Wisdom of Crowds Research

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

http://www.dresneradvisory.com Copyright 2018 – Dresner Advisory Services, LLC

2

Disclaimer

This report should be used for informational purposes only. Vendor and product selections should be made

based on multiple information sources, face-to-face meetings, customer reference checking, product

demonstrations, and proof-of-concept applications.

The information contained in all Wisdom of Crowds® Market Study Reports reflects the opinions expressed in

the online responses of individuals who chose to respond to our online questionnaire and does not represent

a scientific sampling of any kind. Dresner Advisory Services, LLC shall not be liable for the content of

reports, study results, or for any damages incurred or alleged to be incurred by any of the companies

included in the reports as a result of the content.

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

http://www.dresneradvisory.com/



3

Business Intelligence: A Definition Business intelligence (BI) is “knowledge gained through the access and analysis of

business information.

Business intelligence tools and technologies include query and reporting, OLAP (online

analytical processing), data mining and advanced analytics, end-user tools for ad hoc

query and analysis, and dashboards for performance monitoring.”

Howard Dresner, The Performance Management Revolution: Business Results Through

Insight and Action (John Wiley & Sons, 2007).




4

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


http://www.realbusinessintelligence.com/



5

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




6

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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12

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




13

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.


http://advancedanalyticsreport.com/

http://www.analyticaldatainfrastructure.com/

http://www.biccreport.com/

http://www.cloudbi.report/

http://collectiveinsights.report/

http://collectiveinsights.report/

http://www.embeddedbireport.com/

http://www.enduserdataprep.com/

http://www.iotintelligence.report/

http://www.locationintelligence.report/

http://www.twitter.com/howarddresner




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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.




15

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




16

Executive

Summary




17

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…)




19

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




20

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




21

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




22

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




23

Analysis and

Trends




24

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




25

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




26

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%



Finance

Operations

Manufacturing

Research and Development (R&D)


Sales

Competency Center/Center of Excellence

Marketing

Human Resources

Change in Functions Driving BI 2017-2018




27

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




28

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



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




29

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




30

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




31

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

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Executives MiddleManagers

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Professionals

Line Managers Customers Suppliers

Targeted Users for Business Intelligence 2013-2018

2013 2014 2015 2016 2017 2018




32

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

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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




33

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

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1-100 101-1,000 1,001-10,000 More than 10,000

Targeted Users by Organization Size




Suppliers




34

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

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Targeted Users by Industry

Executives Individual Contributors and ProfessionalsMiddle Managers Line ManagersCustomers Partners / AffliatesSuppliers




35

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




36

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

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ImprovedOperational

Efficiency

Growth inRevenues

IncreasedCompetitiveAdvantage

EnhancedCustomer

Service

Compliance /Risk

Management

Business Intelligence Objectives 2014-2018

2014 2015 2016 2017 2018




37

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%


Improved Operational Efficiency /Cost Savings

Increased Competitive Advantage

Growth in Revenues


Percent Change in BI Objectives 2017-2018




38

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

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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




39

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


Finance

Information Technology(IT)

Operations (e.g.,Manufacturing, Supply

Chain, Services)

Research and Development(R&D)

Sales & Marketing

Business Intelligence Objectives by Function







40

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





41

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

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Business Intelligence Objectives by Organization Size







42

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


Growth inRevenues

EnhancedCustomer

Service

ImprovedOperationalEfficiency /…


Business Intelligence Achievements

High Achievement Moderate Achievement Acceptable Achievement

Not Yet Attempted Not Yet Achieved




43

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

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Compliance / RiskManagement

Business Intelligence Achievements by Organization Size

High Achievement Moderate Achievement Acceptable Achievement Not Yet Attempted




44

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%

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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




45

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




46

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




47

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

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Asia Pacific

Penetration of Business Intelligence Solutions Today by Geography

81% or more

61 - 80%

41 - 60%

21 - 40%

11 - 20%

Under 10%




48

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

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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%




49

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

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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




50

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

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Sales &Marketing

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Services)

Expansion Plans for Business Intelligence through 2021 by Function

Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more




51

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%

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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




52

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

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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




53

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

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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%




54

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

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Expansion Plans for Business Intelligence through 2021 by Organization Size

81% or more

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Under 10%




55

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




56

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




57

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




58

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

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Lessthan 1

Year - 5+Years

Don'thave one

Lessthan 1

Year - 5+Years

Don'thave one

Lessthan 1

Year - 5+Years


ImprovedOperational



Growth inRevenues



Business Intelligence Achievements by Presence of CDO





59

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


ImprovedOperational



Growth inRevenues



Business Intelligence Achievements by Presence of CAO





60

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




61

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

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(H

igh

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Fin

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s

Insu

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Ret

ail a

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sale

Hea

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care

Tech

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Edu

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(H

igh

er E

d)

Man

ufa

ctu

rin

g

Bu

sin

ess

Serv

ices


Enterprises with Chief Data or Chief Analytics Officers by Industry





62

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


Enterprises with Chief Data or Chief Analytics Officers by Organization Size





63

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 and Chief Analytics Officer Reporting Structure

CEO CFO CMO CIO Other




64

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


SomewhatSuccessful



Chief Data and Chief Analytics Officer Reporting Structure by Success with BI





65

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




66

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




67

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%


(R&D)



Center

Finance Operations(e.g.,


Services)

Sales &Marketing

ExecutiveManagement

Number of Business Intelligence Tools in Use by Function





68

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





69

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





70

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




71

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

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ing

Das

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Technology Priorities 2015-2018

2015 2016 2017 2018




72

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


End-user "self-service"

Collaborative support for group-based analysis


Data warehousing


Data discovery

Embedded BI (contained within an application, portal, etc.)

Data mining, advanced algorithms, predictive

End-user data preparation and blending



Data storytelling

Governance

In-memory analysis

Prepackaged vertical / functional analytical applications

Data catalog






Ability to write to transactional applications

Cognitive BI (e.g., Artificial Intelligence-based BI)


Natural language analytics (natural language query/ natural language…

Text analytics



Video analytics

Edge computing

Change in Technology Priorities 2017-2018




73

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"


Data warehousing

Data discovery

Data mining, advanced algorithms,…

Data storytelling



Sales Planning


Governance

Software-as-a-Service and cloud…

Data catalog


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



In-memory analysis



Text analytics

Natural language analytics (natural…


Prepackaged vertical / functional…




Edge computingVideo analytics

Technologies and Initiatives Strategic to Business Intelligence Objectives by Geography

Asia Pacific Latin America North America Europe, Middle East and Africa




74

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



Data warehousing

Data discovery


Data storytelling



Sales Planning

Governance




Data catalog

End-user data preparation and blendingGDPR (General Data Protection…

Ability to write to transactional…Collaborative support for group-based…

IT Analytics




In-memory analysis



Text 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)




75

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



Data warehousing

Data discovery

Data mining, advanced algorithms,predictive

Data storytelling

Sales Planning

Governance




Data catalog

Embedded BI (contained within anapplication, portal, etc.)


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



In-memory analysis


Text analytics


Natural language analytics (naturallanguage query/ natural language…


Prepackaged vertical / functional analyticalapplications





Technologies and Initiatives Strategic to Business Intelligence Objectives by Industry

Insurance Financial Services Business Services Technology

Healthcare Manufacturing Retail and Wholesale Education (Higher Ed)




76

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



Data warehousing

Data discovery

Data mining, advanced algorithms,predictive

Data storytelling



Sales Planning


Governance


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



In-memory analysis


Text analytics


Natural language analytics (natural languagequery/ natural language generation)


Prepackaged vertical / functional analyticalapplications





Technologies and Initiatives Strategic to Business Intelligence Objectives by Organization Size

1-100 101-1,000 1,001-10,000 More than 10,000




77

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




78

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




79

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








80

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




81

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


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




82

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

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Business Intelligence and the State of Databy Organization Size


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




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




84

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

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Business Intelligence and Action on Insightby Geography

Rarely Some of the time Most of the time All of the time




85

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%

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Business Intelligence and Action on Insightby Function





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

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Business Intelligence and Action on Insightby Industry





87

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

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Business Intelligence and Action on Insight by Organization Size





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

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Completely Successful Somewhat Successful Somewhat Unsuccessful Unsuccessful




89

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

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Change in Success with Business Intelligence2017-2018




90

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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Measures of Success with Business Intelligence




91

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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92

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





93

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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Improved OperationalEfficiency / Cost Savings

Growth in Revenues

Increased CompetitiveAdvantage



Business Intelligence Objectives by Success with BI





94

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




Suppliers




95

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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96

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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Technologies and Initiatives Strategic to Business Intelligence by BI Success





97

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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Number of Business Intelligence Tools in Use by Success with BI





98

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








99

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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Business Intelligence and Action on Insight by BI Success





100

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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101

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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Business Intelligence Achievements by Success with BI





102


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

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Data warehousing

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Governance


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103

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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104

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





105

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





106

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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Center

Finance Sales &Marketing

ExecutiveManagement

Operations(e.g.,


Services)


(R&D)


Budget Plans for Business Intelligenceby Function





107

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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Insurance Healthcare Manufacturing FinancialServices

BusinessServices

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Budget Plans for Business Intelligenceby Industry





108

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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1-100 101-1,000 1,001-10,000 More than 10,000

Budget Plans for Business Intelligenceby Organization Size





109

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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Increasing over last year Staying the same as lastyear

Decreasing over last year

Budget Plans by Penetration of Business Intelligence Solutions

81% or more

61 - 80%

41 - 60%

21 - 40%

11 - 20%

Under 10%




110

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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Increasing over last year Staying the same as lastyear

Decreasing over last year

Expansion Plans for Business Intelligence through 2021 by BI Budget Plans

81% or more

61 - 80%

41 - 60%

21 - 40%

11 - 20%

Under 10%




111

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





112

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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efficiency/costsavings

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Growth inrevenues

Increasedcompetitiveadvantage


Business Intelligence Achievements by BI Budget Plans





113


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



Data warehousing

Data discovery


Data storytelling


Governance


Sales Planning

Data catalog





Ability to write to transactional…Collaborative support for group-based…Location intelligence / analytics


IT Analytics

In-memory analysis



Text analytics














114

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




115

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

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Functionality Modernization Cost Corporate standard

Reasons for BI Product Replacement

Was Not a Factor

Secondary Reason

Primary Reason




116

Industry and

Vendor

Analysis




117

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.




118

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




119

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




120

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




121

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




122

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

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Professionalism Productknowledge

Experience Continuity Value

Industry Performance - BI Vendor Consulting 2014-2018

2011

2012

2013

2014

2015

2016

2017

2018




123

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




124

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




125

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

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Overall Industry Performance Improvement: 2014-2018

Improved Stayed the same Declined




126

Vendor

Ratings




127

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.




128

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.




129

Figure 105 – Customer experience model




130

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.




131

Figure 106 – Vendor credibility model




132

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)




133

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…


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




134

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

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2.0

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Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

ClearStory Data

ClearStory Data 2018 Overall Sample




135

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

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2.0

2.5

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Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Dimensional Insight

Dimensional Insight 2017 Dimensional Insight 2018 Overall Sample




136

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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Domo

Domo 2017 Domo 2018 Overall Sample




137

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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Google

Google 2017 Google 2018 Overall Sample




138

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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

IBM

IBM 2017 IBM 2018 Overall Sample




139

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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Infor/Birst

Infor 2017 Infor 2018 Overall Sample




140

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


0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Information Builders

Information Builders 2017 Information Builders 2018 Overall Sample




141

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


0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Jedox

Jedox 2017 Jedox 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Klipfolio

Klipfolio 2017 Klipfolio 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Logi Analytics

Logi Analytics 2017 Logi Analytics 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Looker

Looker 2017 Looker 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Microsoft

Microsoft 2017 Microsoft 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

MicroStrategy

MicroStrategy 2017 MicroStrategy 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

OpenText

OpenText 2017 OpenText 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Oracle

Oracle 2017 Oracle 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Pyramid Analytics

Pyramid Analytics 2017 Pyramid Analytics 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Qlik

Qlik 2017 Qlik 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

RapidMiner

RapidMiner 2017 RapidMiner 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Salesforce

Salesforce 2017 Salesforce 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

SAP

SAP 2017 SAP 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

SAS

SAS 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

SiSense

Sisense 2017 Sisense 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Tableau Software

Tableau 2017 Tableau 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

TIBCO Software

TIBCO 2017 TIBCO 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Yellowfin

Yellowfin 2017 Yellowfin 2018 Overall Sample




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



Responsiveness


Business practices



Value


Completeness of…


Scalability

Integration of…



Ease of…



Product knowledge

Responsiveness




Product knowledge

Experience

Continuity

Value

IntegrityRecommend

Zoomdata

Zoomdata 2017 Zoomdata 2018 Overall Sample




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


http://www.predictiveanalyticsreport.com/

http://www.analyticaldatainfrastructure.com/

http://www.bigdataanalyticsreport.com/

http://www.biccreport.com/

http://www.cloudbi.report/

http://www.collectiveinsights.report/

http://www.datacatalog.report/

http://www.embeddedbireport.com/

http://www.enduserdataprep.com/

http://www.enterpriseplanningreport.com/

http://www.iotintelligence.report/

http://www.locationintelligence.report/

http://www.smebireport.com/



161

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




162

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




163

( ) 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

_________________________________________________




164

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:

_________________________________________________




165

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)

( ) ( ) ( ) ( )




166

What roles do the CDO or CAO report to?


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




167

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?

____________________________________________

____________________________________________




168

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




169

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

( ) ( ) ( ) ( ) ( )




170

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

( ) ( ) ( ) ( ) ( )




171

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

( ) ( ) ( ) ( ) ( )




172

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

( ) ( ) ( ) ( ) ( ) ( )




173

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

( ) ( ) ( ) ( )




174




175

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.,

( ) ( ) ( ) ( ) ( )




176

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 ( ) ( ) ( ) ( ) ( )




177

"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

( ) ( ) ( ) ( ) ( )




178

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

( ) ( ) ( ) ( ) ( )




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.)

( ) ( ) ( ) ( ) ( )




180

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




181

( ) 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




182

( ) 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




183

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




184

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)




185

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

( ) ( ) ( ) ( ) ( ) ( )




186

How would you characterize the vendor's technical support?

Excellent

Very

good Adequate Poor

Very

poor

Don't

know


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


Product

knowledge

( ) ( ) ( ) ( ) ( ) ( )

Experience ( ) ( ) ( ) ( ) ( ) ( )

Continuity ( ) ( ) ( ) ( ) ( ) ( )

Value ( ) ( ) ( ) ( ) ( ) ( )

Availability of

resources

( ) ( ) ( ) ( ) ( ) ( )




187

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

____________________________________________

____________________________________________

____________________________________________

____________________________________________


Wisdom of Crowds Research

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