Small and Mid-Sized Enterprise Business Intelligence Market Study

Transcription

1 September 30, 2015 Dresner Advisory Services, LLC 2015 Edition Small and Mid-Sized Enterprise Business Intelligence Market Study Wisdom of Crowds Series Licensed to Klipfolio

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 its content. Reproduction and distribution of this publication in any form without prior written permission is forbidden. 2

3 Business Intelligence: A Definition We define Business intelligence (BI) as 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 eighth anniversary of Dresner Advisory Services! Our thanks to all of you that have been with us along the way, encouraging and challenging us! Since our founding in 2007, we have strived to offer a fresh, real-world and alternative perspective on the Business Intelligence (BI) market. We hope that you agree that we not only have succeeded in doing so but also continue to raise the bar offering increasingly compelling research and greater value with each successive year! Since we published our first Wisdom of Crowds Business Intelligence Market study in 2010, we have continued to expand our research offerings to include a variety of important topics including: Location Intelligence, Advanced and Predictive Analytics, Cloud Computing and BI, Collaborative Computing and BI, Embedded BI, BI Emerging Technologies, and Small & Mid-Sized Enterprise BI. During 2015 we added to these topics with coverage for Enterprise Planning, End-User Data Preparation, Internet of Things (IoT), and Big Data Analytics. For this, our third SME market study report, we created a focused, detailed report examining business intelligence in small and mid-sized organizations. In particular, we consider how their deployments and views differ from each other and from larger organizations. Also included are two new models for examining and understanding the vendor landscape within the SME business intelligence market and a buyer s guide for 22 BI software vendors. In closing, we re very excited about both the market and our ability to continue to add substantial perspective and value to it! Thanks for your support! Best, Howard Dresner Chief Research Officer Dresner Advisory Services 4

5 Contents Business Intelligence: A Definition... 3 Introduction... 4 Benefits of the Study... 7 A Consumer Guide... 7 A Supplier Tool... 7 External Awareness... 7 Internal Planning... 7 About Howard Dresner and Dresner Advisory Services... 8 About Jim Ericson... 9 Survey Method and Data Collection Data Collection Data Quality Executive Summary Study Demographics Geography Functions Vertical Industries Analysis and Trends How SMEs Differ Technology Priorities Changing Departments/Functions Driving Business Intelligence Departmental Drivers User Roles Targeted for Business Intelligence Objectives for Business Intelligence Business Intelligence Objectives by Function Penetration of Business Intelligence SME Success with Business Intelligence State of Data Action on Insight

6 Introduced in 2014, Action on Insight is Dresner Advisory s high-level self-assessment of BI best (and worst) practices Business Intelligence Market Models for SMEs Customer Experience Model for SMEs Vendor Credibility Model for SMEs Business Intelligence Buyers Guide Cloud Platform Support Vendors (A D) Mobile Platform Support Vendors (A D) Traditional Platform Support Vendors (A D) Cloud Platform Support Vendors (I K) Mobile Platform Support Vendors (I K) Traditional Platform Support Vendors (I K) Cloud Platform Support Vendors (L M) Mobile Platform Support Vendors (L - M) Traditional Platform Support Vendors (L - M) Cloud Platform Support Vendors (O - Q) Mobile Platform Support Vendors (O - Q) Traditional Platform Support Vendors (O - Q) Cloud Platform Support Vendors (R - S) Mobile Platform Support Vendors (R - S) Traditional Platform Support Vendors (R - S) Cloud Platform Support Vendors (T - Y) Mobile Platform Support Vendors (T - Y) Traditional Platform Support Vendors (T - Y) Appendix - The 2015 Wisdom of Crowds Business Intelligence Market Survey Instrument Other Dresner Advisory Services Research Reports

7 Benefits of the Study The DAS Small and Mid-Sized Enterprise Business Intelligence Market Study provides a wealth of information and analysis offering value to both consumers and producers of Business Intelligence technology and services. A Consumer Guide As an objective source of industry research, consumers use the DAS Small and Mid- Sized Enterprise Business Intelligence Market Study to understand how their peers use and invest in Business Intelligence and related technologies. Using our trademarked 33-dimension 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 A Supplier Tool Vendor Licensees can use the DAS Small and Mid-Sized Enterprise Business Intelligence Market Study in several important ways, for example to: External Awareness - Build awareness for the Business Intelligence market and supplier brand, citing The DAS Small and Mid-Sized Enterprise Business Intelligence Market Study trends and vendor performance - Create lead and demand-generation for supplier offerings through association with The DAS Small and Mid-Sized Enterprise Business Intelligence Market Study brand, findings, webinars, etc. Internal Planning - Refine internal product plans and align with market priorities and realities as identified in The DAS Small and Mid-Sized Enterprise Business Intelligence Market Study - Better understand customer priorities, concerns, and issues - Identify competitive pressures and opportunities 7

8 About Howard Dresner and Dresner Advisory Services The DAS Small and Mid-Sized Enterprise 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: - Wisdom of Crowds Flagship Business Intelligence Market study - Advanced and Predictive Analytics - Cloud Computing and Business Intelligence - Collaborative Computing and Business Intelligence - End User Data Preparation - Internet of Things and Business 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 tribe discusses a wide range of business intelligence topics. You can find more information about Dresner Advisory Services at 8

9 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. 9

10 Survey Method and Data Collection For this SME study, we sampled different subsets of the 2015 Wisdom of Crowds Business Intelligence Market Survey. Dresner Advisory Services defines Small Enterprise as an organization with between one and 100 employees; Mid-Sized Enterprise an organization with between 101 and 1,000 employees; and Large Enterprise as an organization with more than 1,000 employees. We constructed the study from a survey instrument to collect data and used social media and crowdsourcing techniques to recruit participants. Data Collection A total of 778 surveys (versus 717 in 2014) were submitted by small and mid-sized (SME) organizations. This report focuses on the responses of those SME organizations and draws comparisons between their responses and those of the full sample. 700 SME Study Sample Small (1-100) Mid ( ) Large (1000+) Figure 1 SME study sample 10

11 Data Quality We carefully scrutinized and verified all respondent entries to ensure that the study includes only qualified participants. 11

12 Executive Summary 12

13 Executive Summary Much like larger organizations, small and medium-sized enterprises prioritize a wide span of BI technologies and initiatives (p. 19). SME technology priorities have remained remarkably consistent across three years of study with only minor changes in priority (p. 20). Executive management and sales are the strongest functional drivers at SMEs (slightly more so than at large enterprises). SME drivers have remained consistent across three years of study (pp ). Small and mid-sized enterprises target executives slightly more often than their large-enterprise peers, which are much more likely to target managers. SMEs are slowly moving to target more individual contributors (pp ). BI objectives, led by better decision making, are largely consistent across organizations of different sizes (p. 25). "Better decision-making," "growth in revenues," "increased competitive advantage," and "enhanced customer service" all gained as SME 2015 BI objectives (p. 26). SMEs in 2015 report much higher levels of business intelligence penetration than larger organizations (p.28). Small and mid-sized organizations plan modest development from current levels in the next 12 months that will accelerate in future timeframes (p. 29). SMEs report somewhat disappointing degrees of improved BI penetration in 2014 to 2015 (p. 30). Reports of "complete success" with BI are most likely in small organizations and decrease with organization size (p. 31). An organization's opinion of the state of data governance and consistency decreases as the size of the organization increases (p. 32). As might be expected, SMEs are somewhat more likely to claim an ability to execute with "closed loop processes for action on insight (p. 33). 13

14 Study Demographics The respondents studied in this SME survey provide a cross-section of data by geography, function, organization size, and vertical industries. We believe this supports a representative sample and indicator of true market dynamics. We constructed crosstab analyses using these demographics to identify and illustrate important industry trends. Geography Forty-nine percent of respondents are located in North America, which includes the United States, Canada, and Puerto Rico (fig. 2). EMEA organizations represent 28 percent of respondents. Asia Pacific (10 percent) and Latin America (6 percent) are the other regions represented. Geographies of SMEs Represented Latin America, 6% Asia Pacific, 10% Europe, Middle East, & Africa, 28% North America, 49% Figure 2 - Geographies of SMEs represented 14

15 Functions Information technology (23 percent) and executive management (20 percent) are the functions most represented in the study. Thirteen percent of respondents represent finance and 12 percent represent Business Intelligence Competency Centers (BICCs), which in the SME market can include dedicated BI resources as well as formal organizational departments (fig. 3). Our sample is somewhat more balanced than in 2014 when one-third of respondents were information workers in IT. This distribution across functions enables us to develop analyses comparing and contrasting the plans and priorities of the different departments within organizations. 25% 20% 23% Functions of SMEs Represented 20% 15% 10% 13% 12% 5% 5% 4% 4% 3% 3% 3% 2% 0% Figure 3 - Functions of SMEs represented 15

16 Vertical Industries Vertical industry distribution among SMEs is led by technology and consulting and includes a diverse cross-section of education, retail, and manufacturing organizations among other private and public institutions (fig. 4). 30.0% SME Vertical Industries Represented 25.0% 25% 20.0% 15.0% 17% 14% 10.0% 5.0% 5% 5% 4% 4% 3% 3% 2% 2% 2% 2% 2% 1% 1% 1% 1% 1% 1% 1% 1% 0.0% Figure 4 SME vertical industries represented 16

17 Analysis and Trends 17

18 Analysis and Trends This report describes the Small and Mid-Sized Enterprise market for Business Intelligence by its own characteristics, drivers, and trends, and also by how it compares to the large enterprise market. In 2015 we sampled SME experience with business intelligence including the uptake of technologies and future plans year over year. As in the larger Wisdom of Crowds study, we collected and analyzed data for SMEs surrounding functions driving business intelligence, goals/objectives for BI, targeted user roles, current penetration, and future plans for business intelligence deployment and organizational success. 18

19 How SMEs Differ Technology Priorities Changing Much like larger organizations, small and medium-sized enterprises prioritize a wide span of BI technologies and initiatives (fig. 5). Top priorities in common are dashboards, end-user self-service, advanced visualization, and integration with operational systems. SME interest in other BI technologies differs in part due to complexity, total cost of ownership, and time to value. Smaller organizations show considerably less interest in data warehousing than large peers. Small organizations are considerably more interested in software as a service / cloud computing and slightly more interested in mobile device support. Smaller organizations are somewhat less likely to embrace big data, data mining, and location intelligence but are slightly more interested in social BI. Technology Priorities: SMEs versus Large Enterprises Social media analysis (SocialBI) Text analytics Ability to write to transactional applications Cognitive BI (e.g., Artificial Complex event Intelligence-based processing BI) (CEP) Internet of things (IoT) Open source software Dashboards End user "self service" Advanced visualization Integration with operational processes Data warehousing Data discovery Enterprise planning/budgeting Mobile device support Big Data (e.g., Hadoop) Data mining, advanced algorithms, predictive Pre-packaged vertical/functional Search-based interface In-memory analysis Location intelligence/analytics SME Embedded BI (contained within an application, Software-as-a-service and cloud computing End user data "blending" Collaborative support (data mash for ups) group-based analysis LGE Figure 5 - Technology priorities: SMEs versus large enterprises 19

20 SME technology priorities remain remarkably consistent across three years of study with only minor changes in priority (fig. 6). We believe this reflects good market awareness and thoughtful, if cautious, planning in response to BI provider marketing and industry trends. Though differences are small, if anything, BI interest appears to have peaked in several categories and declined slightly in areas that include cloud/saas, end user self-service, and pre-packaged vertical apps. That said, average interest in most categories hovers near 3.5 or higher, placing them between important and very important. Technology Priority Changes 2013 to 2104: SME versus Overall Sample Ability to write to transactional applications Complex Event Processing (CEP) Open Source Software Social media Analysis (SocialBI) Text Analytics Dashboards End user "self service" Advanced visualization Integration with Operational Processes Data Warehousing Data Discovery Big Data (e.g., Hadoop) Mobile Device Support Pre-packaged vertical/functional Data Mining, Advanced Algorithms, Predictive Search-based interface In-memory analysis Collaborative Support for Group-based Analysis Location Intelligence/Analytics Embedded BI (contained within an application, End user data "blending" (data mash ups) Software-as-a-Service and "Cloud" Computing Figure 6 Technology priority changes 2013 to 2015: SME versus overall sample 20

21 Departments/Functions Driving Business Intelligence Our 2015 survey looks at the functions that drive business intelligence initiatives within the organization. For each function, we asked respondents to specify whether it drives business intelligence always, often, sometimes, rarely, or never. We used this to create a weighted average on a zero-to-five scale. Departmental Drivers Executive management and sales are the strongest functional drivers at SMEs and slightly more so than at large enterprises (fig. 7). Smaller organizations are also more likely to be driven by strategic planning, marketing, and R&D, which might imply a product or project emphasis (as opposed to an enterprise-wide emphasis on BI enablement). Interestingly, organizations of different sizes have similar propensity ( , between "often" and "sometimes") to see their BI efforts driven by IT. As dictated by their structure, SMEs are slightly less likely to be driven by finance or supply chain functions Functions Driving Business Intelligence by Organization Size Small Mid Large Figure 7 Functions driving business intelligence by organization size 21

22 Much like BI technology priorities, SME drivers of business intelligence remain consistent across three years of study (fig. 8). Executive management, always the leading driver, even gained a bit of ground, reminding us that SME leadership is the most likely advocate in the room. Sales returned to the mean after a slight uptick in In a minor watershed event, the strategic planning function and operations slightly surpassed IT as 2015 SME drivers. 4.5 SME Drivers of BI 2013 to Figure 8 - SME Drivers of BI 2013 to

23 User Roles Targeted for Business Intelligence Our survey asked which functions/roles are targeted for automation with business intelligence solutions. Respondents were able to designate these roles as either primary, secondary, or not applicable. Among all organizations sampled, the majority prioritized (in order) executives, middle managers, line managers, individuals, customers, and suppliers. Small and mid-sized enterprises target executives slightly more often than their largeenterprise peers (fig. 9). What stands out in this view is the higher (and growing) emphasis on managerial and individual contributor ranks at large enterprises. We expect the business structure of different sized enterprises with specific departmental autonomy, budgets, priorities, and scope dictates this finding. Not for the first time, small and mid-sized organizations are more likely to target customers than large organizations. 80% Primary Targeted Users for Business Intelligence by Organization Size 70% 60% 50% 40% 30% Small Mid Large 20% 10% 0% Executives Middle managers Line managers Individual contributors & professionals Customers Suppliers Figure 9 - Primary targeted users for business intelligence by organization size 23

24 Across three years of study we observe a slight shifting of BI targeting emphasis at SMEs (fig. 10). Executives get the most attention in 2015 as in previous years, but slightly less so than in 2013 and With fewer BI targets atop SME management structures, this could reflect saturation or a widening view of BI opportunities. In the same time frame there is a corresponding increase in targeting of individual contributors. 90% SME Targets for BI 2013 to % 70% 60% 50% 40% 30% % 10% 0% Executives Middle Managers Line Managers Individual Contributors & Professionals Customers Suppliers Figure 10 - SME targets for BI 2013 to

25 Objectives for Business Intelligence BI objectives as described in the survey are largely consistent across organizations of different sizes (fig. 11). Among all organizations of any size, better decision making is the most-cited objective. As organization size increases, respondents are less likely to cite revenue growth, competitive advantage, or customer service as BI objectives. Large organizations focus more on operational efficiency, which likely reflects the number and/or complexity of business processes. 5 Business Intelligence Objectives by Organization Size Better decision making Improved operational efficiency Growth in revenues Increased competitive advantage Enhanced customer service Small Mid Large Figure 11 - Business intelligence objectives by organization size 25

26 Among small and mid-sized organizations, "better decision making," "growth in revenues," "increased competitive advantage," and "enhanced customer service" all gained prominence as 2015 BI objectives (fig. 12). Revenue growth supplanted "improved operational efficiencies" as the second most-cited objective in SME BI Objectives 2014 to Better decision making Improved operational efficiency Growth in revenues Increased competitive advantage Enhanced customer service Figure 12 SME BI objectives 2014 to

27 Business Intelligence Objectives by Function Across SMEs (and also the entire sample of organizations) "better decision making" is the perennial top BI objective of organizations. This tells us that while organizations may face changing priorities, they have always been ready to leverage business intelligence whenever the opportunity arises. Across functions in 2015, this tendency is as pronounced as ever (fig. 13). Sales, with a focus upon profitable revenue growth, places an equally high priority upon growth in revenues and improved operational efficiency. Among trailing objectives, most functions lean toward operational efficiency, notably IT and finance, which are most likely to be budget and cost containment minded. Even executive management emphasizes operational efficiency over revenue growth. Predictably, marketing is likely to emphasize revenue and competitive advantage above operational efficiency. 5 Business Intelligence Objectives by Function: SMEs Only Marketing Executive management Information Technology (IT) Sales Finance Business Intelligence Competency Center Better decision making Growth in revenues Enhanced customer service Improved operational efficiency Increased competitive advantage Figure 13 BI objectives by function: SMEs Only 27

28 Penetration of Business Intelligence As we found in earlier studies, SMEs in 2015 report much higher levels of business intelligence penetration than larger organizations (fig. 14). Small enterprises (1-100 employees) are almost three times as likely as large organizations to report the highest (81 percent or more) BI penetration and are considerably less likely to report the lowest levels of penetration. Mid-sized organizations (100-1,000 employees) also consider their BI penetration to be more mature than large peers but are not as mature with BI as small organizations. 45% Current BI Penetration by Organization Size 40% 35% 30% 25% 20% Small Mid Large 15% 10% 5% 0% Under 10% 11-20% 21-40% 41-60% 61-80% 81% or more Figure 14 - Current BI penetration by organization size 28

29 Small and mid-sized organizations plan modest BI expansion from current levels in the next 12 months that will accelerate in future time frames (fig. 15). In small organizations, low-level penetration (< 10 percent) will hover near 12 percent going forward, while the highest level (81 percent or greater) will grow at about 5 percent in 24 and 36-month time frames. Mid-sized organizations expect gradual improvements that will move from the three lowest levels to higher BI penetration in consecutive 12, 24 and 36-month time frames. 100% Planned Business Intelligence Penetration through 2018 by Organization Size 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% In 12 months In 24 months In 36 months In 12 months In 24 months In 36 months In 12 months In 24 months Small Mid Large In 36 months Under 10% 11-20% 21-40% 41-60% 61-80% 81% or more Figure 15 Planned business intelligence penetration through 2018 by organization size 29

30 SMEs report only modest degrees of improved BI penetration 2014 to 2015 (fig. 16). The lowest level of penetration (<10 percent) actually ticked up slightly and now accounts for about one-third of respondents. Small improvements were seen in the 41 to 60 percent range (from 7 percent to 11 percent in 2015) with a corresponding decrease in 11 to 20 percent penetration. The highest level (>81 percent) gained slightly, from 18 to 19 percent. 35% SME BI Penetration 2014 to % 25% 20% 15% % 5% 0% Under 10% 11-20% 21-40% 41-60% 61-80% 81% or more Figure 16 - SME BI penetration 2014 to

31 SME Success with Business Intelligence The likelihood of reporting "complete success" with business intelligence programs and initiatives is most pronounced in small (1-100) organizations and decreases with organization size (fig. 17). Mid-sized (100-1,000) and larger organizations are more likely to have mixed views of BI success, more likely to both agree and disagree somewhat. Fewer than 3 percent of organizations of any size disagree completely that their BI initiatives have been successful. 70% Success with Business Intelligence by Organization Size 60% 50% 40% 30% 20% 10% 0% Completely agree Agree somewhat Disagree somewhat Disagree Small Mid Large Figure 17 - Success with business intelligence by organization size 31

32 State of Data An organization's opinion of the state of data governance and consistency decreases as the size of the organization increases (fig. 18). Well more than one-third of closer-knit, small (1-100) enterprises have the highest view of their governance being at the level of data as truth. More than 40 percent of small and mid-sized organizations claim "a common view of enterprise data," somewhat ahead of large organizations. Large organizations are more likely than SMEs to report department-level or multiple inconsistent data sources. 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 We have multiple, inconsistent data sources with conflicting semantics and data. Information is generally unreliable and distrusted 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Large Mid Small Figure 18 Business intelligence and the state of data by organization size 32

33 Action on Insight Introduced in 2014, Action on Insight is Dresner Advisory s high-level self-assessment of BI best (and worst) practices. As might be expected, SMEs are somewhat more likely to claim an ability to execute with closed loop processes (fig. 19). In the largest group, 61 percent of large, 63 percent of mid-sized and 56 percent of small organizations report ad hoc (informal) action on insights across functions. Just 4 percent or fewer of all organizations say they rarely leverage insights. Business Intelligence and Action on Insight by Organization Size 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 0% 10% 20% 30% 40% 50% 60% 70% Large Mid Small Figure 19 Business intelligence and action on insight by organization size 33

34 SME Vendor Rankings 34

35 Business Intelligence Market Models for SMEs For 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 for SMEs The customer experience model considers the real-world experience of customers working with BI products on a daily basis (fig. 20). 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. Service leaders (lower-right quadrant) provide strong customer service with relatively lower technology 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. 35

36 Figure 20 - Customer Experience Model for SMEs A Dimensional Insight B Birst C TIBCO D RapidMiner E Adaptive Insights F Information Builders G Pyramid Analytics H Dundas 36

37 Vendor Credibility Model for SMEs The vendor credibility model considers how customers feel about their vendor (fig. 21). 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. Value leaders (upper-left quadrant) identifies vendors with solid perceived value but relatively lower confidence 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. 37

38 Figure 21 - Vendor Credibility Model for SMEs A Information Builders B Dimensional Insight C Birst D Pyramid Analytics E Yellowfin F RapidMiner G TIBCO H Dundas I Tableau Software J Adaptive Insights 38

39 Business Intelligence Buyers Guide In this section, we present a Business Intelligence Buyers Guide organized by key platforms: traditional, cloud, and mobile. For each vendor, we share data collected for 22 different areas of current capability. An indicates a feature that was available in a vendor s product during Q Cloud Platform Support Vendors (A D) Capability Adaptive Insights Birst Dimensio nal Insight Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data storytelling Dundas 39

40 Mobile Platform Support Vendors (A D) Capability Adaptive Insights Birst Dimensio nal Insight Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data storytelling Dundas 40

41 Traditional Platform Support Vendors (A D) Capability Adaptive Insights Birst Dimensio nal Insight Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data storytelling Dundas 41

42 Cloud Platform Support Vendors (I K) Capability IBM Infor Informati on Builders Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" Klipfolio 42

43 Mobile Platform Support Vendors (I K) Capability IBM Infor Informati on Builders Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" Klipfolio 43

44 Traditional Platform Support Vendors (I K) Capability IBM Infor Informati on Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" Builders Klipfolio 44

45 Cloud Platform Support Vendors (L M) Capability Logi Microsoft Analytics Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" MicroStrat egy 45

46 Mobile Platform Support Vendors (L - M) Capability Logi Microsoft Analytics Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" MicroStrat egy 46

47 Traditional Platform Support Vendors (L - M) Capability Logi Analytics Microsoft MicroStrat egy Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data story telling" 47

48 Cloud Platform Support Vendors (O - Q) Capability Oracle Pentaho Pyramid Qlik Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 48

49 Mobile Platform Support Vendors (O - Q) Capability Oracle Pentaho Pyramid Qlik Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 49

50 Traditional Platform Support Vendors (O - Q) Capability Oracle Pentaho Pyramid Qlik Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 50

51 Cloud Platform Support Vendors (R - S) Capability RapidMiner SAP SAS SiSense Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 51

52 Mobile Platform Support Vendors (R - S) Capability RapidMiner SAP SAS SiSense Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 52

53 Traditional Platform Support Vendors (R - S) Capability RapidMiner SAP SAS SiSense Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 53

54 Cloud Platform Support Vendors (T - Y) Capability Tableau TIBCO Yellowfin Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 54

55 Mobile Platform Support Vendors (T - Y) Capability Tableau TIBCO Yellowfin Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 55

56 Traditional Platform Support Vendors (T - Y) Capability Tableau TIBCO Yellowfin Ability to write to transactional applications Ad-hoc query Advanced visualization Big data (e.g., Hadoop) support Collaborative support for group-based analysis Complex event processing (CEP) Custom CSS Data mining and advanced algorithms Data visualization End user "self service" In-memory support Interactive analysis Personalized dashboards Pre-packaged vertical/functional analytical applications Production reporting Social media analysis (Social BI) Text analytics Data integration/data quality tools/etl Embedded BI (contained within an application, portal, etc.) Search-based interface Location intelligence/analytics End user data "blending" or "mashups" Data "story telling" 56

57 Appendix - The 2015 Wisdom of Crowds Business Intelligence Market Survey Instrument 57

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69 Other Dresner Advisory Services Research Reports - Wisdom of Crowds Flagship Business Intelligence Market study - Advanced and Predictive Analytics - Business Intelligence Competency Center - Cloud Computing and Business Intelligence - Collaborative Computing and Business Intelligence - Embedded Business Intelligence - End User Data Preparation - Enterprise Planning - Internet of Things and Business Intelligence - Location Intelligence - Mobile Computing and Business Intelligence - Small and Mid-sized Enterprise Business Intelligence 69