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May 31, 2018
Dresner Advisory Services, LLC
Wisdom of Crowds® Business Intelligence Market Study
2018 Edition
Licensed to Sisense
2018 Wisdom of Crowds® Business Intelligence Market Study
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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.
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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).
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Introduction In 2018, we celebrate the 11th 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.
We are also excited that our second annual conference, Real Business Intelligence, will
be held June 27 and 28, 2018 on the MIT campus in Cambridge, Massachusetts. Unlike
other events, Real Business Intelligence was designed as an immersive thought
leadership event focused on strategies for success with information management,
business intelligence, analytics and performance management.
Our first market report in 2010 set the stage for where we are today. Since that time, we
expanded our agenda and added new research topics every year. For 2018, we plan to
release 16 major reports, including, this, our original BI flagship report—in its ninth year
of publication!
This latest installment of our flagship Business Intelligence Market Study continues to
evolve. This year, we further expanded our section on the Chief Analytics and Chief
Data Officer roles, added an “achievement” question and began tracking additional
technologies and initiatives including IT analytics, sales planning, and GDPR—bringing
the total to 36.
We hope you enjoy this report!
Best,
Howard Dresner Chief Research Officer Dresner Advisory Services
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Contents Business Intelligence: A Definition .................................................................................. 3
Introduction ..................................................................................................................... 4
Benefits of the Study ..................................................................................................... 12
Consumer Guide ........................................................................................................ 12
Supplier Tool .............................................................................................................. 12
External Awareness ................................................................................................ 12
Internal Planning ..................................................................................................... 12
About Howard Dresner and Dresner Advisory Services ................................................ 13
About Jim Ericson ......................................................................................................... 14
Survey Method and Data Collection .............................................................................. 15
Data Quality ............................................................................................................... 15
New for 2018 ................................................................................................................. 15
Executive Summary ...................................................................................................... 17
Study Demographics ..................................................................................................... 19
Geography ................................................................................................................. 19
Functions ................................................................................................................... 20
Vertical Industries ...................................................................................................... 21
Organization Size ....................................................................................................... 22
Analysis and Trends ...................................................................................................... 24
Departments/Functions Driving Business Intelligence ............................................... 24
Functions Driving Business Intelligence 2013-2018 ............................................... 25
Changes in Functions Driving Business Intelligence 2017-2018 ............................ 26
Functions Driving Business Intelligence by Major Geography ................................ 27
Functions Driving Business Intelligence by Vertical Industry .................................. 28
Functions Driving Business Intelligence by Organization Size ............................... 29
User Roles Targeted for Business Intelligence .......................................................... 30
Targeted Users for Business Intelligence 2013-2018 ............................................. 31
Targeted Users for Business Intelligence by Geography ........................................ 32
User Targets for Business Intelligence by Organization Size ................................. 33
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User Targets for Business Intelligence by Vertical Industries ................................. 34
Objectives for Business Intelligence .......................................................................... 35
Business Intelligence Objectives 2014-2018 .......................................................... 36
Percent Change in BI Objectives 2017-2018 .......................................................... 37
Business Intelligence Objectives by Geography ..................................................... 38
Business Intelligence Objectives by Function ......................................................... 39
Business Intelligence Objectives by Vertical Industry ............................................. 40
Business Intelligence Objectives by Organization Size .......................................... 41
Business Intelligence Achievements .......................................................................... 42
Business Intelligence Achievements by Organization Size ..................................... 43
Penetration of Business Intelligence Solutions .......................................................... 44
Change in Penetration of BI Solutions 2017-2018 .................................................. 45
Expansion Plans for Business Intelligence Through 2021 ...................................... 46
Current Business Intelligence Penetration by Geography ...................................... 47
Planned Business Intelligence Penetration by Geography ..................................... 48
Current Business Intelligence Penetration by Function .......................................... 49
Planned Business Intelligence Penetration by Function ......................................... 50
Current Business Intelligence Penetration by Vertical Industry .............................. 51
Planned Business Intelligence Penetration by Vertical Industry ............................. 52
Current Business Intelligence Penetration by Organization Size ............................ 53
Planned Business Intelligence Penetration by Organization Size ........................... 54
Chief Data and Chief Analytics Officers ..................................................................... 55
Enterprises with Chief Data or Chief Analytics Officers .......................................... 55
Plans to Implement Chief Data or Chief Analytics Officers ..................................... 56
Effectiveness of Chief Data or Chief Analytics Officers .......................................... 57
Business Intelligence Achievements by Presence of CDO ..................................... 58
Business Intelligence Achievements by Presence of CAO ..................................... 59
Enterprises with Chief Data or Chief Analytics Officers by Geography ................... 60
Enterprises with Chief Data or Chief Analytics Officers by Industry ........................ 61
Enterprises with Chief Data or Chief Analytics Officers by Organization Size ........ 62
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Enterprises with Chief Data and Chief Analytics Officers Reporting Structure ....... 63
Chief Data and Chief Analytics Officer and Success with BI by Reporting Structure
................................................................................................................................ 64
Number of Business Intelligence Tools in Use ........................................................... 65
Number of Business Intelligence Tools in Use 2013 to 2018 .................................. 65
Number of Business Intelligence Tools by Geography ........................................... 66
Number of Business Intelligence Tools by Function ............................................... 67
Number of Business Intelligence Tools by Vertical Industry ................................... 68
Number of Business Intelligence Tools by Organization Size ................................. 69
Technologies and Initiatives Strategic to Business Intelligence ................................. 70
Technology Priorities 2015-2018 ............................................................................ 71
Technology Priority Changes 2017-2018 ................................................................ 72
Technologies and Initiatives Strategic to Business Intelligence by Geography ...... 73
Technologies and Initiatives Strategic to Business Intelligence by Function .......... 74
Technologies and Initiatives Strategic to Business Intelligence by Vertical Industry
................................................................................................................................ 75
Technologies and Initiatives Strategic to Business Intelligence by Organization Size
................................................................................................................................ 76
Business Intelligence and the State of Data .............................................................. 77
Business Intelligence and the State of Data 2015-2018 ............................................ 78
Business Intelligence and the State of Data by Geography .................................... 79
Business Intelligence and the State of Data by Function ........................................ 80
Business Intelligence and the State of Data by Industry ......................................... 81
Business Intelligence and the State of Data by Organization Size ......................... 82
Business Intelligence and Action on Insight ............................................................... 83
Business Intelligence and Action on Insight by Geography .................................... 84
Business Intelligence and Action on Insight by Function ........................................ 85
Business Intelligence and Action on Insight by Vertical Industry ............................ 86
Business Intelligence and Action on Insight by Organization Size .......................... 87
Success with Business Intelligence ........................................................................... 88
Change in Success with Business Intelligence 2017-2018 ..................................... 89
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How Successful Organizations Measure Success with Business Intelligence ........ 90
How Unsuccessful Organizations Measure Failure with Business Intelligence ...... 91
Success with Business Intelligence by Organization Size ...................................... 92
Success with Business Intelligence by BI Objectives ............................................. 93
Success with Business Intelligence by Targeted Users .......................................... 94
Success with Business Intelligence and Technology Priorities ............................... 95
Success with Business Intelligence and Technology Priorities ............................... 96
Success with Business Intelligence and Number of BI Tools ................................. 97
Success with Business Intelligence and the State of Data ..................................... 98
Success with Business Intelligence and Action on Insight ...................................... 99
Success with Business Intelligence and Penetration of Users .............................. 100
Business Intelligence Achievements by Success with BI ...................................... 101
Technologies and Initiatives Strategic to Business Intelligence by BI Success .... 102
Budget Plans for Business Intelligence .................................................................... 103
Budget Plans for Business Intelligence 2017-2018 ............................................... 104
Budget Plans for Business Intelligence by Geography ......................................... 105
Budget Plans for Business Intelligence by Function ............................................. 106
Budget Plans for Business Intelligence by Vertical Industry ................................. 107
Budget Plans for Business Intelligence by Organization Size ............................... 108
Budget Plans for Business Intelligence by Penetration of BI Solutions ................. 109
Expansion Plans for Business Intelligence through 2021 by BI Budget Plans ...... 110
Budget Plans for Business Intelligence by Success with BI .................................. 111
Business Intelligence Achievements by BI Budget Plans ..................................... 112
Technologies and Initiatives Strategic to Business Intelligence by BI Budget Plans
.............................................................................................................................. 113
Business Intelligence Product Replacements .......................................................... 114
Current Business Intelligence Products Replaced by Another .............................. 114
Reasons BI Products Are Replaced ..................................................................... 115
Industry and Vendor Analysis ...................................................................................... 117
Scoring Criteria ........................................................................................................ 117
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Industry Performance ............................................................................................... 118
Sales/Acquisition Experience................................................................................ 118
Value .................................................................................................................... 119
Quality and Usefulness of Product ........................................................................ 120
Technical Support ................................................................................................. 121
Consulting ............................................................................................................. 122
Integrity ................................................................................................................. 123
Recommended ..................................................................................................... 124
Performance Improvements ................................................................................. 125
Vendor Ratings ........................................................................................................... 127
Business Intelligence Market Models .......................................................................... 128
Customer Experience Model .................................................................................... 128
Vendor Credibility Model .......................................................................................... 130
Detailed Vendor Ratings ............................................................................................. 132
Board Detailed Score ............................................................................................ 133
ClearStory Data Detailed Score ............................................................................ 134
Dimensional Insight Detailed Score ...................................................................... 135
Domo Detailed Score ............................................................................................ 136
Google Detailed Score .......................................................................................... 137
IBM Detailed Score ............................................................................................... 138
Infor Detailed Score .............................................................................................. 139
Information Builders Detailed Score ..................................................................... 140
Jedox Detailed Score ............................................................................................ 141
Klipfolio Detailed Score ......................................................................................... 142
Logi Analytics Detailed Score ............................................................................... 143
Looker Detailed Score .......................................................................................... 144
Microsoft Detailed Score ....................................................................................... 145
MicroStrategy Detailed Score ............................................................................... 146
OpenText Detailed Score ..................................................................................... 147
Oracle Detailed Score ........................................................................................... 148
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Pyramid Analytics Detailed Score ......................................................................... 149
Qlik Detailed Score ............................................................................................... 150
RapidMiner Detailed Score ................................................................................... 151
Salesforce Detailed Score .................................................................................... 152
SAP Detailed Score .............................................................................................. 153
SAS Detailed Score .............................................................................................. 154
Sisense Detailed Score ........................................................................................ 155
Tableau Software Detailed Score ......................................................................... 156
TIBCO Software Detailed Score ........................................................................... 157
Yellowfin Detailed Score ....................................................................................... 158
Zoomdata Detailed Score ..................................................................................... 159
Other Dresner Advisory Services Research Reports .................................................. 160
Dresner Advisory Services - 2018 Wisdom of Crowds Survey Instrument .................. 161
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2018 Wisdom of Crowds® Business Intelligence Market Study
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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
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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 1989. He
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 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 Study reports, we
engage with a global community to redefine how research is created and shared. Other
research reports include:
- Advanced and Predictive Analytics
- Analytical Data Infrastructure
- Business Intelligence Competency Center
- Cloud Computing and Business Intelligence
- Collective Insights®
- Embedded Business Intelligence
- End User Data Preparation
- IoT Intelligence®
- Location Intelligence
Howard (www.twitter.com/howarddresner) 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
www.dresneradvisory.com.
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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.
As lead writer he interviewed and profiled more than 100 CIOs,
CTOs, and program directors in a 2010-2012 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
writing.
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.
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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 crowd-
sourcing techniques to recruit participants.
We also include our own research community of over 5,000 organizations as well as
vendors’ customer communities.
Data Quality
We carefully scrutinized and verified all respondent entries to ensure that the study
includes only qualified participants.
New for 2018 For 2018, we again expanded our research objectives substantially. This year’s study
adds:
• Three additional technologies and initiatives strategic to business intelligence (IT
analytics, sales planning, and GDPR), extending our study to 36 areas
• Expanded questions surrounding chief data officers (CDOs) and chief analytics
officers (CAOs)
• New question on business intelligence “achievements”
• New questions related to BI product/tool replacements and rationale
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Executive
Summary
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Executive Summary User Analysis:
- Executive Management, Operations, and Sales are the primary roles driving BI in
2018 (pp. 24-29).
- Executives and middle managers are the most likely targeted users of business
intelligence. Customer targeting increased in 2018 (pp. 30-34).
- "Better decision-making" remains the top BI objective; but efficiency, revenue,
and competitive objectives gained momentum (pp. 35-41). BI objectives are not
always reflected in BI achievements (pp. 42-43).
- Penetration of business intelligence grows year over year and continues to grow
over time. Expansion plans are bullish, particularly in small organizations (pp. 44-
54).
- The ongoing uptake and longevity of chief data and chief analytics officers is
quite modest and growing only slowly over time. Organizations with either title
are more effective at achieving goals. CAOs are viewed as somewhat more
effective in their roles and are more likely to report directly to the CEO (pp. 55-
64).
- The number of BI tools in use has grown slightly over time but remains stable
overall (pp. 65-69).
- Familiar technologies—dashboards, reporting, end-user self-service, advanced
visualization, and data warehousing—remain most strategic to BI users. Several
newer technologies grew strongly in 2018 (pp. 70-76).
- Users’ confidence in their "state of data" grew over time, particularly in small
organizations (pp. 77-82).
- Organizations large and small in most industries are confident of their ability to
take action on BI insights (pp. 83-87).
- Our core measure of "success with BI" declined somewhat in the last three years
(pp. 88-89).
- Organizations that are successful with BI tend to measure results on user
feedback. Unsuccessful BI organizations more often measure adoption and
usage rates. Small organizations are usually more successful with BI.
Organizations with an effective CDO or CAO report more success (pp. 90-102).
- More than half of organizations will increase BI spending, and only 6 percent will
decrease BI spending. Highly penetrated, high achieving, and successful
organizations are more likely to increase BI investment further (pp. 103-113).
- Three-quarters of respondents say they did not replace a BI product with another
product. New products are more often purchased to serve a different audience or
function (pp. 114-115).
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Industry Analysis:
- We observe mostly small declines in measures of industry sales and acquisition
performance (p. 118).
- End users continue to report that they receive high value from industry vendors,
and value performance reached an all-time high in 2018 (p. 119).
- Many measures of industry quality and usefulness touched all-time highs
including “completeness of functionality, “customization/extensibility,” and “ease
of administration” (p. 120).
- All measures of industry technical support declined modestly, and “continuity of
personnel” declined most of all (p. 121).
- BI vendor consulting scores all dropped noticeably between 2017 and 2018 (p.
122).
- Vendor integrity—measured as honesty and truthfulness in all dealings—
continued its slow long-term upward climb to an all-time high in 2018 (p. 123).
- The number of customers willing to recommend a provider grew to another
consecutive all-time high and delivered the highest score in our survey (p. 124).
- A recent peak in overall vendor performance may signal maturity and diminishing
urgency for upgrades (p. 125).
- Perpetual on-premises licensing is offered by 50 percent of vendors in 2018, a
notable decline over 2017. Public cloud perpetual licensing dipped more
modestly (p. 126).
- Concurrent use licensing continues to decline in favor with vendors, reaching an
all-time low (p. 127).
- The number of vendors offering subscription licensing continues to grow for both
on-premises and public cloud models (p. 128).
- Named user and concurrent use licensing models become less available over
time (p. 129).
- Among vendors that still offer perpetual licenses, maintenance fees are now as
likely to be based on discounted price as on list price (p. 130).
- (VENDOR RANKINGS…)
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Study Demographics Our 2018 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
Seventy-one percent of respondents work at North America-based organizations
(including the United States, Canada, and Puerto Rico). EMEA accounts for about 20
percent of respondents; the remainder are distributed across Asia Pacific and Latin
America (fig. 1).
Figure 1 – Geographies represented
70.5%
20.3%
5.4%3.7%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
North America Europe, Middle East andAfrica
Asia Pacific Latin America
Geographies Represented
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Functions
Our 2018 sample base includes a mix of functions (fig. 2). IT accounts for the largest
group (28 percent), followed by Executive Management (22 percent), and Finance (19
percent). Sales/Marketing (8 percent) and the BICC (7 percent) are the next most
represented functions.
Tabulating results across functions helps us develop analyses that reflect the
differences and influence of different departments within organizations.
Figure 2 – Functions represented
28.3%
21.7%
18.5%
7.7% 7.3%
4.4% 4.3%
1.5%
6.06%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
Functions Represented
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Vertical Industries
In 2018, technology organizations lead vertical industry distribution (16 percent). Business Services and Healthcare each represent 9 percent of our sample (fig. 3). Manufacturing, Consulting, and Retail/Wholesale are the next most represented. Tabulating results across industries helps us develop analyses that reflect the maturity and direction of different business sectors.
Figure 3 – Vertical industries represented
16%
9% 9%
7%
6% 6%
4% 4%3%
3% 3% 2% 2% 2% 2% 2%
4%
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
Vertical Industries Represented
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Organization Size
Participation in our sample base is balanced across organizations of different sizes in
2018 (based on global headcount). Small organizations (1-100 employees) represent 25
percent of respondents, mid-size organizations (101-1,000 employees) represent 31
percent, and large organizations (>1,000 employees) account for the remaining 43
percent (fig. 4).
Tabulating results by organization size reveals important differences in practices, planning, and maturity.
Figure 4 – Organization sizes represented
25.3%
31.1%
10.0% 10.3%
6.3%
17.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
1-100 101-1,000 1,001-2,000 2,001-5,000 5,001-10,000 More than10,000
Organization Sizes Represented
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Analysis and
Trends
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Analysis and Trends
Departments/Functions Driving Business Intelligence
We asked respondents which functional roles drive business intelligence “always,”
“often,” “sometimes,” “rarely,” or “never” (fig. 5). Our results show a breadth of influence,
and in 2018, survey respondents say Executive Management is the leading BI driver.
Operations (which was the leading driver in 2016 and 2017), closely follows as the
second most likely driver of BI, followed by Sales (which moved ahead of Finance) as
the third most likely driver in 2018. Other departments that, at minimum, "often" drive BI
at least 40 percent of the time include Marketing, IT, and Strategic Planning. While
functional influence may roll up to a centralized program or strategy, we observe that BI
tactics and influence are widely distributed in organizations.
Figure 5 – Functions driving business intelligence
0% 20% 40% 60% 80% 100%
Manufacturing
Human Resources
Research and Development (R&D)
Competency Center/Center of Excellence
Strategic Planning Function
Information Technology (IT)
Marketing
Finance
Sales
Operations
Executive Management
Functions Driving Business Intelligence
Always Often Sometimes Rarely Never
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Functions Driving Business Intelligence 2013-2018
As noted in fig. 5, Executive Management supplants Operations as the leading driver of
business intelligence, though the two functions narrowly shifted order over time (fig. 6).
Also, Sales gains influence as a BI driver in 2018, as do Marketing, the BICC, and HR.
This second tier of role influence likely reflects more front-end control of BI strategy
execution and shorter cycles of decision support. R&D and Manufacturing also gain
influence in small degrees while other functions are flat or lower.
Figure 6 – Functions driving business intelligence 2013-2018
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Functions Driving Business Intelligence 2013-2018
2013 2014 2015 2016 2017 2018
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Changes in Functions Driving Business Intelligence 2017-2018
Fig. 7 depicts another instructive view of year-over-year office and departmental
influence. Most notable in 2018 is a greater than 7 percent increase in influence among
Human Resources respondents (also the leading gainer in 2017). Marketing, the BICC,
and Sales gains also portend an increasing expectation of front-end BI execution.
Despite the attention shown to technology and process automation in 2018, BI influence
slips among IT and Strategic Planning respondents, albeit only slightly.
Figure 7 – Change in functions driving BI 2017-2018
-2.2%
-1.3%
0.0%
0.5%
1.1%
1.1%
2.7%
5.0%
5.1%
5.9%
7.3%
-4.0% -2.0% 0.0% 2.0% 4.0% 6.0% 8.0%
Information Technology (IT)
Strategic Planning Function
Finance
Operations
Manufacturing
Research and Development (R&D)
Executive Management
Sales
Competency Center/Center of Excellence
Marketing
Human Resources
Change in Functions Driving BI 2017-2018
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Functions Driving Business Intelligence by Major Geography
Functional influence of business intelligence varies interestingly by geography (fig. 8).
We note that Executive Management and Operations lead influence over time; but
notably, Sales emerges as the leading driver among Latin American and EMEA
organizations in our 2018 sample. Latin America and Asia Pacific report the greatest
executive influence, while Latin and North America report the most operations influence.
Finance is the next most likely to drive BI decisions across all geographies. Marketing
influence is strongest among Asia-Pacific respondents.
Figure 8 – Functions driving business intelligence by geography
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Asia Pacific Latin America North America Europe, Middle East andAfrica
Functions Driving Business Intelligence by Geography
Executive Management Operations
Sales Finance
Marketing Information Technology (IT)
Strategic Planning Function Competency Center/Center of Excellence
Research and Development (R&D) Human Resources
Manufacturing
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Functions Driving Business Intelligence by Vertical Industry
The importance of various functional drivers of BI is somewhat predictable across
industries (fig. 9). In 2018, Executive Management leadership is greatest in Insurance
and Healthcare. Likewise, Operations influence is most prominent in Insurance,
Healthcare, and Retail/Wholesale organizations. Insurance, Retail/Wholesale and
Business Services often lead BI through sales. Education respondents are most likely to
drive BI through HR while Retail/Wholesale respondents are least likely to drive BI at
the human resource level.
Figure 9 – Functions driving business intelligence by industry
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5Executive Management
Operations
Sales
Finance
Marketing
Information Technology (IT)
Strategic Planning Function
Competency Center/Center ofExcellence
Research and Development(R&D)
Human Resources
Manufacturing
Grand Total
Functions Driving Business Intelligence by Industry
Insurance Manufacturing Financial Services Technology
Healthcare Education (Higher Ed) Retail and Wholesale Business Services
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Functions Driving Business Intelligence by Organization Size
Executive Management and Operations are the most likely drivers of business
intelligence in all enterprises with the exception of small organizations (1-100
employees) where Sales has the most influence (fig. 10). The net influence of Sales
generally decreases as organization size increases, while the influence of IT grows with
organization size. Executive and Operational influence is greatest in very large
organizations (> 10,000 employees), followed by mid-sized organizations (101-1,000
employees). The influence of Finance is also greatest at very large and mid-sized
organizations.
Figure 10 – Functions driving business intelligence by organization size
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
1-100 101-1,000 1,001-10,000 More than 10,000
Functions Driving Business Intelligence by Organization Size
Executive Management Operations
Sales Finance
Marketing Information Technology (IT)
Strategic Planning Function Competency Center/Center of Excellence
Research and Development (R&D) Human Resources
Manufacturing
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User Roles Targeted for Business Intelligence
In 2018 (as in our two most recent studies), executives and middle managers are the
most likely targeted users of business intelligence (fig. 11). By a wide margin,
executives are primary targets 65 percent of the time and not targeted only about 7
percent of the time. Like middle managers, individual contributors and professionals are
primary targets of BI almost 40 percent of the time and only somewhat less targeted as
secondary users compared to middle managers. Suppliers and partners are least often
targeted.
Figure 11 – Targeted users for business intelligence
0% 20% 40% 60% 80% 100%
Suppliers
Partners / Affliates
Customers
Line Managers
Individual Contributors andProfessionals
Middle Managers
Executives
Targeted Users for Business Intelligence
Primary Secondary Future plans No plans
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Targeted Users for Business Intelligence 2013-2018
In 2018, we observe a significant increase in the targeting of customers, individual
contributors, and to a lesser extent, line managers (fig. 12). At the same time,
designated targeting of middle managers decreases significantly year over year,
followed by a slight decline in executives. In our opinion, this demonstrates an
increasing democratization of business intelligence as it increasingly flows to historically
less well-served constituents.
Figure 12 – Targeted users for business intelligence 2013-2018
0%
10%
20%
30%
40%
50%
60%
70%
80%
Executives MiddleManagers
IndividualContributors and
Professionals
Line Managers Customers Suppliers
Targeted Users for Business Intelligence 2013-2018
2013 2014 2015 2016 2017 2018
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Targeted Users for Business Intelligence by Geography
Executives are the most likely targets for business intelligence across all geographies,
though Asia Pacific respondents place the highest secondary emphasis on middle and
line managers (fig. 13). Asia Pacific and Latin America account for a part of the surge in
customer BI enablement noted already. Unlike other geographies, North American
respondents are most likely to target individual contributors and professionals equally or
more than middle or line managers.
Figure 13 – Targeted users for business intelligence by geography
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Asia Pacific North America Latin America Europe, Middle East andAfrica
Targeted Users for Business Intelligenceby Geography
Executives Middle Managers
Individual Contributors and Professionals Line Managers
Customers Partners / Affliates
Suppliers
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User Targets for Business Intelligence by Organization Size
Small organizations (1-100 employees), which tend to be flatter organizationally, most
often choose executives over other roles as targets for BI enablement (fig. 14). Small
organizations are also most likely to target customers. As we would expect,
organizations with more than 1,000 employees are more likely than others to target
middle and line managers, as well as individual contributors and professionals. Very
large organizations (> 10,000 employees) are most likely to target suppliers with BI
enablement.
Figure 14 – Targeted business intelligence users by organization size
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
1-100 101-1,000 1,001-10,000 More than 10,000
Targeted Users by Organization Size
Executives Middle Managers
Individual Contributors and Professionals Line Managers
Customers Partners / Affliates
Suppliers
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User Targets for Business Intelligence by Vertical Industries
In our 2018 sample, respondents in the Insurance industry are most likely to highly
target multiple constituencies of users with BI enablement, and this leading sentiment
extends to customers and lesser tiers of users as well (fig. 15). Business Services and
Technology are the industries next most likely to target customers. After Insurance,
Healthcare organizations are most active in middle-tier enablement of managers and
individual contributors. Like the aforementioned, Manufacturing, and Education
respondents are also likely to target individual contributors and professionals. Executive
targeting nonetheless leads across all industries.
Figure 15 – Targeted business intelligence users by industry
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Targeted Users by Industry
Executives Individual Contributors and ProfessionalsMiddle Managers Line ManagersCustomers Partners / AffliatesSuppliers
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Objectives for Business Intelligence
In 2018, the non-specific goal of “making better decisions” remains atop our list of
business intelligence objectives (fig. 16). (We traditionally associate this goal with
organizations seeking general improvements wherever they may be found through the
use of business intelligence.) At the same time, a second tier of more quantifiable
objective emerged in "improved operational efficiency," "growth in revenues," and
"increased competitive advantage." While the overall finding is not industry specific, it is
worthy to note that "compliance/risk management" is the least likely "critical" driver of
business intelligence.
Figure 16 – Business intelligence objectives
0% 20% 40% 60% 80% 100%
Compliance / Risk Management
Enhanced Customer Service
Increased Competitive Advantage
Growth in Revenues
Improved Operational Efficiency / Cost Savings
Better Decision-Making
Business Intelligence Objectives
Critical Very important Important Somewhat important Unimportant
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Business Intelligence Objectives 2014-2018
As we noted, more notable growth in BI objectives comes in formerly secondary
concerns about customer service, revenue growth, competitive advantage and other
more quantifiable business imperatives (fig. 17). This may result from both the newer
directive and the ability to identify ROI in all investments including BI, and not just the
"soft" benefits of technology evolution. In demonstration of the importance of business
intelligence, the importance of all specified objectives increases year over year in sum
and in detail. (We added compliance/risk management in 2018).
Figure 17 – Business intelligence objectives 2014-2018
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
Better Decision-Making
ImprovedOperational
Efficiency
Growth inRevenues
IncreasedCompetitiveAdvantage
EnhancedCustomer
Service
Compliance /Risk
Management
Business Intelligence Objectives 2014-2018
2014 2015 2016 2017 2018
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Percent Change in BI Objectives 2017-2018
Fig. 18 shows another helpful view of year-over-year attitudes toward BI objectives.
Here we observe clearly that the BI objective of "enhanced customer service" gains the
most importance, followed by "growth in revenues" and "increased competitive
advantage." "Improved operational efficiency" (a topic that gained momentum in earlier
studies), grows at a slower rate, while "better decision-making" continues its upward
trajectory, but at the slowest rate of all BI objectives.
Figure 18 – Percent change in BI objectives 2017-2018
1.2%
2.8%
4.5%
5.4%
8.2%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0%
Better Decision-Making
Improved Operational Efficiency /Cost Savings
Increased Competitive Advantage
Growth in Revenues
Enhanced Customer Service
Percent Change in BI Objectives 2017-2018
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Business Intelligence Objectives by Geography
“Better decision-making” is the most important BI objective across all geographical
regions in 2018, though by lower margins than we observed in earlier studies (fig. 19).
Overall results are somewhat similar across geographies. Excluding non-specific "better
decision-making," revenue growth and competitive advantage are most important to
Latin American respondents, while Asia-Pacific and North American respondents
posted slightly higher scores for better operational efficiency. Asia-Pacific respondents
also gave the highest degree of importance to enhanced customer service.
Figure 19 – Business intelligence objectives by geography
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Asia Pacific North America Latin America Europe, Middle East andAfrica
Business Intelligence Objectives by Geography
Better Decision-Making Improved Operational Efficiency / Cost Savings
Growth in Revenues Increased Competitive Advantage
Enhanced Customer Service Compliance / Risk Management
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Business Intelligence Objectives by Function
In 2018, the importance of "better decision-making" is highest across all functions with
the exception of R&D, where "increased competitive advantage" earns the top priority
(fig. 20). The BICC and Operations are most likely to focus on issues of revenue growth
and customer service. As we might expect, "improved operational efficiency" resonates
strongly with Finance, IT, and Executive Management. Compliance/Risk Management is
the least important objective across all roles.
Figure 20 - Business intelligence objectives by function
00.5
11.5
22.5
33.5
44.5
5
Business IntelligenceCompetency Center
Executive Management
Finance
Information Technology(IT)
Operations (e.g.,Manufacturing, Supply
Chain, Services)
Research and Development(R&D)
Sales & Marketing
Business Intelligence Objectives by Function
Better Decision-Making Improved Operational Efficiency / Cost Savings
Growth in Revenues Increased Competitive Advantage
Enhanced Customer Service Compliance / Risk Management
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Business Intelligence Objectives by Vertical Industry
By industry, Insurance respondents give the highest mean scores for all BI objectives
(with emphasis on customer service) (fig. 21). Financial Services organizations report
the most tightly clustered importance and awarded high importance to “enhanced
customer service” and "compliance/risk management." Interestingly, Healthcare
respondents give above-mean scores to compliance, but are more interested in all other
objectives, led by "better decision-making." Business Services give the highest overall
score to "increased competitive advantage."
Figure 21 – Business intelligence objectives by industry
00.5
11.5
22.5
33.5
44.5
5Insurance
Financial Services
Retail and Wholesale
Technology
Healthcare
Business Services
Manufacturing
Education (Higher Ed)
Business Intelligence Objectives by Industry
Better Decision-Making Growth in Revenues
Improved Operational Efficiency / Cost Savings Increased Competitive Advantage
Enhanced Customer Service Compliance / Risk Management
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Business Intelligence Objectives by Organization Size
Organizations of different sizes all place the highest emphasis on "better decision-
making" with strong mean importance above "very important" (fig. 22). Improved
operational efficiency is the next most important objective at mid-sized organizations
(101-1,000 employees) and larger organizations. 2018 focus on "compliance/risk
management" importance tends to increase with organization size.
Figure 22 – Business intelligence objectives by organization size
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1-100 101-1,000 1,001-10,000 More than 10,000
Business Intelligence Objectives by Organization Size
Better Decision-Making Improved Operational Efficiency / Cost Savings
Growth in Revenues Increased Competitive Advantage
Enhanced Customer Service Compliance / Risk Management
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Business Intelligence Achievements
New for 2018, we asked respondents to gauge their level of "BI achievements" as a way
of augmenting our longstanding and ongoing polling on "BI objectives" (fig. 23). By this
measure, we find some minor distinctions between intent and ongoing accomplishment.
In 2018, for example, the top two achievements ("better decision-making," "improved
operational efficiency") match the top BI objectives (fig. 17, p. 36), but "enhanced
customer service" is the third most cited achievement and perhaps easier to achieve
than the third most cited objective of "growth in revenues" (ibid). Over time, we expect
this polling will help identify some distinctions between specific organizational goals and
the difficulty of modeling and managing different processes successfully.
Figure 23 – Business intelligence achievements
0% 20% 40% 60% 80% 100%
Compliance /Risk
Management
IncreasedCompetitiveAdvantage
Growth inRevenues
EnhancedCustomer
Service
ImprovedOperationalEfficiency /…
Better Decision-Making
Business Intelligence Achievements
High Achievement Moderate Achievement Acceptable Achievement
Not Yet Attempted Not Yet Achieved
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Business Intelligence Achievements by Organization Size
Measured by organization size, respondents at all organizations find "better decision-
making" their most successful organizational BI achievement (fig. 24). Perhaps more
interesting, we observe that small organizations (1-100 employees) and, to a lesser
degree, mid-sized (101-1,000) organizations are more successful than larger peers at
achieving revenue growth and increased competitive advantage through BI. Both small
and very large organizations (>10,000 employees) are similarly successful at improving
operational efficiency. Finally, compliance and risk management achievement (success)
requirements are more often achieved as organization size increases.
Figure 24 – Business intelligence achievements by organization size
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1-1
00
10
1-1
,00
0
1,0
01
-10
,00
0
Mo
re t
han
10
,00
0
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00
10
1-1
,00
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1,0
01
-10
,00
0
Mo
re t
han
10
,00
0
1-1
00
10
1-1
,00
0
1,0
01
-10
,00
0
Mo
re t
han
10
,00
0
1-1
00
10
1-1
,00
0
1,0
01
-10
,00
0
Mo
re t
han
10
,00
0
1-1
00
10
1-1
,00
0
1,0
01
-10
,00
0
Mo
re t
han
10
,00
0
1-1
00
10
1-1
,00
0
1,0
01
-10
,00
0
Mo
re t
han
10
,00
0
Better Decision-Making
ImprovedOperational
Efficiency / CostSavings
EnhancedCustomer Service
Growth inRevenues
IncreasedCompetitiveAdvantage
Compliance / RiskManagement
Business Intelligence Achievements by Organization Size
High Achievement Moderate Achievement Acceptable Achievement Not Yet Attempted
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Penetration of Business Intelligence Solutions
In an ongoing (and positive) development, the penetration of business intelligence (as a
percentage of total employees) increases over the past four years (fig. 25). Percentages
of the lowest penetration (< 10 percent) fall from 34 percent to 30 percent in 2018, while
mid-tier (11-20, 21-40, 41-60 percent) all increase. At the highest levels of penetration,
the 61-80 percent segment grows slightly while the highest (> 81 percent) penetration
group declines slightly. The trending of this finding gives us some confidence that the BI
enablement and democratization we saw last year continues to show improvement.
Figure 25 – Business intelligence penetration 2015-2018
0%
5%
10%
15%
20%
25%
30%
35%
40%
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
Penetration of Business Intelligence Solutions2015-2018
2015 2018
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Change in Penetration of BI Solutions 2017-2018
Year over year, increased BI penetration is greatest at the 21-40 percent level, where
there is 2.6 percent growth (fig. 26). We also see lesser growth in mid-tier levels of
penetration, while the highest level of growth declines slightly. The largest decrease is
at the lowest (< 10 percent) level, reflective of the aforementioned improvements in BI
penetration generally.
Figure 26 – Change in penetration of BI solutions 2017-2018
-1.3%
1.3%
1.3%
2.6%
0.9%
-4.7%
-6.0% -5.0% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0% 2.0% 3.0%
81% or more
61 - 80%
41 - 60%
21 - 40%
11 - 20%
Under 10%
Change in Penetration of BI Solutions 2017-2018
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Expansion Plans for Business Intelligence Through 2021
Beyond current deployment, respondents describe bullish plans for expanding BI in
future time frames (we consider the 12-month period the most likely to be supportable
and budgeted) (fig. 27). In this context, respondents expect a decline in the sub-10
percent segment, and an increase in all higher levels of penetration in the coming 12
months. This 12-month finding includes significant growth at high penetration levels
above 41 percent. Extended time frame plans beyond 12 months are expected to
extrapolate higher levels of high BI penetration and lower levels of low penetration.
Figure 27 – Expansion plans for business intelligence through 2021
0% 20% 40% 60% 80% 100%
In 36 months
In 24 months
In 12 months
Today
Expansion Plans for Business Intelligence through 2021
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
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Current Business Intelligence Penetration by Geography
Arguably the most mature among BI markets, North America leads penetration at the
highest (> 81 percent) level in 2018 (fig. 28). Penetration at or above the 41-60 percent
level, however, is slightly higher in EMEA. Asia Pacific respondents report the greatest
number of low-penetration (from less than 10 percent to 40 percent) BI users.
Figure 28 – Business intelligence user penetration today by geography
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
North America Latin America Europe, Middle Eastand Africa
Asia Pacific
Penetration of Business Intelligence Solutions Today by Geography
81% or more
61 - 80%
41 - 60%
21 - 40%
11 - 20%
Under 10%
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Planned Business Intelligence Penetration by Geography
A view of future BI plans by geography reveals distinctions but globally supports the
overall theme of growing expectations in 12, 24 and 36-month time frames (fig. 29).
North America and EMEA expect the most expansion at the highest (> 81 percent) level
in future time frames. Over time, Asia-Pacific respondents by far expect the largest
lingering constituencies of lower penetration (<10 percent, 11-20 percent, 21-40
percent) BI constituencies.
Figure 29 – Planned business intelligence user penetration through 2021 by geography
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
In 1
2 m
on
ths
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on
ths
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on
ths
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ths
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ths
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on
ths
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6 m
on
ths
Europe, Middle Eastand Africa
North America Latin America Asia Pacific
Expansion Plans for Business Intelligence through 2021 by Geography
81% or more
61 - 80%
41 - 60%
21 - 40%
11 - 20%
Under 10%
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Current Business Intelligence Penetration by Function
In 2018, the most penetrated BI users by function are in Executive Management, the
BICC and IT (fig. 30). This tranche generally leads with the most numerous high level
and fewest low-level penetration of users. A second tier of Finance, Sales/Marketing,
Operations reveals noticeably more lingering low-level penetration but still holds distinct
pockets of users at the higher penetration levels. Somewhat curiously, R&D
respondents fare worst at both low and high levels of BI penetration.
Figure 30 – Business intelligence penetration today by function
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
ExecutiveManagement
BusinessIntelligenceCompetency
Center
InformationTechnology (IT)
Finance Sales &Marketing
Operations(e.g.,
Manufacturing,Supply Chain,
Services)
Research andDevelopment
(R&D)
Penetration of Business Intelligence Solutions Today by Function
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
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Planned Business Intelligence Penetration by Function
All functions expect to see increased BI penetration over time (fig. 31). BICC and
Executive Management respondents expect the greatest improvements at varying
levels of penetration. Conversely, Operations respondents expect far larger cohorts of
low to mid-level BI penetration and very little BI penetration at the highest (>81 percent)
level. Notably, IT respondents expect greater future improvements than do
Sales/Marketing respondents.
Figure 31 – Expansion plans for business intelligence through 2021 by function
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
In 1
2 m
on
ths
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4 m
on
ths
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6 m
on
ths
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on
ths
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on
ths
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on
ths
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6 m
on
ths
BusinessIntelligenceCompetency
Center
ExecutiveManagement
InformationTechnology (IT)
Sales &Marketing
Finance Operations (e.g.,Manufacturing,Supply Chain,
Services)
Expansion Plans for Business Intelligence through 2021 by Function
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
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Current Business Intelligence Penetration by Vertical Industry
Both high and low levels of BI penetration vary inconsistently across different vertical
industries (fig. 32). In our 2018 sample, Insurance again provides the best overall result
with the greatest high level and lowest low-level penetration among industries.
Technology reports 40 percent of organizations with 41 percent or greater penetration,
while, by the same measure, Business Services reports 38 percent penetration.
Education and Manufacturing report the lowest overall penetration by weighted mean,
though "pockets" of penetration among certain specialist users in all industries color
these results.
Figure 32 – Penetration of business intelligence solutions today by industry
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Insurance Technology BusinessServices
Retail andWholesale
Healthcare FinancialServices
Manufacturing Education(Higher Ed)
Penetration of Business Intelligence Solutions Today by Industry
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
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Planned Business Intelligence Penetration by Vertical Industry
In our 2018 sample, expansion plans for business intelligence vary unevenly by industry
(fig. 33). Insurance respondents, already with the fewest low-penetration users, expect
to carry that forward and increase penetration most dramatically at high levels.
Technology and Business Services make up a clear second-best tier of future
expectations while Education respondents expect only modest gains in BI penetration in
coming time frames.
Figure 33 – Expansion plans for business intelligence through 2021 by industry
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
In 1
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on
ths
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2 m
on
ths
In 2
4 m
on
ths
In 3
6 m
on
ths
Insurance Technology BusinessServices
Healthcare FinancialServices
Retail andWholesale
Education(Higher Ed)
Expansion Plans for Business Intelligence through 2021 by Industry
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
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Current Business Intelligence Penetration by Organization Size
As we reported in every year of our study, small organizations of one to 100 employees
have higher BI penetration than larger peers (fig. 34). While overall headcount almost
ensures this score, we also 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 saw in earlier
studies, very high penetration rates tend to decrease with organization size, while low
penetration rates tend to linger.
Figure 34 – Penetration of business intelligence solutions today by organization size
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1-100 101-1,000 1,001-10,000 More than 10,000
Penetration of Business Intelligence Solutions Today by Organization Size
81% or more
61 - 80%
41 - 60%
21 - 40%
11 - 20%
Under 10%
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Planned Business Intelligence Penetration by Organization Size
Along with being the most penetrated BI users today, small organizations (1-100
employees) have the steepest expectations for high future BI penetration in coming time
frames (fig. 35). Though less aggressive, mid-sized organizations (101-1,000
employees) expect the next highest number of highly penetrated (> 60 percent) user
audiences. Large and very large organizations (> 10,000 employees) have somewhat
lower expectations (which may be colored by large global headcounts not considered
audiences for business intelligence).
Figure 35 – Expansion plans for business intelligence through 2021 by organization size
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
In 1
2 m
on
ths
In 2
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on
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on
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on
ths
1-100 101-1,000 1,001-10,000 More than 10,000
Expansion Plans for Business Intelligence through 2021 by Organization Size
81% or more
61 - 80%
41 - 60%
21 - 40%
11 - 20%
Under 10%
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Chief Data and Chief Analytics Officers
Beginning 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 cause significant changes 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 longevity of chief data and chief analytics officers is quite
modest but growing over time. Fewer than 15 percent of respondent organizations have
a chief data officer and only about 10 percent have a chief analytics officer (fig. 31). In
both cases, the largest groups of adopters have had a CDO or CAO either five years or
longer or from 3 to 5 years. In the last year, more CDOs have been appointed than
CAOs. Over three years of study, momentum is somewhat in favor of the former as the
preferred role/title.
Figures 36 – Enterprises with chief data or chief analytics officers in place
75% 80% 85% 90% 95% 100%
Chief AnalyticsOfficer (CAO)
Chief DataOfficer (CDO)
Enterprises with Chief Data or Chief Analytics Officers in Place
Don't have one For less than 1 year 1 -3 years 3 - 5 years More than 5 years
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Plans to Implement Chief Data or Chief Analytics Officers
Among the large majority of organizations that have no CDO or CAO, adoption plans for
coming time frames are modest and similar for both roles (fig. 37). Just 5 percent of
organizations say they will name a CDO this year (3 percent will name a CAO), and 11
percent or less will name a CDO or CAO either this year or next. Two-thirds or more of
all organizations currently have no plans to appoint either title.
Figure 37 – Plans to implement Chief Data or Chief Analytics Officer Roles
0% 20% 40% 60% 80% 100%
Chief AnalyticsOfficer (CAO)
Chief DataOfficer (CDO)
Plans to Implement Chief Data or Chief Analytics Officer Roles
This Year Next Year Distant Future No plans
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Effectiveness of Chief Data or Chief Analytics Officers
We asked respondents to describe the effectiveness of a chief data officer or chief
analytics officer in their organization (fig. 38). By this measure, we find that CAOs are
viewed as more effective in their roles than CDOs. More than 20 percent of CAOs are
"highly" effective compared to 17 percent of CDOs. Likewise, CAOs are only about half
as likely (13 percent) as CDOs (25 percent) to have "low" effect on their organization. In
total, however, between 79 and 82 percent of CDOs and CAOs have, at least
"moderate" effectiveness, which indicates that the presence of a CDO or CAO definitely
creates broad workforce awareness and a more noticeable approach to data and
analytics that likely brings more organizational focus.
Figure 38 – Effectiveness of Chief Data and Chief Analytics officers
0% 20% 40% 60% 80% 100%
CAO
CDO
Effectiveness of Chief Data and Chief Analytics Officers
Low Moderate High
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Business Intelligence Achievements by Presence of CDO
We asked respondents to describe the degree of achievement of BI objectives in the
presence of a chief data officer (fig. 39). This question also addresses impact
regardless of the tenure of the CDO. Across all objectives/achievements we study,
organizations with a CDO fare better than those without. In 2018, about half of
respondents with a CDO report a "high" achievement of "better decision-making"
compared to 42 percent of those without a CDO. Similar high achievement levels
accrued in "improved operational efficiency," "enhanced customer service," revenue
growth," and "increased competitive advantage. The largest marginal improvement,
greater than two-to-one, was achieved in the area of compliance and risk management
under the CDO.
Figure 39 – Business intelligence achievements by presence of CDO
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Better Decision-Making
ImprovedOperational
Efficiency / CostSavings
EnhancedCustomer Service
Growth inRevenues
IncreasedCompetitiveAdvantage
Compliance / RiskManagement
Business Intelligence Achievements by Presence of CDO
High Achievement Moderate Achievement Acceptable Achievement Not Yet Attempted
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Business Intelligence Achievements by Presence of CAO
We asked respondents to describe the degree of achievement of BI objectives in the
presence of a chief analytics officer (fig. 40). This question also addresses impact
regardless of the tenure of the CAO. As in the case of the CDO (see previous chart)
organizations with a CAO fare better than those without across all
objectives/achievements. Notably, however, organizations with a CAO versus a CDO
perform at a higher level of achievement in 2018. For example, more than 35 percent of
organizations with a CAO experience "high" achievement in "improved operational
efficiency" compared to about 30 percent in CDO-titled organizations. Likewise, "high"
achievement levels in operational efficiency, revenue growth, and competitive
advantage under the CAO are in the range of 10 percent more likely under a CAO
versus a CDO, while compliance/risk management improvements are about the same
under either title.
Figure 40 – Business intelligence achievements by presence of CAO
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Don'thave one
Lessthan 1
Year - 5+Years
Better Decision-Making
ImprovedOperational
Efficiency / CostSavings
EnhancedCustomer Service
Growth inRevenues
IncreasedCompetitiveAdvantage
Compliance / RiskManagement
Business Intelligence Achievements by Presence of CAO
High Achievement Moderate Achievement Acceptable Achievement Not Yet Attempted
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Enterprises with Chief Data or Chief Analytics Officers by Geography
Measured by geography, the title of chief data officer has the longest net tenure and
most penetration in Latin America followed by Asia Pacific (fig. 41). In reverse order, the
CAO is most penetrated in Asia Pacific followed by Latin America. Asia-Pacific
respondents report the most long-tenured (>5 years) CDOs and CAOs among all
regions. EMEA is the third most likely among geographies to employ either title followed
by North America, though more North American CDOs and CAOs are longer tenured (>
five years) than those in EMEA.
Figure 41 – Enterprises with chief data or chief analytics officers by geography
0%
5%
10%
15%
20%
25%
30%
35%
Asia Pacific LatinAmerica
NorthAmerica
Europe,Middle Eastand Africa
Asia Pacific LatinAmerica
NorthAmerica
Europe,Middle Eastand Africa
Chief Data Officer (CDO) Chief Analytics Officer (CAO)
Enterprises with Chief Data or Chief Analytics Officers by Geography
For less than 1 year 1 -3 years 3 - 5 years More than 5 years
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Enterprises with Chief Data or Chief Analytics Officers by Industry
In 2018, Financial Services organizations report the highest percentages of working
CDOs (34 percent) and CAOs (25 percent) among all industries we sample (fig. 42).
Both Financial Services and Insurance (second most penetrated by both titles) boast
the most long-tenured (>five years) CDOs and CAOs. Retail/Wholesale respondents are
about equally likely to have a CDO or CAO of any tenure, while Healthcare and
Technology organizations are more likely to employ a CDO than a CAO. Education,
Manufacturing and Business Services organizations are less or far less than 10 percent
likely to employ either a CDO or a CAO.
Figure 42 – Enterprises with chief data or chief analytics officers by industry
0%
5%
10%
15%
20%
25%
30%
35%
Fin
anci
al S
erv
ice
s
Insu
ran
ce
Ret
ail a
nd
Wh
ole
sale
Hea
lth
care
Tech
no
logy
Edu
cati
on
(H
igh
er E
d)
Man
ufa
ctu
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g
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sin
ess
Serv
ices
Fin
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erv
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s
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sale
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(H
igh
er E
d)
Man
ufa
ctu
rin
g
Bu
sin
ess
Serv
ices
Chief Data Officer (CDO) Chief Analytics Officer (CAO)
Enterprises with Chief Data or Chief Analytics Officers by Industry
For less than 1 year 1 -3 years 3 - 5 years More than 5 years
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Enterprises with Chief Data or Chief Analytics Officers by Organization Size
Appointments of chief data officers and/or chief analytics officers are, for the most part,
longer tenured, large-organization phenomena (fig. 43). Both large organizations
(1,001-10,000 employees) and very large organizations (>10,000 employees) are more
likely to appoint a chief data officer than a chief analytics officer. Small organizations (1-
100 employees) and mid-sized (101-1,000 employees) are less than 10 percent likely to
employ either title, though among those named, the CDO is a slightly more likely
appointment.
Figure 43 – Enterprises with chief data or chief analytics officers by organization size
0%
5%
10%
15%
20%
25%
30%
35%
1-100 101-1,000 1,001-10,000 More than10,000
1-100 101-1,000 1,001-10,000 More than10,000
Chief Data Officer (CDO) Chief Analytics Officer (CAO)
Enterprises with Chief Data or Chief Analytics Officers by Organization Size
For less than 1 year 1 -3 years 3 - 5 years More than 5 years
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Enterprises with Chief Data and Chief Analytics Officers Reporting Structure
Among organizations with a CAO or CDO, both titles are most likely to report to the
CEO or CIO (fig. 44). Interestingly, and perhaps predictably, chief data officers are more
likely to report to the CIO, while CAOs are more likely to report directly to the CEO. By
this measure, we might conclude that the CAO has more visibility and influence at the
highest levels of the organization. It is worthwhile to also note that Marketing, often
mentioned as the "tip of the spear" of analytic activities, is by far least likely to have
reporting oversight of the CDO or CAO.
Figure 44 – Chief data and chief analytics officer reporting structure
0%
5%
10%
15%
20%
25%
30%
35%
40%
Chief Data Officer (CDO) Chief Analytics Officer (CAO)
Chief Data and Chief Analytics Officer Reporting Structure
CEO CFO CMO CIO Other
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Chief Data and Chief Analytics Officer and Success with BI by Reporting Structure
Organizations with a chief data officer that is completely or somewhat successful with BI
are far more likely to report to the CEO or CFO than they are likely to report to the CIO
or another title. To similar extent, organizations with CAOs who report to the CEO or
CFO have much higher success than those that report to other titles (fig. 45). Thus, we
can observe that BI success of CDOs and CAOs increases (or shows better results)
when reporting to the highest levels of the organization.
Figure 45 – Chief data and chief analytics officer and success with BI by reporting structure
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
CompletelySuccessful
SomewhatSuccessful
SomewhatUnsuccessful
CompletelySuccessful
SomewhatSuccessful
SomewhatUnsuccessful
Chief Data Officer (CDO) Chief Analytics Officer (CAO)
Chief Data and Chief Analytics Officer Reporting Structure by Success with BI
CEO CFO CMO CIO Other
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Number of Business Intelligence Tools in Use
Number of Business Intelligence Tools in Use 2013 to 2018
Across seven years of study, we see relatively minor changes in the number of
business intelligence tools in use by organizations—accompanied by somewhat
improved awareness (fewer "don't know") (fig. 46). Generally, we observe the number of
organizations with only one tool in use declines slightly over time, while the number
using four or more tools increases slightly. This finding gives contradicts the older
expectation that tool proliferation would lead to consolidation. Instead, more recently we
observe an influx of service-based and/or role-based options for BI tools that are easily
implemented and paid for within departmental or project budgets.
Figure 46 – Number of business intelligence tools in use 2013-2018
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2014 2015 2016 2017 2018
Number of Business Intelligence Tools in Use 2013-2018
1 2 3 4 or more Don't know
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Number of Business Intelligence Tools by Geography
North American and EMEA organizations are most likely to use four, five or more
business intelligence tools (red and blue bands, fig. 47). Asia-Pacific and Latin
American respondents are most likely to use only one tool and also most likely to use
up to three tools. Awareness of the number of tools in use is lowest in North America
compared to other regions, though the vast majority of users in all geographies have
awareness of the number of tools in use.
Figure 47 – Number of business intelligence tools in use by geography
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
North America Europe, Middle East andAfrica
Asia Pacific Latin America
Number of Business Intelligence Tools in Use by Geography
1 2 3 4 5 or more Don't know
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Number of Business Intelligence Tools by Function
Executive Management respondents are most likely to report one or two BI tools in use
and (along with BICC respondents), the most BI tool awareness (fig. 48). The BICC,
with the most attention to all organizational BI projects, tends to report higher numbers
of tools in use than do other functions. IT, Sales/Marketing and (interestingly) Finance
are likely to have more BI tools in use than operations. R&D respondents have the
lowest awareness of the number of BI tools in use.
Figure 48 – Number of business intelligence tools in use by function
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Research andDevelopment
(R&D)
InformationTechnology (IT)
BusinessIntelligenceCompetency
Center
Finance Operations(e.g.,
Manufacturing,Supply Chain,
Services)
Sales &Marketing
ExecutiveManagement
Number of Business Intelligence Tools in Use by Function
1 2 3 4 5 or more Don't know
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Number of Business Intelligence Tools by Vertical Industry
By industry, Financial Services respondents are most likely to use four, five or more BI
tools in 2018 (fig. 49). Healthcare respondents are also highly likely to use more than
one and up to five or more tools. Education and Insurance respondents are most likely
to use more than one BI tool and also tend to be the least aware of the number of tools
in use. Manufacturing, Business Services, and Retail/Wholesale respondents are most
likely to use one, two, or three tools.
Figure 49 – Numbers of business intelligence tools in use by industry
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
FinancialServices
Healthcare Education(Higher Ed)
Insurance Technology Manufacturing BusinessServices
Retail andWholesale
Number of Business Intelligence Tools in Use by Industry
1 2 3 4 5 or more Don't know
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Number of Business Intelligence Tools by Organization Size
High organizational headcount historically correlates to greater numbers of business
intelligence tools in use, and the same is true in 2018 (fig. 50). Very large organizations
(> 10,000 employees) are the least likely to use only one tool and most likely to report
five or more tools in use. Small organizations (1-100 employees) are the most likely to
use only one or two BI tools; the number of tools thereafter increases as the number of
global employees increases.
Figure 50 – Number of business intelligence tools in use by organization size
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1-100 101-1,000 1,001-10,000 More than 10,000
Number of Business Intelligence Tools in Use by Organization Size
1 2 3 4 5 or more Don't know
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Technologies and Initiatives Strategic to Business Intelligence
Familiar BI technologies—dashboards, reporting, end-user self-service, advanced
visualization, and data warehousing—top our list of technologies and initiatives strategic
to business intelligence (36 topics are under study in 2018) (fig. 51). Second-tier
initiatives include data discovery, data mining/advanced algorithms, data storytelling,
integration with operational processes, and enterprise and sales planning. Governance,
SaaS, embedded BI, and end-user data preparation are also second-tier priorities.
Compared to many of these longstanding BI assets, some “hot button” topics including
Internet of Things, cognitive BI, and in-memory analysis are relatively low (but growing)
priorities in 2018.
Figure 51 – Technologies and initiatives strategic to business intelligence
0% 20% 40% 60% 80% 100%
Video analytics
Edge computing
Internet of Things (IoT)
Complex event processing (CEP)
Prepackaged vertical / functional analytical…
Social media analysis (Social BI)
Open source software
Natural language analytics (natural language…
Text analytics
Cognitive BI (e.g., Artificial Intelligence-…
Streaming data analysis
In-memory analysis
Search-based interface
IT Analytics
Location intelligence / analytics
Big Data (e.g., Hadoop)
Ability to write to transactional applications
Collaborative support for group-based…
GDPR (General Data Protection Regulation)
End-user data preparation and blending
Embedded BI (contained within an…
Data catalog
Software-as-a-Service and cloud computing
Governance
Mobile device support
Sales Planning
Enterprise planning / budgeting
Integration with operational processes
Data storytelling
Data mining, advanced algorithms, predictive
Data discovery
Data warehousing
Advanced visualization
End-user "self-service"
Reporting
Dashboards
Technologies and Initiatives Strategic to Business Intelligence
Critical Very important Important Somewhat important Not important
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Technology Priorities 2015-2018
Between 2015 and 2018, technology priority rankings (particularly the highest scoring),
remain fairly consistent (fig. 52). In 2018, all priorities under our multi-year study gain
importance year over year, many dramatically. Among middle-tier priorities, notable
gainers include data catalog, governance, and the just-arrived enforcement of GDPR
regulation. Other mid-tier priorities, including collaborative support, location intelligence,
ability to write to transactional systems, and big data are also large relative gainers.
Finally, lower-tier priorities, including natural language processing, text analytics,
cognitive BI, open source, and more are among the largest gainers overall.
Figure 52 – Technology priorities 2015-2018
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Rep
ort
ing
Das
hb
oar
ds
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r "s
elf-
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ice"
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van
ced
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ion
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ased
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ings
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T)
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co
mp
uti
ng
Vid
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an
alyt
ics
Technology Priorities 2015-2018
2015 2016 2017 2018
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Technology Priority Changes 2017-2018
Fig. 53 displays another instructive view of year-over-year gains in business intelligence
priorities. Edge computing, video analytics, and IoT gained 30 percent or more in
importance compared to 2017. Open source, text analytics, natural language analytics,
social media analysis, and cognitive BI are among the next tier of big priority gainers.
The most important and engrained BI priorities overall (reporting, dashboards,
advanced visualization, self-service) grow more slowly than other newer technologies
but remain the "bread and butter" of BI requirements.
Figure 53 – Technology priority changes 2017-2018
1%3%3%
4%7%7%8%8%8%
9%9%9%9%
11%11%
12%12%
15%15%
17%17%
19%20%
21%21%
22%23%23%24%
24%30%
31%32%
0% 10% 20% 30% 40%
Reporting
Dashboards
Advanced visualization
End-user "self-service"
Collaborative support for group-based analysis
Search-based interface
Data warehousing
Mobile device support
Data discovery
Embedded BI (contained within an application, portal, etc.)
Data mining, advanced algorithms, predictive
End-user data preparation and blending
Integration with operational processes
Enterprise planning / budgeting
Data storytelling
Governance
In-memory analysis
Prepackaged vertical / functional analytical applications
Data catalog
Location intelligence / analytics
Big Data (e.g., Hadoop)
Software-as-a-Service and cloud computing
Streaming data analysis
Complex event processing (CEP)
Ability to write to transactional applications
Cognitive BI (e.g., Artificial Intelligence-based BI)
Social media analysis (Social BI)
Natural language analytics (natural language query/ natural language…
Text analytics
Open source software
Internet of Things (IoT)
Video analytics
Edge computing
Change in Technology Priorities 2017-2018
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Technologies and Initiatives Strategic to Business Intelligence by Geography
By region, Latin America leads interest in dashboards, self-service, advanced
visualization, and other lower BI technologies and initiatives (fig. 54.). North American
respondents report the most interest in reporting and mobile device support but only
average interest in most other priorities. EMEA respondents have the most interest in
data discovery, governance, and (expectedly), GDPR. Asia-Pacific respondents give the
highest scores to enterprise planning, integration with operational processes, SaaS,
embedded BI. and many of the lower-priority initiatives.
Figure 54 – Technologies and initiatives strategic to business intelligence objectives by geography
1
1.5
2
2.5
3
3.5
4
4.5Dashboards
ReportingEnd-user "self-service"
Advanced visualization
Data warehousing
Data discovery
Data mining, advanced algorithms,…
Data storytelling
Enterprise planning / budgeting
Integration with operational processes
Sales Planning
Mobile device support
Governance
Software-as-a-Service and cloud…
Data catalog
Embedded BI (contained within an…
End-user data preparation and blendingGDPR (General Data Protection…
Collaborative support for group-based…Ability to write to transactional…Big Data (e.g., Hadoop)
IT Analytics
Location intelligence / analytics
Search-based interface
In-memory analysis
Streaming data analysis
Cognitive BI (e.g., Artificial Intelligence-…
Text analytics
Natural language analytics (natural…
Open source software
Prepackaged vertical / functional…
Social media analysis (Social BI)
Complex event processing (CEP)
Internet of Things (IoT)
Edge computingVideo analytics
Technologies and Initiatives Strategic to Business Intelligence Objectives by Geography
Asia Pacific Latin America North America Europe, Middle East and Africa
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Technologies and Initiatives Strategic to Business Intelligence by Function
As we would expect, functional attitudes toward BI technologies and initiatives often
relate to specific daily roles and responsibilities (fig. 55). Sales/Marketing, BICC, and
Executive Management respondents report the highest interest in dashboards and
advanced visualization. Almost all functions, especially Finance, place high emphasis
on reporting. Finance also leads enterprise planning interest but has low interest in most
other technologies. Operations leads interest in data mining, data storytelling,
integration with operational processes, mobile device support, and data catalog, among
others. BICC respondents most advocate end-user self-service and attach high
importance to many other categories. Unsurprisingly, IT indicates the strongest interest
in IT analytics and governance. R&D shows the lowest interest across the board.
Figure 55 – Technologies and initiatives strategic to business intelligence by function
1
1.5
2
2.5
3
3.5
4
4.5Dashboards
ReportingEnd-user "self-service"
Advanced visualization
Data warehousing
Data discovery
Data mining, advanced algorithms,…
Data storytelling
Enterprise planning / budgeting
Integration with operational processes
Sales Planning
Governance
Mobile device support
Embedded BI (contained within an…
Software-as-a-Service and cloud…
Data catalog
End-user data preparation and blendingGDPR (General Data Protection…
Ability to write to transactional…Collaborative support for group-based…
IT Analytics
Big Data (e.g., Hadoop)
Location intelligence / analytics
Search-based interface
In-memory analysis
Streaming data analysis
Cognitive BI (e.g., Artificial Intelligence-…
Text analytics
Natural language analytics (natural…
Open source software
Prepackaged vertical / functional…
Social media analysis (Social BI)
Complex event processing (CEP)
Internet of Things (IoT)
Edge computingVideo analytics
Technologies and Initiatives Strategic to Business Intelligence Objectives by Function
Sales & Marketing Operations (e.g., Manufacturing, Supply Chain, Services)Business Intelligence Competency Center Information Technology (IT)Executive Management FinanceResearch and Development (R&D)
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Technologies and Initiatives Strategic to Business Intelligence by Vertical Industry
Vertical industries describe a range of interest in different business intelligence
initiatives and priorities (fig. 56). Insurance reports high scores for dashboards,
reporting, end-user self-service, data warehousing, and many more. Business Services
gives the highest marks to advanced visualization, data storytelling, and embedded BI.
Manufacturing most prioritizes sales planning and enterprise planning but trails in other
high-ranking priorities. Technology gives the highest score to software as a service.
Retail/Wholesale gives high marks to data catalog and mobile device support but only
average or lower marks to most other priorities.
Figure 56 – Technologies and initiatives strategic to business intelligence by industry
1
1.5
2
2.5
3
3.5
4
4.5Dashboards
ReportingEnd-user "self-service"
Advanced visualization
Data warehousing
Data discovery
Data mining, advanced algorithms,predictive
Data storytelling
Sales Planning
Governance
Integration with operational processes
Enterprise planning / budgeting
Software-as-a-Service and cloud computing
Data catalog
Embedded BI (contained within anapplication, portal, etc.)
Mobile device support
GDPR (General Data Protection Regulation)End-user data preparation and blending
Collaborative support for group-basedanalysis
Ability to write to transactional applicationsBig Data (e.g., Hadoop)
IT Analytics
Search-based interface
Location intelligence / analytics
In-memory analysis
Cognitive BI (e.g., Artificial Intelligence-based BI)
Text analytics
Streaming data analysis
Natural language analytics (naturallanguage query/ natural language…
Open source software
Prepackaged vertical / functional analyticalapplications
Social media analysis (Social BI)
Complex event processing (CEP)
Internet of Things (IoT)
Edge computingVideo analytics
Technologies and Initiatives Strategic to Business Intelligence Objectives by Industry
Insurance Financial Services Business Services Technology
Healthcare Manufacturing Retail and Wholesale Education (Higher Ed)
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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 in 2018 (fig. 57). That
said, mid-sized organizations of 101 to 1,000 employees lead demand specifically for
dashboards, and small organizations (1-100 employees) expectedly lead interest in
cloud computing as well as social media analysis. We note that the top two initiatives,
reporting and dashboards, are tightly grouped with relative high importance compared
to all other technologies and initiatives.
Figure 57 – Technologies and initiatives strategic to business intelligence by organization size
1
1.5
2
2.5
3
3.5
4
4.5Dashboards
ReportingEnd-user "self-service"
Advanced visualization
Data warehousing
Data discovery
Data mining, advanced algorithms,predictive
Data storytelling
Enterprise planning / budgeting
Integration with operational processes
Sales Planning
Mobile device support
Governance
Software-as-a-Service and cloud computing
Data catalog
Embedded BI (contained within anapplication, portal, etc.)
End-user data preparation and blendingGDPR (General Data Protection Regulation)
Collaborative support for group-basedanalysis
Ability to write to transactional applicationsBig Data (e.g., Hadoop)
IT Analytics
Location intelligence / analytics
Search-based interface
In-memory analysis
Streaming data analysis
Text analytics
Cognitive BI (e.g., Artificial Intelligence-based BI)
Natural language analytics (natural languagequery/ natural language generation)
Open source software
Prepackaged vertical / functional analyticalapplications
Social media analysis (Social BI)
Complex event processing (CEP)
Internet of Things (IoT)
Edge computingVideo analytics
Technologies and Initiatives Strategic to Business Intelligence Objectives by Organization Size
1-100 101-1,000 1,001-10,000 More than 10,000
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Business Intelligence and the State of Data
For a fifth year, we polled respondents for attitudes and behaviors reflective of the “state
of data” in their organizations (fig. 58). As their choices describe, a 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-five percent report
consistent "department-level data." Eleven percent report the worst state of data,
"multiple inconsistent data sources with conflicting semantics and data." These findings
are very similar to the results in 2017.
Figure 58 – Business intelligence and the state of data
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
Data as "truth" - Acommon view ofenterprise data is
available with commonapplication of data, filters,
rules, and semantics
A common view ofenterprise data is
available. However,information views and
semantics aremanipulated to support
specific positions
Consistent data isavailable at a
departmental level.Conflicting, functionalviews of data causes
confusion anddisagreement
We have multiple,inconsistent data sourceswith conflicting semanticsand data. Information isgenerally unreliable and
distrusted
Business Intelligence and the State of Data
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Business Intelligence and the State of Data 2015-2018
Across the last four years of our study, respondents’ overall opinions of their “state of
data” improve slowly (fig. 59). As mentioned, results are very little changed between
2017 and 2018. Over four years, however, we see progressively more "data as truth"
responses and slightly fewer "common view of enterprise data" responses. The lowest
state of data (multiple, inconsistent data sources) remain between 11 and 12 percent
throughout the last four years of our study.
Figure 59 – Business intelligence and the state of data 2015-2018
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2015 2016 2017 2018
Business Intelligence and the State of Data2015-2018
Data as "truth" - A common viewof enterprise data is available withcommon application of data,filters, rules, and semantics
A common view of enterprise datais available. However, informationviews and semantics aremanipulated to support specificpositions
Consistent data is available at adepartmental level. Conflicting,functional views of data causesconfusion and disagreement
We have multiple, inconsistentdata sources with conflictingsemantics and data. Information isgenerally unreliable and distrusted
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Business Intelligence and the State of Data by Geography
Estimations of organizational data maturity plainly differ according to regional
geography (fig. 60). Asia-Pacific respondents report the highest scores for "data as
truth" (44 percent) and the fewest instances of "multiple inconsistent data sources. The
percentage of respondents reporting "data as truth" thereafter declines cross EMEA,
North America and Latin America, while less desirable states of data slowly increase.
Figure 60 – Business intelligence and the state of data by geography
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Asia Pacific Europe, MiddleEast and Africa
North America Latin America
Business Intelligence and the State of Databy Geography
Data as "truth" - A common viewof enterprise data is available withcommon application of data,filters, rules, and semantics
A common view of enterprise datais available. However, informationviews and semantics aremanipulated to support specificpositions
Consistent data is available at adepartmental level. Conflicting,functional views of data causesconfusion and disagreement
We have multiple, inconsistentdata sources with conflictingsemantics and data. Information isgenerally unreliable and distrusted
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Business Intelligence and the State of Data by Function
Estimations of organizational data maturity also vary by function (fig. 61). Finance,
BICC, and Executive Management respondents are most likely to believe in "data as
truth" and highly unlikely to see their data as inconsistent and conflicting. Conversely,
Sales/Marketing respondents are least likely to see "data as truth," though they have
fairly strong mid-level confidence in their data. Manufacturing/Supply Chain and R&D
respondents are most likely to report "multiple inconsistent data sources."
Figure 61 – Business intelligence and the state of data by function
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Business Intelligence and the State of Databy Function
Data as "truth" - A commonview of enterprise data isavailable with commonapplication of data, filters,rules, and semantics
A common view ofenterprise data is available.However, informationviews and semantics aremanipulated to supportspecific positions
Consistent data is availableat a departmental level.Conflicting, functional viewsof data causes confusionand disagreement
We have multiple,inconsistent data sourceswith conflicting semanticsand data. Information isgenerally unreliable anddistrusted
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Business Intelligence and the State of Data by Industry
By industry, Manufacturing, with its rigid production exactness, is most likely (77
percent) to report its data conforms to the two highest states of data (fig. 62).
Retail/Wholesale and Technology respondents are the next most likely to report a state
of "data as truth." Higher Education, Financial Services, and Business Services are
more likely to report the two poorest states of data.
Figure 62 – Business intelligence and the state of data by industry
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Business Intelligence and the State of Databy Industry
Data as "truth" - A commonview of enterprise data isavailable with commonapplication of data, filters,rules, and semantics
A common view of enterprisedata is available. However,information views andsemantics are manipulated tosupport specific positions
Consistent data is available ata departmental level.Conflicting, functional views ofdata causes confusion anddisagreement
We have multiple,inconsistent data sources withconflicting semantics anddata. Information is generallyunreliable and distrusted
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Business Intelligence and the State of Data by Organization Size
Smaller organizations (which on average manage a smaller scope of data than larger
peers) are most likely to “have their act together” with the most reports of "data as truth"
compared to larger peers (fig. 63). Moving left to right, we see the state of data tends to
become less coordinated and more fragmented as organization headcount increases
until it improves slightly at the largest organizations with more than 10,000 employees.
Less than 15 percent of organizations of any size report the lowest state on multiple,
inconsistent data sources, similar to results reported in 2017.
Figure 63 – Business intelligence and the state of data by organization size
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1-100 101-1,000 1,001-10,000 More than10,000
Business Intelligence and the State of Databy Organization Size
Data as "truth" - A commonview of enterprise data isavailable with commonapplication of data, filters,rules, and semantics
A common view of enterprisedata is available. However,information views andsemantics are manipulated tosupport specific positions
Consistent data is available ata departmental level.Conflicting, functional viewsof data causes confusion anddisagreement
We have multiple,inconsistent data sourceswith conflicting semanticsand data. Information isgenerally unreliable anddistrusted
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83
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 2018, respondents paint a positive
picture of their ability to leverage BI insights (fig. 64). More than half of respondents (54
percent) that are able to execute "closed loop" information sharing, do so "all of the
time" or "most of the time." About two-thirds that are able to apply "ad hoc informal"
action on insights do so all or most of the time. Still, 40 percent of organizations with
"underleveraged insights" are in that ineffective state all or most of the time.
Figure 64 – Business intelligence and action on insight
0% 20% 40% 60% 80% 100%
"Closed loop" -Information isshared, teams
work to process itand act in a…
Ad hoc (informal)action on insightsacross functions
Uncoordinated/self-serving action(sometimes at theexpense of others)
Insights areunder-leveraged
Business Intelligence and Action on Insight
All of the time Most of the time Some of the time Rarely
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Business Intelligence and Action on Insight by Geography
Organizational estimations of the ability to take action on insight vary unevenly by
geographic region (fig. 65). Organizations with effective "closed loop" information
sharing are more often found in EMEA and Latin America. Globally, organizations are
strongest in "ad hoc, informal insights," especially in Latin America and Asia Pacific.
Latin America and Asia-Pacific respondents are also more likely to display
“uncoordinated/self-serving" action than other regions. Underleveraged insight
organizations are more prevalent in North America compared to other regions.
Figure 65 – Business intelligence and action on insight by geography
0%10%20%30%40%50%60%70%80%90%
100%
Lati
n A
mer
ica
Asi
a P
acif
ic
No
rth
Am
eric
a
Euro
pe,
Mid
dle
Eas
t an
d A
fric
a
Lati
n A
mer
ica
Asi
a P
acif
ic
No
rth
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eric
a
Euro
pe,
Mid
dle
Eas
t an
d A
fric
a
Lati
n A
mer
ica
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ic
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eric
a
Euro
pe,
Mid
dle
Eas
t an
d A
fric
a
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n A
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ica
Asi
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acif
ic
No
rth
Am
eric
a
Euro
pe,
Mid
dle
Eas
t an
d A
fric
a
"Closed loop" - Information isshared, teams work to processit and act in a timely fashion.
No formal boundaries
Ad hoc (informal) action oninsights across functions
Uncoordinated/ self-servingaction (sometimes at the
expense of others)
Insights are under-leveraged
Business Intelligence and Action on Insightby Geography
Rarely Some of the time Most of the time All of the time
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Business Intelligence and Action on Insight by Function
BICC respondents, most aware of BI and collaborative capabilities, have traditionally
been the most confident in their organization's ability to take action on insight. This is
again the case among "closed loop" sharing organizations in 2018 (fig. 66). Within
"closed loop" organizations, executive and R&D respondents tend to be least effective.
In low-performing organizations where "insights are rarely leveraged," operations
performs worst among functions.
Figure 66 – Business intelligence and action on insight by function
0%10%20%30%40%50%60%70%80%90%
100%
Op
erat
ion
s (e
.g.,
Man
ufa
ctu
rin
g,…
Sale
s &
Mar
keti
ng
Fin
ance
Info
rmat
ion
Tec
hn
olo
gy (
IT)
Res
ear
ch a
nd
Dev
elo
pm
en
t (R
&D
)
Bu
sin
ess
Inte
llige
nce
Co
mp
ete
ncy
…
Exe
cuti
ve M
anag
emen
t
Op
erat
ion
s (e
.g.,
Man
ufa
ctu
rin
g,…
Sale
s &
Mar
keti
ng
Fin
ance
Info
rmat
ion
Tec
hn
olo
gy (
IT)
Res
ear
ch a
nd
Dev
elo
pm
en
t (R
&D
)
Bu
sin
ess
Inte
llige
nce
Co
mp
ete
ncy
…
Exe
cuti
ve M
anag
emen
t
Op
erat
ion
s (e
.g.,
Man
ufa
ctu
rin
g,…
Sale
s &
Mar
keti
ng
Fin
ance
Info
rmat
ion
Tec
hn
olo
gy (
IT)
Res
ear
ch a
nd
Dev
elo
pm
en
t (R
&D
)
Bu
sin
ess
Inte
llige
nce
Co
mp
ete
ncy
…
Exe
cuti
ve M
anag
emen
t
Op
erat
ion
s (e
.g.,
Man
ufa
ctu
rin
g,…
Sale
s &
Mar
keti
ng
Fin
ance
Info
rmat
ion
Tec
hn
olo
gy (
IT)
Res
ear
ch a
nd
Dev
elo
pm
en
t (R
&D
)
Bu
sin
ess
Inte
llige
nce
Co
mp
ete
ncy
…
Exe
cuti
ve M
anag
emen
t
"Closed loop" - Information isshared, teams work to processit and act in a timely fashion.
No formal boundaries
Ad hoc (informal) action oninsights across functions
Uncoordinated/ self-servingaction (sometimes at the
expense of others)
Insights are under-leveraged
Business Intelligence and Action on Insightby Function
Rarely Some of the time Most of the time All of the time
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86
Business Intelligence and Action on Insight by Vertical Industry
Industries generally exhibit high confidence in their ability to take action on insight (fig.
67). In our 2018 sample, respondents in Insurance report the most frequent closed-loop
and ad hoc processes and greatest ability to act on insight. Financial Services is the
second-best performing industry. Manufacturing and Business Services are generally
the industries least able to take action on insight in 2018.
Figure 67 – Business Intelligence and action on insight by industry
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Insu
ran
ce
Fin
anci
al S
erv
ice
s
Ret
ail a
nd
Wh
ole
sale
Tech
no
logy
Hea
lth
care
Edu
cati
on
(H
igh
er E
d)
Bu
sin
ess
Serv
ices
Man
ufa
ctu
rin
g
Insu
ran
ce
Fin
anci
al S
erv
ice
s
Ret
ail a
nd
Wh
ole
sale
Tech
no
logy
Hea
lth
care
Edu
cati
on
(H
igh
er E
d)
Bu
sin
ess
Serv
ices
Man
ufa
ctu
rin
g
Insu
ran
ce
Fin
anci
al S
erv
ice
s
Ret
ail a
nd
Wh
ole
sale
Tech
no
logy
Hea
lth
care
Edu
cati
on
(H
igh
er E
d)
Bu
sin
ess
Serv
ices
Man
ufa
ctu
rin
g
Insu
ran
ce
Fin
anci
al S
erv
ice
s
Ret
ail a
nd
Wh
ole
sale
Tech
no
logy
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lth
care
Edu
cati
on
(H
igh
er E
d)
Bu
sin
ess
Serv
ices
Man
ufa
ctu
rin
g
"Closed loop" - Information isshared, teams work to processit and act in a timely fashion.
No formal boundaries
Ad hoc (informal) action oninsights across functions
Uncoordinated/ self-servingaction (sometimes at the
expense of others)
Insights are under-leveraged
Business Intelligence and Action on Insightby Industry
Rarely Some of the time Most of the time All of the time
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Business Intelligence and Action on Insight by Organization Size
Organizations of different size show similar capabilities in their ability to act on insight
(fig. 68). Among those with "closed loop" sharing, small organizations are somewhat
more able to succeed all or some of the time compared to larger peers. Performance
continues to deteriorate faster at larger organizations as the ability to act on insight
more often falls to "uncoordinated" and "underleveraged."
Figure 68 – Business intelligence and action on insight by organization size
0%
10%
20%
30%
40%
50%
60%
70%
80%
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100%
1-1
00
10
1-1
,00
0
1,0
01
-10
,00
0
Mo
re t
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10
,00
0
1-1
00
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1-1
,00
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01
-10
,00
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,00
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00
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1-1
,00
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01
-10
,00
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,00
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00
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1-1
,00
0
1,0
01
-10
,00
0
Mo
re t
han
10
,00
0
"Closed loop" - Informationis shared, teams work to
process it and act in a timelyfashion. No formal
boundaries
Ad hoc (informal) action oninsights across functions
Uncoordinated/ self-servingaction (sometimes at the
expense of others)
Insights are under-leveraged
Business Intelligence and Action on Insight by Organization Size
Rarely Some of the time Most of the time All of the time
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88
Success with Business Intelligence
Over the last three years of our ongoing study, our core measure of "success with
business intelligence" declines somewhat (fig. 69). Organizations that reported being
"completely successful" reached an all-time high of 37 percent in 2016, but that result
falls to 33 percent in 2017 and to 31 percent in 2018. Also, the number of "somewhat
successful" organizations increases from 8 percent in 2016 to 14 percent in 2017 before
falling to 11 percent this year. We cannot be certain what mix of new BI objectives
and/or expectations these scores represent, but we are not surprised to see continuing
fluctuation of satisfaction with BI, even in the wake of many successes.
Figure 69 – Success with business intelligence 2015-2018
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2015 2016 2017 2018
Success with Business Intelligence2015-2018
Completely Successful Somewhat Successful Somewhat Unsuccessful Unsuccessful
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Change in Success with Business Intelligence 2017-2018
Year-over-year changes in levels of success with BI fluctuates at different levels of
achievement (fig. 70). For example, the number of “completely successful”
organizations falls by about 2 percent, but the number of “somewhat successful”
organizations increases by 3.3 percent. There are also about 2.6 percent more
“somewhat successful” organizations than reported in 2017.
Figure 70 – Change in success with business intelligence 2017-2018
0.14%
-2.55%
3.34%
-1.93%
-3.00% -2.00% -1.00% 0.00% 1.00% 2.00% 3.00% 4.00%
Unsuccessful
Somewhat Unsuccessful
Somewhat Successful
Completely Successful
Change in Success with Business Intelligence2017-2018
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How Successful Organizations Measure Success with Business Intelligence
Beginning in 2017, we asked respondents to quantify in more detail how they measure
the success of business intelligence initiatives (fig. 71). The top results in 2017 and
again (even more affirmatively) this year are "user feedback/satisfaction" (78 percent),
followed by "customer feedback/satisfaction" (52 percent), "ROI" (41 percent),
"system/app activity" (38 percent), and "numbers of users" (30 percent). A clear
takeaway from this view is rather obviously to "engage with users" rather than focus on
system activity or raw numbers of users.
Figure 71 – Measures of success with business intelligence
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Userfeedback/satisfaction
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Return oninvestment (ROI)
model
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Other
Measures of Success with Business Intelligence
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How Unsuccessful Organizations Measure Failure with Business Intelligence
We also asked respondents to better quantify how they measure the failure of business
intelligence initiatives (fig. 72). The top results are "limited adoption" (60 percent),
followed by "lack of usage" (57 percent), and "user feedback" (46 percent). From this
view, we might conclude that less successful organizations are less likely to judge their
failure on user feedback than successful ones (or that user feedback is solicited
following observations of limited adoption). Unsuccessful companies, instead, are more
likely to judge their failure on less transparent, high-level (often IT) metrics of system
activity and user headcount. We believe the findings in figs. 70 and 71 also demonstrate
the value and more frequent success of organizations with a business intelligence
competency center.
Figure 72 – Measures of failure with business intelligence
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Limited adoption Lack of usage User feedback Cost/Return oninvestment
Other
Measures of Failure with Business Intelligence
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Success with Business Intelligence by Organization Size
The smallest organizations are most likely (37 percent) to consider themselves
“completely successful” with BI, and 87 percent say they are at least “somewhat
successful” (fig.73). "Complete" success thereafter declines slowly as global headcount
increases. In large and very large organizations, however, the number of organizations
that are at least "somewhat successful” is comparable (about 87 percent) to small
organizations that are at least somewhat successful. Mid-sized organizations (101-
1,000 employees) are slightly less successful than all other cohorts by size.
Figure 73 – Success with business intelligence by organization size
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Success with Business Intelligenceby Organization Size
Completely Successful Somewhat Successful Somewhat Unsuccessful Unsuccessful
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Success with Business Intelligence by BI Objectives
Organizations that are successful with business intelligence are most likely to focus on
the full range of objectives we sampled in 2018 (fig. 74). Among organizations that are
"completely successful" with BI, all objectives except "compliance/risk management" are
above or well above an adjusted mean value of 4.0 ("very important"). Thus, a holistic
embrace of BI objectives is reflective of success, though "better decision-making"
remains the foremost guidepost. Organizations that consider themselves unsuccessful
are less emphatic in all areas and possibly more likely to look for hard over "soft
benefits" of generally improved performance.
Figure 74 – Success with business intelligence by BI objectives
0
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5Better Decision-Making
Improved OperationalEfficiency / Cost Savings
Growth in Revenues
Increased CompetitiveAdvantage
Enhanced Customer Service
Compliance / RiskManagement
Business Intelligence Objectives by Success with BI
Completely Successful Somewhat Successful Somewhat Unsuccessful Unsuccessful
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Success with Business Intelligence by Targeted Users
Organizations at all levels of BI success are most likely to first target executives as
users (fig. 75). We notice that the least successful BI organizations take executive focus
to the extreme (though this failing may also be tied to fledgling attempts at BI). We also
see that successful BI organizations are most likely to put high emphasis on managers
and individual contributors. Successful organizations are also the most likely to target
customers and more likely to target partners. While the findings are not linear, a broad
targeting focus is generally an indicator of BI success.
Figure 75 – Success with business intelligence by targeted users
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Success With Business Intelligence by Targeted Users
Executives Middle Managers
Individual Contributors and Professionals Line Managers
Customers Partners / Affliates
Suppliers
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Success with Business Intelligence and Technology Priorities
Organizations with a chief data officer or chief analytics officer with "high" or "moderate"
effectiveness are highly likely to report success with business intelligence (fig. 76). This
highly linear finding relative to success would by itself appear to be an endorsement of
the CDO/CAO function, though we reiterate that the criteria for what constitutes
"effectiveness" and sentiments of success are abstract and in the eye of the beholder.
Figure 76 – Effectiveness of chief data and analytics officers by success with BI
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CDO CAO
Effectiveness of Chief Data and Chief Analytics Officers by Success with BI
Low Moderate High
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Success with Business Intelligence and Technology Priorities
Organizations that are successful with business intelligence pay considerably more
attention to multiple BI-related technology priorities than all other groups (fig. 77). This
diversity of attention extends from the most basic (reporting, dashboards) to the more
obscure priorities. Almost without fail, unsuccessful organizations care much less about
any and all BI initiatives and technologies. The most significant gaps in priorities
between high- and low-performing BI organizations also run from the foundational
(dashboards, reporting, end-user self-service) to the emerging and experimental
(natural language, social media analysis). Areas where unsuccessful organizations pay
above-average attention appear to be one-off initiatives in projects for text analytics or
complex event processing. Overall, attention to technologies and initiatives diminishes
in an orderly way as success with BI diminishes.
Figure 77 – Technologies and initiatives strategic to business intelligence by BI success
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4.5Dashboards
ReportingEnd-user "self-service"
Advanced visualization
Data warehousing
Data discovery
Data mining, advanced algorithms,…
Data storytelling
Integration with operational processes
Governance
Mobile device support
Sales Planning
Data catalog
Embedded BI (contained within an…
Software-as-a-Service and cloud…
Enterprise planning / budgeting
GDPR (General Data Protection…End-user data preparation and blending
Collaborative support for group-based…Ability to write to transactional…
Location intelligence / analytics
Big Data (e.g., Hadoop)
IT Analytics
In-memory analysis
Search-based interface
Streaming data analysis
Cognitive BI (e.g., Artificial Intelligence-…
Text analytics
Natural language analytics (natural…
Social media analysis (Social BI)
Open source software
Prepackaged vertical / functional…
Complex event processing (CEP)
Internet of Things (IoT)
Edge computingVideo analytics
Technologies and Initiatives Strategic to Business Intelligence by BI Success
Completely Successful Somewhat Successful Somewhat Unsuccessful Unsuccessful
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Success with Business Intelligence and Number of BI Tools
In 2018 (and historically), we find that organizations that are successful with business
intelligence generally have fewer tools in use, an outcome we associate with strategic
intent and engaged leadership (fig. 78). However, this relationship is not without
exception. For example, similar percentages of completely successful and
"unsuccessful" organizations are about equally likely to employ five or more BI tools.
Awareness of the number of tools in use also increases with the degree of BI success.
As noted elsewhere in this report (figs. 46-50, pp. 65-69) the emergence of specialized /
lightweight / service-based BI tools could change the historically virtuous pursuit of BI
tool consolidation.
Figure 78 – Number of business intelligence tools in use by success with BI
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Completely Successful Somewhat Successful Somewhat Unsuccessful Unsuccessful
Number of Business Intelligence Tools in Use by Success with BI
1 2 3 4 5 or more Don't know
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Success with Business Intelligence and the State of Data
Success with business intelligence correlates directly and powerfully to an
organization’s state of data (fig. 79). Organizations that view data as “truth” are more
than 80 percent likely to be "successful," compared to 40 percent for "somewhat
unsuccessful" and just 12 percent of "unsuccessful" organizations. "Unsuccessful" BI
organizations are 60 percent likely to have "multiple, inconsistent data sources."
Figure 79 – Business intelligence and the state of data by BI success
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Business Intelligence and the State of Data by BI Success
Data as "truth" - A common viewof enterprise data is available withcommon application of data,filters, rules, and semantics
A common view of enterprise datais available. However, informationviews and semantics aremanipulated to support specificpositions
Consistent data is available at adepartmental level. Conflicting,functional views of data causesconfusion and disagreement
We have multiple, inconsistentdata sources with conflictingsemantics and data. Information isgenerally unreliable and distrusted
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Success with Business Intelligence and Action on Insight
Success with business intelligence also correlates very strongly with an organization’s
ability to take action on insights (fig. 80). At the high end of performance, more than 70
percent of organizations with closed-loop processes are completely successful all or
some of the time. The poorest-performing organizations are more than 70 percent likely
to report under-leveraged insights all or some of the time. In summary, organizations
with the lowest level of coordination are much more likely to fail than to succeed.
Figure 80 – Business intelligence and action on insight by BI success
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No formal boundaries
Ad hoc (informal) action oninsights across functions
Uncoordinated/ self-servingaction (sometimes at the
expense of others)
Insights are under-leveraged
Business Intelligence and Action on Insight by BI Success
Rarely Some of the time Most of the time All of the time
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Success with Business Intelligence and Penetration of Users
In near linear fashion, organizations that are more successful with business intelligence
have a higher number of users as a percentage of the workforce (fig. 81). Sixty percent
of "completely successful" organizations have BI penetration levels of 21-40 percent or
greater. In contrast, only 33 percent of "unsuccessful" organizations can claim this level
of penetration. More than half of "unsuccessful" organizations have less than 10 percent
BI penetration, compared to just 20 percent of "completely successful" organizations.
This finding makes a strong case for the merits of "information democracy" but does not
diminish the challenge of providing "actionable" information to users.
Figure 81 – Penetration of business intelligence solutions today by BI success
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Penetration of Business Intelligence Solutions Today by BI Success
81% or more
61 - 80%
41 - 60%
21 - 40%
11 - 20%
Under 10%
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Business Intelligence Achievements by Success with BI
As we would expect, high-achieving organizations are far more likely to be successful at
BI (fig. 82). In terms of quantifiable measures, for example, "completely successful"
organizations are 3.5 times more likely to achieve "better decision-making" than those
with unsuccessful BI programs. "Completely successful" organizations are almost three
times more likely to achieve "improved operational efficiency," compared to
unsuccessful organizations. Organizations that are "unsuccessful” at BI are also far less
likely to have attempted to meet various BI objectives.
Figure 82 – Business intelligence achievements by success with BI
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Better Decision-Making
ImprovedOperational
Efficiency / CostSavings
EnhancedCustomer Service
Growth inRevenues
IncreasedCompetitiveAdvantage
Compliance / RiskManagement
Business Intelligence Achievements by Success with BI
High Achievement Moderate Achievement Acceptable Achievement Not Yet Attempted
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Technologies and Initiatives Strategic to Business Intelligence by BI Success
Predictably, organizations that are more successful at business intelligence are, by far,
more likely to place a higher priority upon multiple technologies and initiatives (fig. 83).
Some of the widest gaps between successful and unsuccessful organizations are in
technologies including dashboards, mobile device support—and also some more
advanced undertakings including big data.
Figure 83 – Technologies and initiatives strategic to business intelligence by BI success
1
1.5
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3.5
4
4.5Dashboards
ReportingEnd-user "self-service"
Advanced visualization
Data warehousing
Data discovery
Data mining, advanced algorithms,…
Data storytelling
Integration with operational processes
Governance
Mobile device support
Sales Planning
Data catalog
Embedded BI (contained within an…
Software-as-a-Service and cloud…
Enterprise planning / budgeting
GDPR (General Data Protection…End-user data preparation and blending
Collaborative support for group-based…Ability to write to transactional…
Location intelligence / analytics
Big Data (e.g., Hadoop)
IT Analytics
In-memory analysis
Search-based interface
Streaming data analysis
Cognitive BI (e.g., Artificial Intelligence-…
Text analytics
Natural language analytics (natural…
Social media analysis (Social BI)
Open source software
Prepackaged vertical / functional…
Complex event processing (CEP)
Internet of Things (IoT)
Edge computingVideo analytics
Technologies and Initiatives Strategic to Business Intelligence by BI Success
Completely Successful Somewhat Successful Somewhat Unsuccessful Unsuccessful
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Budget Plans for Business Intelligence
We asked organizations whether they will increase, decrease, or maintain existing
business intelligence budgets (fig. 84). In 2018, more than half of respondent
organizations plan to increase BI investment while just 6 percent will decrease BI
budgets from 2016 levels. The remaining 40 percent will maintain current budgeting.
(We do not know the extent to which BI expansion might consist of departmental
spending or the adoption of BI subscription services.)
Figure 84 – Budget plans for business intelligence
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Increasing over last year Staying the same as last year Decreasing over last year
Budget Plans for Business Intelligence
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Budget Plans for Business Intelligence 2017-2018
Between 2017 and 2018, budgets for business intelligence increase slightly (fig. 85).
The number of organizations that increase spending rises from 50-54 percent, while the
number that maintains current spending levels declines from 45-40 percent. Again, we
are not aware of the extent to which BI expansion might consist of departmental
spending or the adoption of BI services.
Figure 85 – Budget plans for business intelligence 2017-2018
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Budget Plans for Business Intelligence2017-2018
Increasing over last year Staying the same as last year Decreasing over last year
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Budget Plans for Business Intelligence by Geography
Although a majority of organizations across all geographies plan to increase BI
spending in 2018, EMEA (57 percent) and Asia Pacific (55 percent) are more likely to
increase budgets (fig. 86). Organizations that maintain current budgets are more
commonly found in Latin America (44 percent) and North America (42 percent). About 9
percent of EMEA organizations, the most in any geography, plan to decrease BI
budgets compared to last year.
Figure 86 – Budget plans for business intelligence by geography
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Budget Plans for Business Intelligence by Geography
Increasing over last year Staying the same as last year Decreasing over last year
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Budget Plans for Business Intelligence by Function
In 2018, 70 percent of BICC respondents (usually the most ardent BI advocates) say
they will increase BI spending over last year (fig. 87). Majorities of respondents in
Finance (61 percent), Sales/Marketing (58 percent), and Executive Management (58
percent) also plan to increase spending. In R&D and IT, the majority of respondents
plan to maintain or decrease BI spending.
Figure 87 – Budget plans for business intelligence by function
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Services)
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(R&D)
InformationTechnology (IT)
Budget Plans for Business Intelligenceby Function
Increasing over last year Staying the same as last year Decreasing over last year
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Budget Plans for Business Intelligence by Vertical Industry
In 2018, 73 percent of Insurance respondents plan to increase BI spending, and none
plan to decrease spending (fig. 88). Healthcare, Manufacturing, and Financial Services
organizations are the next most likely to increase or maintain BI spending. At the other
end of the spectrum, only about half of Retail/Wholesale, Technology, and Business
Services plan to increase BI spending.
Figure 88 – Budget plans for business intelligence by industry
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Budget Plans for Business Intelligenceby Industry
Increasing over last year Staying the same as last year Decreasing over last year
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Budget Plans for Business Intelligence by Organization Size
In 2018, increases in BI spending become more common as global headcount
increases (fig. 89). Very large organizations (>10,000 employees) are most likely (60
percent) to increase BI spending, compared to just over 50 percent of small
organizations (1-100 employees). About 10 percent of very large organizations also
plan to decrease BI spending compared to last year, the most of any group by size.
Figure 89 – Budget plans for business intelligence by organization size
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Budget Plans for Business Intelligenceby Organization Size
Increasing over last year Staying the same as last year Decreasing over last year
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Budget Plans for Business Intelligence by Penetration of BI Solutions
In 2018, BI spending plans do not correlate neatly with existing BI penetration (fig. 90).
However, we can say generally that organizations with lower levels of BI penetration are
often more likely to decrease than maintain or increase BI budgets. (Another way of
expressing this is that penetration requires investment.) Both saturation and histories of
success may be involved in these decisions. For example, more organizations with the
highest level of BI penetration (81 percent or more) plan to maintain versus increase BI
budgets. However, organizations with lower levels of penetration (40 percent or less)
are more likely to decrease than maintain or increase BI budgets in 2018.
Figure 90 – Budget plans by penetration of business intelligence solutions
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Budget Plans by Penetration of Business Intelligence Solutions
81% or more
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Under 10%
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Expansion Plans for Business Intelligence through 2021 by BI Budget Plans
Expansion plans for BI spending through 2021 are more predictable than 2018 plans
alone. For example, organizations with increased budgets are more likely to plan for
deeper user penetration than those staying at the same levels or decreasing budgets
(fig. 91). At the same time, organizations decreasing spending are more focused on the
two lowest levels of penetration in coming timeframes.
Figure 91 – Expansion plans for business intelligence through 2021 by BI budget plans
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Expansion Plans for Business Intelligence through 2021 by BI Budget Plans
81% or more
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Budget Plans for Business Intelligence by Success with BI
In near-linear fashion, organizations that are successful with business intelligence are
incrementally more likely to increase BI spending in 2018 (fig. 92). Sixty percent of
"completely successful" organizations will increase budgets this year, compared to 40
percent of "somewhat unsuccessful" and 31 percent of "completely unsuccessful"
organizations. As success decreases, organizations also are more likely to decrease
year-over-year budgets.
Figure 92 – Budget plans for business intelligence by success with BI
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Budget Plans for Business Intelligenceby Success with BI
Increasing over last year Staying the same as last year Decreasing over last year
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Business Intelligence Achievements by BI Budget Plans
High-achieving organizations are more likely to increase spending on discrete BI
achievements "across the board" (fig. 93). Likewise, organizations with "acceptable"
achievement or those with goals not yet attempted are comparably less likely to
increase budgets and more likely to decrease budgets. Generally, business intelligence
achievement requires strategic intent and investment that is reflected in budget priorities
that follow the same order as BI objectives (fig. 16, p. 35).
Figure 93 – Business intelligence achievements by BI budget plans
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ove
r la
st y
ear
Stay
ing
the
sam
e a
s la
st y
ear
Dec
reas
ing
ove
r la
st y
ear
Incr
eas
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ove
r la
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ear
Stay
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the
sam
e a
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ear
Dec
reas
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ove
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ear
Better decision-making
Improvedoperational
efficiency/costsavings
Enhancedcustomer service
Growth inrevenues
Increasedcompetitiveadvantage
Compliance / RiskManagement
Business Intelligence Achievements by BI Budget Plans
High Achievement Moderate Achievement Acceptable Achievement Not Yet Attempted
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Technologies and Initiatives Strategic to Business Intelligence by BI Budget Plans
Planned BI spending generally—but not always—increases in proportion with the
priority ranking of BI technologies and initiatives (fig. 94). For example, those with
increased budgets place the highest priority upon the number one and two
technology/initiative priorities, dashboards. and reporting. This is followed by advanced
visualization and data warehousing. At the same time, more esoteric initiatives such as
edge computing and IoT are more likely to see investment from these organizations
than those maintaining or decreasing investment. In contrast, less well-funded
organizations are more likely to invest in open source software to reduce costs.
Figure 94 – Technologies and initiatives strategic to business intelligence by BI budget plans
1
1.5
2
2.5
3
3.5
4
4.5Dashboards
ReportingEnd-user "self-service"
Advanced visualization
Data warehousing
Data discovery
Data mining, advanced algorithms,…
Data storytelling
Integration with operational processes
Governance
Mobile device support
Sales Planning
Data catalog
Embedded BI (contained within an…
Software-as-a-Service and cloud…
Enterprise planning / budgeting
GDPR (General Data Protection…End-user data preparation and blending
Ability to write to transactional…Collaborative support for group-based…Location intelligence / analytics
Big Data (e.g., Hadoop)
IT Analytics
In-memory analysis
Search-based interface
Streaming data analysis
Text analytics
Cognitive BI (e.g., Artificial Intelligence-…
Natural language analytics (natural…
Open source software
Social media analysis (Social BI)
Prepackaged vertical / functional…
Complex event processing (CEP)
Internet of Things (IoT)
Edge computingVideo analytics
Technologies and Initiatives Strategic to Business Intelligence by BI Budget Plans
Increasing over last year Staying the same as last year Decreasing over last year
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Business Intelligence Product Replacements
Current Business Intelligence Products Replaced by Another
In 2018, we asked respondents whether their current BI product replaced another BI
product (fig. 95), which revealed what might be considered a surprising amount of BI
tool continuity. Seventy-six percent of respondents say replacement of another product
was not the outcome of BI tool or service acquisition. That said, in many cases we
would expect they implemented a product where there was no product before. In other
cases, they might have implemented a new product to serve a different audience or
function with new capabilities. However, the net replacement rate is somewhat lower
than we would have expected.
Figure 95 – Current BI product replaced by another BI product
Yes24%
No76%
Current BI Product Replaced Another BI Product
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Reasons BI Products Are Replaced
For the 25 percent that indicate their current BI product replaced another (fig. 96), the
primary reasons for doing so are functionality (80 percent) and modernization (63
percent). Cost and corporate standards are cited as a primary reason for replacing a BI
product less than 20 percent of the time.
Figure 96 – Reasons BI products are replaced
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Functionality Modernization Cost Corporate standard
Reasons for BI Product Replacement
Was Not a Factor
Secondary Reason
Primary Reason
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Industry and
Vendor
Analysis
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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.
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Industry Performance
Sales/Acquisition Experience
Year over year, we observe mostly small declines in measures of industry sales and
acquisition performance (fig. 97). “Contractual terms” and “flexibility/accommodation”
increase slightly while all other measures decline somewhat, though not radically.
Across five years of data, "professionalism" and "product knowledge" retain the highest
and most consistent scores in our evaluation.
Figure 97 – Industry performance — sales and acquisition experience: 2014-2018
3.60
3.70
3.80
3.90
4.00
4.10
4.20
4.30
4.40
2014 2015 2016 2017 2018
Industry Performance – Sales and Acquisition Experience: 2014-2018
Business practices Contractual terms and conditions
Flexibility/accommodation Follow up after the sale
Product knowledge Professionalism
Responsiveness Understanding our business/needs
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Value
End users continue to report that they receive high and improving value from industry
vendors over time (fig. 98). Industry performance is at an all-time high (adjusted mean
4.2), which more than reverses the only recorded decline in value performance in 2017.
Across the last four years of our study, the ongoing trend line above 4.0 is strongly
positive.
Figure 98 – Industry performance — value 2014-2018
3.90
3.95
4.00
4.05
4.10
4.15
4.20
4.25
2014 2015 2016 2017 2018
Industry Performance - Value: 2014-2018
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Quality and Usefulness of Product
Many measures of industry quality and usefulness touch all-time highs in 2018 (fig. 99).
These include “completeness of functionality, “customization/extensibility,” “ease of
administration,” “online training and documentation,” “reliability,” and “robustness.”
However, between 2017 and 2018, there are slight declines in “integration of
components within product” (often a byproduct of technology acquisitions) and
“scalability.” Overall trending remains positive in this category.
Figure 99 – Industry performance — quality and usefulness of products: 2014-2018
3.5
3.6
3.7
3.8
3.9
4.0
4.1
4.2
4.3
Completeness offunctionality
Customization andextensibility
Ease of administration
Ease of installation
Ease of upgrade/migrationto new versions
Integration of componentswithin product
Integration with third-partytechnologies
Online training, forums anddocumentation
Overall usability
Reliability of technology
Robustness/sophistication oftechnology
Scalability
Industry Performance - Quality and Usefulness of Products: 2014-2018
2014 2015 2016 2017 2018
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Technical Support
In 2018, all measures of industry technical support decline, albeit modestly (fig. 100).
Most notably, “continuity of personnel” falls from an all-time high to back below an
adjusted mean of 4.0. Over the last five years of our study, all other measures trend
positively including those we see relevant to immediacy in support of urgent customer
needs. In total, these results indicate investments in support that should continue into
the future.
Figure 100 – Industry performance — technical support: 2014-2018
3.50
3.60
3.70
3.80
3.90
4.00
4.10
4.20
4.30
4.40
Professionalism Product knowledge Responsiveness Continuity ofpersonnel
Time to resolveproblems
Industry Performance - Technical Support: 2014-2018
2014 2015 2016 2017 2018
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Consulting
BI vendor consulting scores drop noticeably between 2017 and 2018 (fig. 101).
Curiously, almost every attribute we measure reached an all-time high in 2017 but
capitulated to all-time low measurements in 2018. We cannot immediately assign a
reason to this across-the-board decline in vendor consulting satisfaction, though it
merits future examination. All scores nonetheless remain in the range of 3.8 to 4.0,
approaching “very good.”
Figure 101 – Industry performance — BI vendor consulting: 2014-2018
3.5
3.6
3.7
3.8
3.9
4
4.1
4.2
4.3
4.4
Professionalism Productknowledge
Experience Continuity Value
Industry Performance - BI Vendor Consulting 2014-2018
2011
2012
2013
2014
2015
2016
2017
2018
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Integrity
Vendor integrity—measured as honesty and truthfulness in all dealings—continues its
slow, long-term upward climb to an all-time high in 2018 (fig. 102). Year-over-year
adjusted mean scores rise from 4.37 to 4.39, a smaller increase than we observe in
2016-2017. But with mean scores well above 4.0, indicating "very good" to "excellent,"
this parameter of industry performance is secure and improving as a core competency
of the provider industry.
Figure 102 – Industry performance — integrity: 2014-2018
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
2014 2015 2016 2017 2018
Industry Performance - Integrity: 2014-2018
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Recommended
Industry performance, by the measure of customers willing to recommend, continues a
five-year positive trend grew to another all-time high in 2018 (fig. 103). Year-over-year
scores increase at a faster rate than during the period 2014-2017. “Recommend” ratings
have the highest mean scores of any in our survey, at 4.82, well above "very likely" and
approaching certainty.
Figure 103 – Industry performance — recommended: 2014-2018
4.55
4.60
4.65
4.70
4.75
4.80
4.85
2014 2015 2016 2017 2018
Industry Performance - Recommended: 2014-2018
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Performance Improvements
Another view of overall vendor performance over time shows a recent peak in positive
scores that may signal maturity in features and diminishing urgency for vendor
upgrades and other improvements (fig. 104). Year-over-year performance between
2016 and 2017 is nearly flat, and 2018 measures of “improved” fall very slightly,
nonetheless suggesting that vendors paid suitable attention to their products in a
positive sales climate. At the same time, estimations of declining industry performance
fall to an all-time low of just 4 percent.
Figure 104 – Overall industry performance improvement: 2014-2018
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2014 2015 2016 2017 2018
Overall Industry Performance Improvement: 2014-2018
Improved Stayed the same Declined
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Vendor
Ratings
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Vendor Ratings In this section, we offer ratings of business intelligence software vendors. We rate
vendors using 33 different criteria, on a five-point scale for each. Criteria covers 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 use in scoring the industry and vendors:
• 5.0 = Excellent
• 4.0 = Very good
• 3.0 = Adequate
• 2.0 = Poor
• 1.0 = Very poor
In 2016, we dispensed with market segmentation and now rely upon our Customer
Experience and Vendor Credibility models 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 is replaced (where possible) with a year-over-year comparison for
each vendor.
Based on our scoring methodology, all vendors perform 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.
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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 combine 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 plot customer sentiment surrounding
product, derived from the 12 product and technology measures used to rank vendors.
On the resulting four quadrants, we plot 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.
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Figure 105 – Customer experience model
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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.
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Figure 106 – Vendor credibility model
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Detailed Vendor Ratings In this section, we offer detailed vendor scores. Using our 33-criteria evaluation model
(table 1), we compare each vendor’s performance to its previous year’s performance
and to the average for all vendors (all records in the study population).
The detailed criteria are below. We add “clock” position information to assist in locating
specific scores.
Table 1 - Detailed vendor rating criteria
- Sales/acquisition experience (12 - 2 o’clock)
o Professionalism o Product knowledge o Understanding our
business/needs o Responsiveness o Flexibility/accommodation o Business practices o Contractual terms and
conditions o Follow-up after the sale
- Value for price (3 o’clock)
- Quality and usefulness of product
(3 - 7 o’clock) o Robustness/sophistication of
technology o Completeness of functionality o Reliability of technology o Scalability o Integration of components
within product o Integration with third-party
technologies o Overall usability o Ease of installation o Ease of administration
- Quality and usefulness of product (continued)
o Customization and extensibility
o Ease of upgrade/migration to new versions
o Online forums and documentation
- - Quality of technical support
(8 - 9 o’clock) o Professionalism o Product knowledge o Responsiveness o Continuity of personnel o Time to resolve problems
- Quality and value of consulting
services (9 - 10 o’clock) o Professionalism o Product knowledge o Experience o Continuity o Value
- Integrity (11 o’clock)
- Whether vendor is
recommended (12 o’clock)
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Board Detailed Score
Figure 107 – Board detailed score
In its first year of inclusion, Board is generally above or in line with the overall sample
for most categories of measurement. It is considered a technology leader in the
Customer Experience Model and a trust leader in the Vendor Credibility Model. It has a
perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Board
Board 2018 Overall Sample
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ClearStory Data Detailed Score
Figure 108 – ClearStory Data detailed score
In its first year of inclusion, ClearStory Data is generally above the overall sample for
most measures and is an overall leader in the Customer Experience Model and a trust
leader in the Vendor Credibility Model. It has a perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
ClearStory Data
ClearStory Data 2018 Overall Sample
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Dimensional Insight Detailed Score
Figure 109 – Dimensional Insight detailed score
With increased performance for 2018, Dimensional Insight scores well above the overall
sample and continues to be an overall leader in both the Customer Experience and
Vendor Credibility models. It is best in class for a majority of measures across all
categories of measurement including sales, product, value, and integrity and maintains
a perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Dimensional Insight
Dimensional Insight 2017 Dimensional Insight 2018 Overall Sample
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Domo Detailed Score
Figure 110 – Domo detailed score
With scores generally above the overall sample, Domo’s 2018 scores improve in
several key areas including technical support and consulting. It is ranked as an overall
leader in both the Customer Experience and Vendor Credibility models and is best in
class for product integration with third-party technologies, ease of installation, and ease
of upgrade/migration to new versions. It maintains a perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Domo
Domo 2017 Domo 2018 Overall Sample
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Google Detailed Score
Figure 111 – Google detailed score
In its second year of inclusion, Google has substantial improvements in several areas
including value, technical support, consulting, and product. It is considered a technology
leader in the Customer Experience Model and an overall leader in the Vendor Credibility
Model. It maintains a perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Google 2017 Google 2018 Overall Sample
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IBM Detailed Score
Figure 112 – IBM detailed score
Although its scores are generally below the overall sample, IBM has performance
improvements for 2018 in several categories, but especially in product/technology. This
is offset, however, by declines in technical support and consulting. It is considered a
contender in both Customer Experience and Vendor Credibility models.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
IBM
IBM 2017 IBM 2018 Overall Sample
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Infor Detailed Score
Figure 113 – Infor detailed score
In 2018, Infor/Birst’s scores improve sharply across all categories of measurement. It is
now generally in line with or slightly below the overall sample. It is considered a
contender in the Customer Experience Model and a trust leader in the Vendor
Credibility Model.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Infor/Birst
Infor 2017 Infor 2018 Overall Sample
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Information Builders Detailed Score
Figure 114 – Information Builders detailed score
With scores consistently above the entire sample, Information Builders is an overall
leader in the Customer Experience Model and a trust leader in the Vendor Credibility
Model. In 2018, it has key improvements across most sales, consulting, and technical
support measures and is best in class for sales flexibility/accommodation. It maintains a
perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Information Builders
Information Builders 2017 Information Builders 2018 Overall Sample
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Jedox Detailed Score
Figure 115 – Jedox detailed score
With scores generally above the overall sample, Jedox has key improvements across a
majority of product/technology and sales measures. For 2018, it remains an overall
leader in both the Customer Experience and Vendor Credibility models. It maintains a
perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Jedox
Jedox 2017 Jedox 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC
142
Klipfolio Detailed Score
Figure 116 – Klipfolio detailed score
With scores consistently above the overall sample, Klipfolio is an overall leader in both
the Customer Experience and Vendor Credibility models. It has year-over-year
improvements in sales and product measures.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Klipfolio
Klipfolio 2017 Klipfolio 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC
143
Logi Analytics Detailed Score
Figure 117 – Logi Analytics detailed score
With scores consistently above the overall sample, Logi Analytics is an overall leader in
both the Customer Experience and Vendor Credibility models. In 2018, it has
improvements in several key sales and product measures and technical support time to
resolve problems.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Logi Analytics
Logi Analytics 2017 Logi Analytics 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC
144
Looker Detailed Score
Figure 118 – Looker detailed score
In 2018, Looker continues to be ranked as an overall leader in both the Customer
Experience and Vendor Credibility models. It scores significantly above the overall
sample for virtually all measures and is best in class for sales business practices,
technical support professionalism, and responsiveness. It maintains a perfect
recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Looker
Looker 2017 Looker 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC
145
Microsoft Detailed Score
Figure 119 – Microsoft detailed score
In 2018, Microsoft has key improvements across most measures. Although its scores
remain below the overall sample, Microsoft is now considered a technology leader in the
Customer Experience Model and a trust leader in the Vendor Credibility Model.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Microsoft
Microsoft 2017 Microsoft 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC
146
MicroStrategy Detailed Score
Figure 120 – MicroStrategy detailed score
In 2018, MicroStrategy has sharply improved performance across virtually every
measure in sales, product, value, technical support, and consulting categories. Its
scores are generally above or in line with the overall sample. It is considered a
technology leader in the Customer Experience Model and a trust leader in the Vendor
Credibility Model and has a perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
MicroStrategy
MicroStrategy 2017 MicroStrategy 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC
147
OpenText Detailed Score
Figure 121 – OpenText detailed score
For 2018, OpenText has a substantial drop in performance across all categories of
measurement. Its scores are generally below the overall sample, and it is considered a
contender in both Customer Experience and Vendor Credibility models.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
OpenText
OpenText 2017 OpenText 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC
148
Oracle Detailed Score
Figure 122 – Oracle detailed score
With scores already well below the overall sample, Oracle has a substantial decline
across virtually all measures in 2018. It is an outlier in both Customer Experience and
Vendor Credibility models.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Oracle
Oracle 2017 Oracle 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC
149
Pyramid Analytics Detailed Score
Figure 123 – Pyramid Analytics detailed score
With scores consistently above the overall sample, Pyramid Analytics is an overall
leader in both Customer Experience and Vendor Credibility models. For 2018, it saw
improvements in product Integration of components within product, integration with
third-party technologies, and ease of installation. It maintains a perfect recommend
score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Pyramid Analytics
Pyramid Analytics 2017 Pyramid Analytics 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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150
Qlik Detailed Score
Figure 124 – Qlik detailed score
In 2018, Qlik has key improvements across most categories of measurement including
sales, value, and product. With scores generally above or in line with the overall
sample, Qlik is now considered an overall leader in the Customer Experience Model
and a trust leader in the Vendor Credibility Model.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Qlik
Qlik 2017 Qlik 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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151
RapidMiner Detailed Score
Figure 125 – RapidMiner detailed score
In 2018, RapidMiner once again has across-the-board improvements in all categories of
measurement. With scores well above the overall sample, it is best in class for product
ease of administration and is an overall leader in Customer Experience and Vendor
Credibility models. It has a perfect recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
RapidMiner
RapidMiner 2017 RapidMiner 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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152
Salesforce Detailed Score
Figure 126 – Salesforce detailed score
In 2018, Salesforce has key improvements in sales, value, and product categories of
measurement. Its scores are generally above or in line with the overall sample. It is
considered an overall leader in the Customer Experience Model and a value leader in
the Vendor Credibility Model.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Salesforce
Salesforce 2017 Salesforce 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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153
SAP Detailed Score
Figure 127 – SAP detailed score
Although its scores remain below the overall sample, SAP has improvements in a
number of areas of measurement for 2018 including value, product, technical support,
consulting, integrity, and recommend. It is now considered a contender in the Vendor
Credibility Model.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
SAP
SAP 2017 SAP 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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154
SAS Detailed Score
Figure 128 – SAS detailed score
With scores generally above the overall sample, SAS is best in class for product
completeness of functionality, online training, forums and documentation, consulting
product knowledge, and experience. It is considered an overall leader in the Customer
Experience Model and a trust leader in the Vendor Credibility Model.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
SAS
SAS 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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155
Sisense Detailed Score
Figure 129 – Sisense detailed score
With scores consistently above the overall sample, Sisense is an overall leader for
,Customer Experience and Vendor Credibility models. For 2018 it has improved
performance for value, integrity, understanding customer business/needs, and
contractual terms and conditions.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
SiSense
Sisense 2017 Sisense 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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156
Tableau Software Detailed Score
Figure 130 – Tableau Software detailed score
In 2018, Tableau has improved performance across all categories of measurement
including sales, value, product, technical support, consulting, integrity, and recommend.
With scores generally above the overall sample, Tableau is considered a technology
leader in the Customer Experience Model and a trust leader in the Vendor Credibility
Model.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Tableau Software
Tableau 2017 Tableau 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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157
TIBCO Software Detailed Score
Figure 131 – TIBCO Software detailed score
With scores generally above the overall sample, TIBCO Software has improved
performance in value, product, and recommend measures. It is considered a technology
leader in the Customer Experience Model and an overall leader in the Vendor Credibility
Model.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
TIBCO Software
TIBCO 2017 TIBCO 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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158
Yellowfin Detailed Score
Figure 132 – Yellowfin detailed score
With scores generally above the overall sample, Yellowfin is an overall leader in the
Customer Experience and Vendor Credibility models. In 2018, it has improvements in
several categories of measurement including sales, product, technical support, and
consulting services.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Yellowfin
Yellowfin 2017 Yellowfin 2018 Overall Sample
2018 Wisdom of Crowds® Business Intelligence Market Study
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159
Zoomdata Detailed Score
Figure 133 – Zoomdata detailed score
With scores consistently above the overall sample, in 2018, Zoomdata has
improvements for a number of key sales measures including sales responsiveness,
professionalism, and follow-up after the sale. It remains an overall leader in the
Customer Experience and Vendor Credibility models and maintains a perfect
recommend score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Sales: professionalism
Product knowledgeUnderstanding our…
Responsiveness
Flexibility/accommodation
Business practices
Contractual terms and…
Follow up after the sale
Value
Product: robustness/…
Completeness of…
Reliability of technology
Scalability
Integration of…
Integration with third-…Overall usability
Ease of installationEase of administrationCustomization and…
Ease of…
Online training, forums…
Support: professionalism
Product knowledge
Responsiveness
Continuity of personnel
Time to resolve problems
Consult: professionalism
Product knowledge
Experience
Continuity
Value
IntegrityRecommend
Zoomdata
Zoomdata 2017 Zoomdata 2018 Overall Sample
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160
Other Dresner Advisory Services Research Reports
- Advanced and Predictive Analytics
- Analytical Data Infrastructure
- Big Data Analytics
- Business Intelligence Competency Center
- Cloud Computing and Business Intelligence
- Collective Insights®
- Data Catalog
- Embedded Business Intelligence
- End User Data Preparation
- Enterprise Planning
- IoT Intelligence®
- Location Intelligence
- Small and Mid-Sized Enterprise Business Intelligence
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Dresner Advisory Services - 2018 Wisdom of Crowds Survey Instrument
Please enter your contact information below
First Name*: _________________________________________________
Last Name*: _________________________________________________
Title: _________________________________________________
Company Name*: _________________________________________________
Street Address: _________________________________________________
City: _________________________________________________
State: _________________________________________________
Zip: _________________________________________________
Country: _________________________________________________
Email Address*: _________________________________________________
Phone Number: _________________________________________________
URL: _________________________________________________
May we contact you to discuss your responses and for additional information?
( ) Yes ( ) No
What major geography do you reside in?*
( ) North America
( ) Europe, Middle East and Africa
( ) Latin America
( ) Asia Pacific
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Please identify your primary industry*
( ) Advertising
( ) Aerospace
( ) Agriculture
( ) Apparel & Accessories
( ) Automotive
( ) Aviation
( ) Biotechnology
( ) Broadcasting
( ) Business Services
( ) Chemical
( ) Construction
( ) Consulting
( ) Consumer Products
( ) Defense
( ) Distribution & Logistics
( ) Education (Higher Ed)
( ) Education (K-12)
( ) Energy
( ) Entertainment and Leisure
( ) Executive search
( ) Federal Government
( ) Financial Services
( ) Food, Beverage and Tobacco
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( ) Healthcare
( ) Hospitality
( ) Insurance
( ) Legal
( ) Manufacturing
( ) Mining
( ) Motion Picture and Video
( ) Not for Profit
( ) Pharmaceuticals
( ) Publishing
( ) Real estate
( ) Retail and Wholesale
( ) Sports
( ) State and Local Government
( ) Technology
( ) Telecommunications
( ) Transportation
( ) Utilities
( ) Other - Please specify below
Please type in your industry
_________________________________________________
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How many employees does your company employ worldwide?
( ) 1-100
( ) 101-1,000
( ) 1,001-2,000
( ) 2,001-5,000
( ) 5,001-10,000
( ) More than 10,000
What function do you report into?*
( ) Business Intelligence Competency Center
( ) Executive Management
( ) Finance
( ) Human Resources
( ) Information Technology (IT)
( ) Marketing
( ) Operations (e.g., Manufacturing, Supply Chain, Services)
( ) Research and Development (R&D)
( ) Sales
( ) Strategic Planning Function
( ) Other - Write In
Please specify the function that you report to:
_________________________________________________
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9) Does your organization have a Chief Data Officer or Chief Analytics Officer in place?
For
less
than
1
year
1 -3
years
3 - 5
years
More
than
5
years
Don't
have
one
Chief
Data
Officer
(CDO)
( ) ( ) ( ) ( ) ( )
Chief
Analytics
Officer
(CAO)
( ) ( ) ( ) ( ) ( )
Do you anticipate having a CDO or CAO in the future?
No
plans
This
Year
Next
Year
Distant
Future
Chief
Data
Officer
(CDO)
( ) ( ) ( ) ( )
Chief
Analytics
Officer
(CAO)
( ) ( ) ( ) ( )
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What roles do the CDO or CAO report to?
CEO CFO CMO CIO Other
Chief
Data
Officer
(CDO)
( ) ( ) ( ) ( ) ( )
Chief
Analytics
Officer
(CAO)
( ) ( ) ( ) ( ) ( )
What role does your CDO report into?
_________________________________________________
What role does your CAO report in to?
_________________________________________________
How effective has the Chief Data Officer been within your organization?
0 ________________________[__]_____________________________ 100
How effective has the Chief Analytics Officer been within your organization?
0 ________________________[__]_____________________________ 100
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Please respond to the following statement: "My organization considers our business
intelligence initiatives a success."
( ) Completely agree
( ) Agree somewhat
( ) Disagree somewhat
( ) Disagree
What has been the key to your success with business intelligence?
____________________________________________
____________________________________________
____________________________________________
____________________________________________
How do you determine BI success?
[ ] Return on investment (ROI) model
[ ] User feedback/satisfaction
[ ] Customer feedback/satisfaction
[ ] Numbers of deployed users
[ ] System/application activity
[ ] Other - Write In: _________________________________________________
[ ] Other - Write In: _________________________________________________
What have been the obstacles to success with business intelligence?
____________________________________________
____________________________________________
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How do you measure failure with BI?
[ ] User feedback
[ ] Cost/Return on investment
[ ] Limited adoption
[ ] Lack of usage
[ ] Other - Write In: _________________________________________________
[ ] Other - Write In: _________________________________________________
This year our budget for business intelligence / analytics is:
( ) Increasing over last year
( ) Decreasing over last year
( ) Staying the same as last year
Percentage of BI/analytics budget spent on new software purchases
0 ________________________[__]_____________________________ 100
Percentage of BI/analytics budget spent on software maintenance
0 ________________________[__]_____________________________ 100
Percentage of BI/analytics budget spent on software subscription services
0 ________________________[__]_____________________________ 100
Percentage of BI/analytics budget spent on external consulting services
0 ________________________[__]_____________________________ 100
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Which function drives your business intelligence initiatives?
Always Often Sometimes Rarely Never
Operations ( ) ( ) ( ) ( ) ( )
Competency
Center/Center
of Excellence
( ) ( ) ( ) ( ) ( )
Sales ( ) ( ) ( ) ( ) ( )
Finance ( ) ( ) ( ) ( ) ( )
Research and
Development
(R&D)
( ) ( ) ( ) ( ) ( )
Information
Technology
(IT)
( ) ( ) ( ) ( ) ( )
Human
Resources
( ) ( ) ( ) ( ) ( )
Executive
Management
( ) ( ) ( ) ( ) ( )
Marketing ( ) ( ) ( ) ( ) ( )
Manufacturing ( ) ( ) ( ) ( ) ( )
Strategic
Planning
Function
( ) ( ) ( ) ( ) ( )
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Where has business intelligence helped to achieve business goals?
High
Achievement
Moderate
Achievement
Acceptable
Achievement
Not Yet
Attempted
Not Yet
Achieved
Better
decision-
making
( ) ( ) ( ) ( ) ( )
Compliance /
Risk
Management
( ) ( ) ( ) ( ) ( )
Growth in
revenues
( ) ( ) ( ) ( ) ( )
Improved
operational
efficiency/cost
savings
( ) ( ) ( ) ( ) ( )
Enhanced
customer
service
( ) ( ) ( ) ( ) ( )
Increased
competitive
advantage
( ) ( ) ( ) ( ) ( )
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What does your organization expect to achieve with business intelligence?
Critical
Very
important Important
Somewhat
important Unimportant
Better
decision-
making
( ) ( ) ( ) ( ) ( )
Compliance /
Risk
Management
( ) ( ) ( ) ( ) ( )
Growth in
revenues
( ) ( ) ( ) ( ) ( )
Improved
operational
efficiency/cost
savings
( ) ( ) ( ) ( ) ( )
Enhanced
customer
service
( ) ( ) ( ) ( ) ( )
Increased
competitive
advantage
( ) ( ) ( ) ( ) ( )
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Who are the targeted consumers of business intelligence within your organization?
Primary Secondary
Future
plans No plans
Customers ( ) ( ) ( ) ( )
Executives ( ) ( ) ( ) ( )
Individual
Contributors and
Professionals
( ) ( ) ( ) ( )
Line Managers ( ) ( ) ( ) ( )
Middle Managers ( ) ( ) ( ) ( )
Partners / Affliates ( ) ( ) ( ) ( )
Suppliers ( ) ( ) ( ) ( )
What percentage of all employees have access to business intelligence solutions?
Under
10%
11 -
20%
21 -
40%
41 -
60%
61 -
80%
81% or
more
Today ( ) ( ) ( ) ( ) ( ) ( )
In 12
months
( ) ( ) ( ) ( ) ( ) ( )
In 24
months
( ) ( ) ( ) ( ) ( ) ( )
In 36
months
( ) ( ) ( ) ( ) ( ) ( )
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Please choose one of the following to describe the state of data governance in your
organization.
( ) 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, information views and
semantics are manipulated 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
How do people in your organization take advantage of insights learned from business
intelligence solutions?
All of the
time
Most of
the time
Some of
the time Rarely
“Closed loop” -
Information is shared,
teams work to process it
and act in a timely
fashion. No formal
boundaries
( ) ( ) ( ) ( )
Ad hoc (informal) action
on insights across
functions
( ) ( ) ( ) ( )
Uncoordinated/ self-
serving action (sometimes
at the expense of others)
( ) ( ) ( ) ( )
Insights are under-
leveraged
( ) ( ) ( ) ( )
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How many business intelligence products are currently used in your organization today?
( ) Don't know
( ) 1
( ) 2
( ) 3
( ) 4
( ) 5
( ) 6
( ) 7
( ) 8
( ) 9
( ) 10 or more
Please indicate the importance of the following technologies to your strategy and plans.
Critical
Very
important Important
Somewhat
important
Not
important
Ability to
write to
transactional
applications
( ) ( ) ( ) ( ) ( )
Advanced
visualization
( ) ( ) ( ) ( ) ( )
Big Data
(e.g.,
Hadoop)
( ) ( ) ( ) ( ) ( )
Cognitive BI
(e.g.,
( ) ( ) ( ) ( ) ( )
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Artificial
Intelligence-
based BI)
Collaborative
support for
group-based
analysis
( ) ( ) ( ) ( ) ( )
Complex
event
processing
(CEP)
( ) ( ) ( ) ( ) ( )
Dashboards ( ) ( ) ( ) ( ) ( )
Data catalog ( ) ( ) ( ) ( ) ( )
Data
discovery
( ) ( ) ( ) ( ) ( )
Data mining,
advanced
algorithms,
predictive
( ) ( ) ( ) ( ) ( )
Data
storytelling
( ) ( ) ( ) ( ) ( )
Data
warehousing
( ) ( ) ( ) ( ) ( )
Edge
computing
( ) ( ) ( ) ( ) ( )
Embedded BI
(contained
within an
application,
portal, etc.)
( ) ( ) ( ) ( ) ( )
End-user ( ) ( ) ( ) ( ) ( )
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"self-service"
End-user data
preparation
and blending
( ) ( ) ( ) ( ) ( )
Enterprise
planning /
budgeting
( ) ( ) ( ) ( ) ( )
GDPR
(General Data
Protection
Regulation)
( ) ( ) ( ) ( ) ( )
Governance ( ) ( ) ( ) ( ) ( )
In-memory
analysis
( ) ( ) ( ) ( ) ( )
Integration
with
operational
processes
( ) ( ) ( ) ( ) ( )
Internet of
Things (IoT)
( ) ( ) ( ) ( ) ( )
IT Analytics ( ) ( ) ( ) ( ) ( )
Location
intelligence /
analytics
( ) ( ) ( ) ( ) ( )
Mobile
device
support
( ) ( ) ( ) ( ) ( )
Natural
language
analytics
(natural
( ) ( ) ( ) ( ) ( )
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language
query/ natural
language
generation)
Open source
software
( ) ( ) ( ) ( ) ( )
Prepackaged
vertical /
functional
analytical
applications
( ) ( ) ( ) ( ) ( )
Reporting ( ) ( ) ( ) ( ) ( )
Sales
Planning
( ) ( ) ( ) ( ) ( )
Search-based
interface
( ) ( ) ( ) ( ) ( )
Social media
analysis
(Social BI)
( ) ( ) ( ) ( ) ( )
Software-as-
a-Service and
cloud
computing
( ) ( ) ( ) ( ) ( )
Streaming
data analysis
( ) ( ) ( ) ( ) ( )
Text analytics ( ) ( ) ( ) ( ) ( )
Video
analytics
( ) ( ) ( ) ( ) ( )
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179
What is your level of investment in IoT technologies?
( ) Investing today
( ) Planning for this year
( ) Planning for next year
( ) We have no plans
Which types of IoT investments are most/least important to your organization?
Critical
Very
important Important
Somewhat
important
Not
important
Investment
in IoT
Infrastructure
(hardware,
signals,
processes)
( ) ( ) ( ) ( ) ( )
Investment
in Data
Supply
Chain
(capture,
movement,
prep,
management,
etc.)
( ) ( ) ( ) ( ) ( )
Investment
in IoT
Analytics
Application
development
(Attributes
and Metrics
etc.)
( ) ( ) ( ) ( ) ( )
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Please select one vendor to rate. You will have an opportunity to rate a second vendor
at the end of this section.
( ) 1010data
( ) Adaptive Insights
( ) ADVIZOR Solutions
( ) Alteryx
( ) Amazon (i.e., QuickSight)
( ) AnswerRocket
( ) Arcadia Data
( ) arcplan (Longview)
( ) BIME (Zendesk)
( ) Board
( ) ClearStory Data
( ) Cubeware
( ) Datameer
( ) DataRPM
( ) Datawatch (Panopticon)
( ) Dimensional Insight
( ) Domo
( ) Dundas
( ) FICO
( ) GoodData
( ) Google Analytics
( ) IBM
( ) iDashboards
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( ) InetSoft
( ) Infor (Birst)
( ) Information Builders (IBI)
( ) Izenda
( ) Jedox
( ) Jinfonet/JReport
( ) Klipfolio
( ) KNIME
( ) Lavastorm
( ) Logi Analytics
( ) Looker
( ) Microsoft
( ) MicroStrategy
( ) Narrative Science
( ) OpenText (Actuate)
( ) Oracle
( ) Panorama
( ) Pentaho (Hitachi Vantara)
( ) Phocas
( ) Pyramid Analytics
( ) Qlik
( ) RapidMiner
( ) Salesforce.com
( ) SAP
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( ) SAS Institute
( ) Sisense
( ) Solver
( ) Tableau
( ) TARGIT
( ) ThoughtSpot
( ) TIBCO (Spotfire, Statistica, Alpine Data, Jaspersoft)
( ) Yellowfin
( ) Yseop
( ) Zoomdata
( ) Other - Write In: _________________________________________________
Please specify the product name and version for the selected vendor
_________________________________________________
How long has this product been in use in your organization?
( ) Less than 1 year
( ) 1 - 2 years
( ) 3 - 5 years
( ) 6 - 10 years
( ) More than 10 years
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Did this product replace another BI product? If so, which one?
Did this product replace another BI product?
( ) Yes ( ) No
Which product did it replace?
_________________________________________________
Why was it replaced?
Primary
Reason
Secondary
Reason
Was
Not a
factor
Cost ( ) ( ) ( )
Functionality ( ) ( ) ( )
Corporate
standard
( ) ( ) ( )
Modernization ( ) ( ) ( )
How many users currently use this product?
( ) 1-10
( ) 11-50
( ) 51-100
( ) 101-200
( ) 201-500
( ) More than 500
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How would you characterize the sales/acquisition experience with this vendor?
Excellent
Very
good Adequate Poor
Very
poor
Don't
know
Professionalism ( ) ( ) ( ) ( ) ( ) ( )
Product knowledge ( ) ( ) ( ) ( ) ( ) ( )
Understanding our
business needs
( ) ( ) ( ) ( ) ( ) ( )
Responsiveness ( ) ( ) ( ) ( ) ( ) ( )
Flexibility/accommodation ( ) ( ) ( ) ( ) ( ) ( )
Business practices ( ) ( ) ( ) ( ) ( ) ( )
Contractual terms and
conditions
( ) ( ) ( ) ( ) ( ) ( )
Follow-up after the sale ( ) ( ) ( ) ( ) ( ) ( )
How would you characterize the value for the price paid?
( ) Great value (Well exceeded expectations)
( ) Good value (Somewhat exceeded expectations)
( ) Average value (Met expectations)
( ) Poor value (Fell short of expectations)
( ) Very poor value (Fell far short of expectations)
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How would you characterize the quality and usefulness of the product?
Excellent
Very
good Adequate Poor
Very
poor
Don't
know
Robustness/sophistication
of technology
( ) ( ) ( ) ( ) ( ) ( )
Completeness of
functionality
( ) ( ) ( ) ( ) ( ) ( )
Reliability of technology ( ) ( ) ( ) ( ) ( ) ( )
Scalability ( ) ( ) ( ) ( ) ( ) ( )
Integration of
components within
product
( ) ( ) ( ) ( ) ( ) ( )
Integration with third-
party technologies
( ) ( ) ( ) ( ) ( ) ( )
Overall usability ( ) ( ) ( ) ( ) ( ) ( )
Ease of installation ( ) ( ) ( ) ( ) ( ) ( )
Ease of administration ( ) ( ) ( ) ( ) ( ) ( )
Customization and
extensibility
( ) ( ) ( ) ( ) ( ) ( )
Ease of
upgrade/migration to new
versions
( ) ( ) ( ) ( ) ( ) ( )
Online training, forums
and documentation
( ) ( ) ( ) ( ) ( ) ( )
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How would you characterize the vendor's technical support?
Excellent
Very
good Adequate Poor
Very
poor
Don't
know
Professionalism ( ) ( ) ( ) ( ) ( ) ( )
Product
knowledge
( ) ( ) ( ) ( ) ( ) ( )
Responsiveness ( ) ( ) ( ) ( ) ( ) ( )
Continuity of
personnel
( ) ( ) ( ) ( ) ( ) ( )
Time to resolve
problems
( ) ( ) ( ) ( ) ( ) ( )
How would you characterize the vendor's consulting services?
Excellent
Very
good Adequate Poor
Very
poor
Don't
know
Professionalism ( ) ( ) ( ) ( ) ( ) ( )
Product
knowledge
( ) ( ) ( ) ( ) ( ) ( )
Experience ( ) ( ) ( ) ( ) ( ) ( )
Continuity ( ) ( ) ( ) ( ) ( ) ( )
Value ( ) ( ) ( ) ( ) ( ) ( )
Availability of
resources
( ) ( ) ( ) ( ) ( ) ( )
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How would you rate the integrity (i.e., truthfulness, honesty) of this BI vendor?
( ) Excellent
( ) Very good
( ) Adequate
( ) Poor
( ) Very poor
( ) Don't know
Did your experience with this vendor improve, remain the same or decline from last
year?
( ) Improved
( ) Stayed the same
( ) Declined
Would you recommend this vendor/product?
( ) I would recommend this vendor/product
( ) I would NOT recommend this vendor/product
Please enter any additional comments regarding this vendor and/or its products
____________________________________________
____________________________________________
____________________________________________
____________________________________________