20% 15% 10% % 0% Data as "truth" - A common view of enterprise data is available with common application of data, filters, rules, and semantics. A common view of enterprise data is available. However, parochial views and semantics are used to support specific positions Consistent data is available at a departmental level. Conflicting, functional views of data causes confusion and disagreement We have multiple, inconsistent data sources with conflicting semantics and data. Information is generally unreliable and distrusted Figure 53 - Business intelligence and the state of data 2015 to

69 Business Intelligence and the State of Data by Geography Estimations of organizational data maturity vary by regional geography (fig. 54). EMEA is most comfortable with the quality and use of their data, followed by Asia Pacific and North America, which reports the lowest self-estimation of "data as truth." Latin America trails the state of data reported in other regions, though not significantly. 100% 90% 80% 70% 60% 50% Business Intelligence and the State of Data by Geography Data as "truth" - A common view of enterprise data is available with common application of data, filters, rules, and semantics. A common view of enterprise data is available. However, parochial views and semantics are used to support specific positions 40% 30% 20% Consistent data is available at a departmental level. Conflicting, functional views of data causes confusion and disagreement 10% 0% Europe, Middle East and Africa Asia Pacific North America Latin America We have multiple, inconsistent data sources with conflicting semantics and data. Information is generally unreliable and distrusted Figure 54 - Business intelligence and the state of data by geography 69

70 Business Intelligence and the State of Data by Function Executive management is most likely to report the highest common view of data as truth or a common view of enterprise data (fig. 55). The BICC, however, is most likely to affirm the highest state of data achievement. Marketing and sales, often BI frontrunners, are the next most-mature functions. IT and finance report higher levels of department-level or inconsistent data. Business Intelligence and the State of Data by Function 100% 90% 80% 70% 60% 50% 40% 30% 20% Data as "truth" - A common view of enterprise data is available with common application of data, filters, rules, and semantics A common view of enterprise data is available. However, parochial views and semantics are used to support specific positions Consistent data is available at a departmental level. Conflicting, functional views of data causes confusion and disagreement 10% 0% Executive Management Business Intelligence Competency Center Marketing and Sales Information Technology (IT) Finance We have multiple, inconsistent data sources with conflicting semantics and data. Information is generally unreliable and distrusted Figure 55 - Business intelligence and the state of data by function 70

71 Business Intelligence and the State of Data by Vertical Industry By industry, 70 percent of insurance respondents are most likely to report the two highest levels of their organizations' state of data (fig. 56). Business services, higher education, and healthcare are next most likely to predict high states of data, followed by financial services and manufacturing. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Business Intelligence and the State of Data by Selected Vertical Industry Data as "truth" - A common view of enterprise data is available with common application of data, filters, rules, and semantics A common view of enterprise data is available. However, parochial views and semantics are used to support specific positions Consistent data is available at a departmental level. Conflicting, functional views of data causes confusion and disagreement We have multiple, inconsistent data sources with conflicting semantics and data. Information is generally unreliable and distrusted Figure 56 - Business intelligence and the state of data by selected vertical industry 71

72 Business Intelligence and the State of Data by Organization Size Smaller organizations that, on average, manage a smaller scope of data than larger peers are most likely to have their act together with a more mature state of data than larger peers (fig. 57). Moving left to right, we see the state of data tends to become less coordinated and more fragmented as organization headcount increases. Less than 17 percent of organizations of any size report the lowest state on multiple, inconsistent data sources. 100% 90% 80% 70% 60% 50% 40% 30% Business Intelligence and the State of Data by Organization Size Data as "truth" - A common view of enterprise data is available with common application of data, filters, rules, and semantics A common view of enterprise data is available. However, parochial views and semantics are used to support specific positions Consistent data is available at a departmental level. Conflicting, functional views of data causes confusion and disagreement 20% 10% 0% ,000 1,001-10,000 More than 10,000 We have multiple, inconsistent data sources with conflicting semantics and data. Information is generally unreliable and distrusted Figure 57 - Business intelligence and the state of data by organization size 72

73 Business Intelligence and Action on Insight In 2014, we introduced action on insight, a high-level self-assessment of best (and worst) practices in organizational use of data. In 2016, respondents paint a very positive picture of their data usage (fig. 58). Eighty-three percent of respondents say they have either "closed loop" or ad hoc action on insights, meaning that they actively share BIderived insights with colleagues. Just 12 percent report uncoordinated or parochial action and only 5 percent say insights are rarely leveraged. 60% Business Intelligence and Action on Insight 55% 50% 40% 30% 28% 20% 12% 10% 5% 0% "Closed loop" - Information is shared, teams work to process and act in a timely fashion. No formal boundaries Ad hoc (informal) action on insights across functions Uncoordinated/ parochial action (sometimes at the expense of others) Insights are rarely leveraged Figure 58 - Business intelligence and action on insight 73

74 Across three years of data, we have seen a slightly negative trend in estimations of organizational ability to take action on insight (fig. 59). While we see a 2 percent yearover-year improvement in "closed loop" success, somewhat more negative views have increased slightly. With that said, a very small minority of 5 percent of respondents are fully doubtful of their organizational ability to take action on insight. 70% 60% Business Intelligence and Action on Insight 2014 to % 60% 55% 50% 40% 30% 28% 26% 24% % 10% 10% 11% 12% 4% 4% 5% % Closed-loop processes for action - Information is shared, teams work to process and act in a timely fashion. No formal boundaries Ad hoc (informal) action on insights across functions Uncoordinated/ parochial action (sometimes at the expense of others) Insights are rarely leveraged Figure 59 Business intelligence and action on insight 2014 to

75 Business Intelligence and Action on Insight by Geography Organizational estimations of the ability to take action on insight run strongest in Asia Pacific, followed by EMEA (fig. 60). This geographical finding jibes with our 2016 state of data also (fig. 54, p. 70). North America respondents are slightly less confident and Latin America respondents trails in perceived ability to execute with data insights. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Business Intelligence and Action on Insight by Geography "Closed loop" - Information is shared, teams work to process and act in a timely fashion. No formal boundaries Ad hoc (informal) action on insights across functions Uncoordinated/ parochial action (sometimes at the expense of others) Insights are rarely leveraged 0% Asia Pacific Europe, Middle East and Africa North America Latin America Figure 60 - Business intelligence and action on insight by geography 75

76 Business Intelligence and Action on Insight by Function BICC respondents, with the broadest account of business intelligence users, are most likely to claim ability to take action on insight, particularly at the highest level of closedloop processes (fig. 61). "On-the-go" users in sales/marketing and executive ranks are the next most likely to say they are adept at taking action on data insights. Of the functions studied, IT and finance are least likely to coordinate action on insight in % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Business Intelligence Competency Center Business Intelligence and Action on Insight by Function Marketing and Sales Executive Management Finance Information Technology (IT) "Closed loop" - Information is shared, teams work to process and act in a timely fashion. No formal boundaries Ad hoc (informal) action on insights across functions Uncoordinated/ parochial action (sometimes at the expense of others) Insights are rarely leveraged Figure 61 - Business intelligence and action on insight by function 76

77 Business Intelligence and Action on Insight by Vertical Industry Industries generally exhibit high confidence in their ability to take action on insight (fig. 62). In our 2016 sample, respondents in financial services, insurance, and business services are the most confident in their ability to act on data. Manufacturing and higher education trail in this ranking and are most likely to report uncoordinated or unleveraged insights. 100% Business Intelligence and Action on Insight by Vertical Industry 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% "Closed loop" - Information is shared, teams work to process and act in a timely fashion. No formal boundaries Ad hoc (informal) action on insights across functions Uncoordinated/ parochial action (sometimes at the expense of others) Insights are rarely leveraged Figure 62 - Business Intelligence and action on insight by vertical industry 77

78 Business Intelligence and Action on Insight by Organization Size Though small (1-100 employees) and very large (>10,000 employees) organizations fare best, organization size does not bear greatly on the ability to take action on insights (fig. 63). Close to or well more than 80 percent of organizations claim "closed loop" or ad hoc abilities; 6 percent or fewer organizations of any size say insights are rarely leveraged. 100% 90% 80% 70% 60% Business Intelligence and Action on Insight by Organization Size "Closed loop" - Information is shared, teams work to process and act in a timely fashion. No formal boundaries Ad hoc (informal) action on insights across functions 50% 40% 30% 20% Uncoordinated/ parochial action (sometimes at the expense of others) Insights are rarely leveraged 10% 0% ,000 1,001-10,000 More than 10,000 Figure 63 - Business intelligence and action on insight by organization size 78

79 Success with Business Intelligence In 2016, our core measure of success with business intelligence showed a small net gain but no dramatic movement (fig. 64). The number of respondents that say their organizations are successful with Bl increased 3 percent, rebounding from a trough in This success was offset by 3 percent fewer who responded "somewhat successful" in our poll. Respondents reporting "somewhat unsuccessful" and "unsuccessful" are essentially flat year over year. 60% Success with Business Intelligence 2013 to % 48% 49% 53% 50% 40% 41% 41% 38% 35% 30% 20% 10% 8% 8% 10% 9% 0% 3% 2% 2% 3% Successful Somewhat successful Somewhat unsuccessful Unsuccessful Figure 64 - Success with business intelligence 2013 to

80 Reasons Why Business Intelligence Succeeds Tag cloud analysis of BI success flows through classic expectations of business, data, "information," and customers (fig. 65). "Analytics" and "tools" found growing importance as emphasis on particular roles softened. Positive expectations ("more," "good," "successful") reflect the general tone in Primary reasons for success with BI carried over from 2015 and include senior management view BI as strategic, a stable organization, focus on critical opportunities and the requisite skills to deliver solutions. Figure 65 - Reasons for success with business intelligence 80

81 Reasons Why Business Intelligence Fails Asked for reasons why business intelligence fails, respondents point to "data" as a moving target of quality, integration, security, etc. and their "lack" of ability to access, manipulate, and use data (fig. 66). They see shortfalls of "resources" and "analytics" and are not convinced that "management" has grasped issues surrounding "support," "training," "adoption," and "use." Primary reasons for failure carry over from 2015 and include cumbersome processes, too much focus upon technology versus solving business problems and a lack of skills and resources to deliver solutions. Figure 66 - Reasons why business intelligence fails 81

82 Success with Business Intelligence by Organization Size The smallest organizations are most likely (47 percent) to consider themselves completely successful, and more than 90 percent say they are at least somewhat successful with business intelligence (fig. 67). In larger organizations, between 30 percent and 36 percent are likely to claim complete success. As we see elsewhere in our survey data, reports of success tend to decline with organizational size until they rebound slightly with the very largest. Nonetheless, organizations of more than 100 employees are between 84 percent and 87 percent likely to say they are, at minimum, somewhat successful with BI. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Success with Business Intelligence by Organization Size Completely agree Agree somewhat Disagree somewhat Disagree Mean Figure 67 - Success with business intelligence by organization size 82

83 Success with Business Intelligence by BI Objectives Organizations that are successful with business intelligence focus on the full range of objectives we sampled in 2016 (fig. 68). The most popular objective of successful BI organizations is better decision making followed in somewhat equal measure by improved operational efficiency, revenue growth, and increased competitive advantage. Organizations that consider themselves unsuccessful are less emphatic in all areas, likely to focus most on better decision making, operational improvements, and customers, and pay less attention to revenue growth or competitive advantage. 5 Success with Business Intelligence by BI Objectives Better decision making Improved operational efficiency Growth in revenues Increased competitive advantage Enhanced customer service Successful Somewhat successful Somewhat unsuccessful Unsuccessful Mean Figure 68 - Success with business intelligence by BI objectives 83

84 Success with Business Intelligence by Targeted Users Executives top the target priorities at organizations that are successful, somewhat successful, and somewhat unsuccessful at business intelligence (fig. 69). In contrast, the least successful organizations deemphasize all target groups but do not place disproportionate emphasis on executive audiences. Unsuccessful organizations place much less emphasis on customers and disproportionately high emphasis on individual contributors. Unsuccessful organizations may thus be more ad hoc in nature and uncoordinated structurally. 80% Success With Business Intelligence by Targeted Users 70% 60% 50% 40% 30% 20% 10% 0% Successful Somewhat successful Somewhat unsuccessful Unsuccessful Executives Line managers Customers Middle managers Individual contributors & professionals Suppliers Figure 69 - Success with business intelligence by targeted users 84

85 Success with Business Intelligence and Technology Priorities Organizations that are successful with business intelligence broadly pay much more attention to BI-related technology priorities than all other groups (fig. 70). Unsuccessful organizations place much less emphasis on dashboards than more successful businesses and have the highest interest in (albeit lower priority) prepackaged vertical/functional analytical applications. In general, beyond reporting and dashboards, less successful BI organizations appear hesitant toward BI technologies and priorities. Technologies and Initiatives Strategic to Business Intelligence by BI Success Complex event processing (CEP) Cognitive BI (e.g., Artificial Social media analysis (SocialBI) Open source software Streaming data analysis Text analytics Edge computing Internet of things (IoT) Reporting Dashboards End-user "self-service" Advanced visualization Data discovery Data warehousing Data mining, advanced algorithms, Integration with operational processes Data storytelling Pre-packaged vertical/functional Enterprise planning/budgeting Big Data (e.g., Hadoop) Location intelligence/analytics Mobile device support Embedded BI (contained within an Ability to write to transactional Governance In-memory analysis Collaborative support for group-based Software-as-a-service and cloud End-user data preparation and blending Search-based interface Successful Somewhat successful Somewhat unsuccessful Unsuccessful Figure 70 - Technologies and initiatives strategic to business intelligence by BI success 85

86 Success with Business Intelligence and Number of BI Tools Historically, we have found that organizations that are successful with business intelligence generally have fewer tools in use based on strategic intent and engaged leadership. Awareness of the number of tools in use also increases with the degree of BI success (fig. 71). It can be argued, however, that BI tools can proliferate or be restrained independent of BI success. 100% Number of Business Intelligence Tools in Use by BI Success 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Successful Somewhat successful Somewhat unsuccessful Unsuccessful One Two Three to six Seven or more Don't know Figure 71 Numbers of business intelligence tools in use by BI success 86

87 Success with Business Intelligence and the State of Data Success with business intelligence relates powerfully and directly to an organization s state of data (fig. 72). Organizations that view data as truth or with a common view of enterprise data are 80 percent likely to be successful, compared to 39 percent for somewhat unsuccessful and just 15 percent of unsuccessful organizations (none of which report complete success). 100% 90% 80% 70% 60% 50% 40% 30% Business Intelligence and the State of Data by BI Success Data as "truth" - A common view of enterprise data is available with common application of data, filters, rules, and semantics A common view of enterprise data is available. However, parochial views and semantics are used to support specific positions Consistent data is available at a departmental level. Conflicting, functional views of data causes confusion and disagreement 20% 10% 0% Successful Somewhat successful Somewhat unsuccessful Unsuccessful We have multiple, inconsistent data sources with conflicting semantics and data. Information is generally unreliable and distrusted Figure 72 Business intelligence and the state of data by BI success 87

88 Success with Business Intelligence and Action on Insight Success with business intelligence correlates perhaps even more strongly with an organization s ability to take action on insights (fig. 73). At the high end of performance, organizations with closed-loop processes are completely successful 47 percent of the time and at least somewhat successful 93 percent of the time. Organizations with ad hoc or informal action on insights are well less than half as likely to report complete success. Organizations with the two lowest levels of coordination are much more likely to fail than to succeed. No unsuccessful organizations in our 2016 sample say they are able to leverage closed-loop processes. 100% 90% 80% 70% 60% Business Intelligence and Action on Insight by BI Success "Closed loop" - Information is shared, teams work to process and act in a timely fashion. No formal boundaries Ad hoc (informal) action on insights across functions 50% 40% 30% 20% Uncoordinated/ parochial action (sometimes at the expense of others) Insights are rarely leveraged 10% 0% Successful Somewhat successful Somewhat unsuccessful Unsuccessful Figure 73 Business intelligence and action on insight by BI success 88

89 Success with Business Intelligence and Penetration of Users Organizations that are more successful with business intelligence have higher number of users as a percentage of the workforce (fig. 74). Fifty-eight percent of organizations with at least percent BI penetration say they are successful. As BI success decreases, so does penetration: among unsuccessful organizations, less than 20 percent have the same (21-40 percent) level of BI penetration. Seventy percent of unsuccessful organizations have less than 10 percent BI penetration. 100% Penetration of Business Intelligence Solutions Today by BI Success 90% 80% 70% 60% 50% 40% 30% 81% or more 61-80% 41-60% 21-40% 11-20% Under 10% 20% 10% 0% Successful Somewhat successful Somewhat unsuccessful Unsuccessful Figure 74 - Penetration of business intelligence solutions today by BI success 89

90 Industry and Vendor Analysis 90

91 Industry and Vendor Analysis In this section we review business intelligence vendor and market performance, using our trademark 33-criteria evaluation model. Scoring Criteria The criteria for the various industry and vendor rankings are grouped into seven categories including sales/acquisition experience, value for price paid, quality and usefulness of product, quality of technical support, quality and value of consulting, integrity, and whether the vendor is recommended. 91

92 Industry Performance Sales/Acquisition Experience Across the last two years of our study, industry professionalism and product knowledge have retained the highest and most consistent scores in our evaluation (fig. 75). During the same period, scores for responsiveness, flexibility/accommodation, business practices and follow-up after the sale have improved. From an industry perspective, it suggests that sales groups have stepped up efforts to respond to customers and support them beyond the close of the sale. 4.4 Industry Performance Sales and Acquisition Experience: 2011 to Professionalism Understanding our business/needs Responsiveness Flexibility/accommodation Business practices Contractual terms and conditions Follow up after the sale Figure 75 - Industry performance sales/acquisition experience: 2011 to

93 Value End users generally believe they are getting high and improving value from industry vendors over time (fig. 76). An ongoing rating higher than 4.0 extends a high and mildly positive trend line. Though gradients are in a narrow range, the 2016 score of 4.11 nonetheless represents an all-time high for industry value performance. With the exception of 2012, perceived value for price paid has remained above "very good." 4.15 Industry Performance Value: 2011 to Figure 76 - Industry performance value: 2011 to

94 Quality and Usefulness of Product Industry product quality and usefulness has improved over time across almost all dimensions (fig. 77). The most notable improvements are in areas reflecting ease of use: ease of upgrade, ease of installation, and ease of administration. These improvements correspond directly to areas where we have seen the industry invest to improve customer and end-user autonomy through simplification. Most other parameters, including the top priority of robustness, also held gains or improved. Industry Performance Quality and Usefulness of Products: 2011 to 2016 Customization and extensibility Online training, forums and documentation Ease of upgrade/migration to new versions Robustness/sophistication of technology Completeness of functionality Reliability of technology Scalability Ease of administration Integration of components within product Ease of installation Overall usability Integration with thirdparty technologies Figure 77 - Industry performance quality and usefulness of products: 2011 to

95 Technical Support In 2016, the vendor industry performed well among user respondents in the area of technical support (fig. 78). and responsiveness reached all-time high levels. Time to resolve problems rebounded strongly from a 2015 decline to near an all-time high. (We consider product knowledge, responsiveness, and time to resolve problems as cornerstones of immediacy in support of urgent customer needs). Continuity of personnel, often a challenge for vendors to sustain, held solid. In all, charts are encouraging signs of vendor success that bode well for the industry's future. 4.3 Industry Performance Technical Support: 2011 to Professionalism Responsiveness Continuity of personnel Time to resolve problems Figure 78 - Industry performance technical support: 2011 to

96 Consulting BI vendor consulting was also generally positive in 2016 (fig. 79). The only attribute that suffered a decline was experience, which (like continuity of personnel), is a traditional sore spot in user engagements. Project expansion and broad general demand often become a cost of success that leads to employee poaching and/or hiring fewer experienced or domain-familiar consulting personnel. Apart from this weakness, other parameters, noticeably value, held steady or improved. 4.3 Industry Performance BI Vendor Consulting: 2011 to Professionalism Experience Continuity Value Figure 79 - Industry performance BI vendor consulting: 2011 to

97 Integrity Vendor integrity measured as honesty and truthfulness in all dealings has remained mostly flat over the last three years with some slight positive trending (fig. 80). With mean scores well above 4.0, indicating "very good" to "excellent," this parameter of industry performance is secure as a core strength within the industry Industry Performance Integrity: 2012 to Figure 80 - Industry performance integrity: 2012 to

98 Recommended Industry performance in the area of customers willing to recommend rose slightly year over year, though the six-year trend line is down, thanks to a very strong 2011 performance (fig. 81). As the scores for recommend have rebounded for three straight years, they are also the highest mean scores of any in our survey, well above "very likely" and approaching near certainty. 5 Industry Performance Recommended: 2011 to Figure 81 - Industry performance recommended: 2011 to

99 Performance Improvements Another view of more recent year-over-year vendor performance shows a significant improvement in 2016 (fig. 82). Compared to 2016, perceived overall industry improvement jumped 6 percentage points from 39 percent to 45 percent. This suggests that vendors are paying greater attention to their full range of services and products (perhaps in conjunction with a better business climate and BI and information management success rates overall). In either or both cases, customers are upgrading their vendors' performance alongside their own abilities to manage data and leverage it. 100% 90% Overall Industry Performance Improvement: 2014 to % 5% 5% 80% 70% 60% 50% 40% 30% 20% 10% 56% 57% 39% 39% 50% 45% 0% Improved Stayed the same Declined Figure 82 - Overall industry performance improvement 2014 to

100 Vendor Ratings 100

101 Vendor Ratings In this section we offer ratings of business intelligence software vendors. We rated vendors using 33 different criteria, on a five-point scale for each. Criteria covered sales /acquisition experience (8 criteria), value for price paid (1), quality and usefulness of product (12), quality of technical support (5), quality and value of consulting services (5), whether the vendor is recommended (1), and integrity (1). As we explore vendor performance in more detail, it is important to understand the scale we used in scoring the industry and vendors: 5.0 = Excellent 4.0 = Very good 3.0 = Adequate 2.0 = Poor 1.0 = Very poor For 2016 we dispensed with market segmentation and now rely upon our Customer Experience and Vendor Credibility models, introduced last year, as a means of presenting relative vendor ratings. As a result, we no longer include a peer average for individual vendor rating charts. Instead, this has been replaced (where possible) with a year-over-year comparison for each vendor. Based on our scoring methodology, all vendors performed at a level that is considered more than adequate for all criteria categories. Please note that average score is the mathematical mean of all items included in vendor ratings. Each column in the chart represents a scale consisting of varying numbers of items (for example, "sales" is a scale consisting of eight items, while "value for price paid is one item). As such, each column is weighted differently (based upon the number of items represented and the number of respondents rating those items) in calculating the overall average rating. The average score cannot be calculated by simply averaging across the subscale scores. 101

102 Business Intelligence Market Models Starting in 2015, we developed two new models for examining and understanding the business intelligence market. Using quadrants, we plotted aggregated user sentiment into x and y axes. Customer Experience Model The customer experience model considers the real-world experience of customers working with BI products on a daily basis (fig. 110). For the x axis, we combined all vendor touch points including the sales and acquisition process (8 measures), technical support (5 measures), and consulting services (5 measures) into a single sales and service dimension. On the y axis, we plotted customer sentiment surrounding product, derived from the 12 product and technology measures used to rank vendors. On the resulting four quadrants we plotted vendors based on these measures. The upper-right quadrant contains the highest-scoring vendors and is named overall experience leaders. Technology leaders (upper-left quadrant) identifies vendors with strong product offerings but relatively lower services scores. Contenders (lower-left quadrant) would benefit from varying degrees of improvement to product, services, or both. User sentiment surrounding outliers (outside of the four quadrants) suggests that significant improvements are required to product and services. 102

103 Figure 83 - Customer experience model 103

104 Vendor Credibility Model The vendor credibility model considers how customers feel about their vendor (fig. 111). The x axis plots perceived value for the price paid. The y axis combines the integrity and recommend measures, creating a confidence dimension. The resulting four quadrants position vendors based on these dimensions. The upper-right quadrant contains the highest-scoring vendors and is named credibility leaders. Trust leaders (upper-left quadrant) identifies vendors with solid perceived confidence but relatively lower value scores. Contenders (lower-left quadrant) would benefit by working to improve customer value, confidence, or both. User sentiment surrounding outliers (outside of the four quadrants) suggests that significant improvements are required to improve perceived value and confidence. 104

105 Figure 84 - Vendor credibility model 105