the bi survey 17 – the results - cubus...after data cleansing and removing responses from...

The BI Survey 17 – The Results

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The Results

This report features an overview and analysis of the

most important findings from The BI Survey 17


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Table of Contents

Introduction ...............................................................................................................................................4

Business Benefits .....................................................................................................................................7

Levels of Achievement ..........................................................................................................................8

Revenue Growth ...................................................................................................................................8

Cost Reduction......................................................................................................................................9

Critical Reporting................................................................................................................................ 10

Performance Improvement ................................................................................................................ 10

A Note on Measurement .................................................................................................................... 10

A Note on Risk and Failure ................................................................................................................ 11

The Business Benefits Index (BBI) .................................................................................................... 12

Industry Achievement of the Business Benefits Index ....................................................................... 14

Satisfaction with the BI Project Experience ....................................................................................... 15

Project Success Surveyed by Product ............................................................................................... 16

Deployment............................................................................................................................................ 18

BI Product Penetration by Product..................................................................................................... 18

BI Product Penetration by Region...................................................................................................... 21

BI Broadens its Audience ................................................................................................................... 21

The Selection Process ........................................................................................................................... 23

Drivers for Investing in a Product Selection Process ......................................................................... 23

Selection Methods ............................................................................................................................. 25

Reasons for Purchase ....................................................................................................................... 26

Plans for Employing BI Products ....................................................................................................... 27

Products Evaluated ............................................................................................................................ 28

Key Resources in the Evaluation Process ......................................................................................... 29

Products Acquired Post Evaluation.................................................................................................... 30

Implementation, Support and Challenges ............................................................................................. 31

Support Quality .................................................................................................................................. 33

Implementation Time ......................................................................................................................... 34

BI Trends ............................................................................................................................................... 36

Regional Perspective ............................................................................................................................. 42

Penetration Rates .............................................................................................................................. 42

Predictive Analytics ............................................................................................................................ 43

Cloud Feasibility by Region ............................................................................................................... 44

Trends in Use by Region ................................................................................................................... 44

Amount of Data Used with BI ............................................................................................................. 45

Usage Problems................................................................................................................................. 46


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Implementation Problems .................................................................................................................. 46

Reasons to Purchase ......................................................................................................................... 47

Use With Data Sources ......................................................................................................................... 49

Front-end Products for Microsoft SQL Server Analysis Services (SSAS) ......................................... 49

Front-end Products for IBM Cognos TM1 .......................................................................................... 49

Front-end Products for Oracle Essbase ............................................................................................ 50

Front-end Products for SAP BW ........................................................................................................ 51

Amount of Data in Databases for BI ...................................................................................................... 52

Data Volume by Industry .................................................................................................................... 53

Usage of BI Across Departments by Region ..................................................................................... 53

Product-based Research Findings and Analysis ................................................................................... 55

Products by Region ............................................................................................................................ 55

Industry Sectors Represented by Product ......................................................................................... 56

Trends in Use by Product .................................................................................................................. 58

Median Data Volume (GB) in the Databases Used by BI Product .................................................... 60

Recommendation by Product ............................................................................................................. 61

Reasons to Buy by Product ............................................................................................................... 62

Project Success by Product ............................................................................................................... 64

Competition Trend ............................................................................................................................. 65

Summary ............................................................................................................................................... 66

Authors of The BI Survey 17 ................................................................................................................. 67


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Introduction

The BI Survey 17 follows on from fifteen successful editions of The BI Survey (formerly The OLAP

Survey). Based on the real-world experiences of 3,066 respondents, the value of The Survey depends

on us analyzing a sufficiently large, well-distributed and unbiased sample effectively. The BI Survey is

the largest and most thorough fact-based analysis of the BI market currently available, using 17 years

of experience to analyze market trends and challenge some of the myths surrounding the BI industry.

After data cleansing and removing responses from participants unable to answer specific questions

about their use of BI tools, we were left with a sample of 2,237 end users, 379 consultants and

236 vendor and reseller employees. Participants from all over the world took part in The BI Survey 17.

46 percent of user respondents stated they have an IT job function and 54 percent perform various line-

of-business roles.

The market for BI tools remains highly competitive and continues to be difficult to navigate for buyers.

Over the past few years, the emergence of new cloud tools as well as cloud versions of existing offerings

now contributes to the challenge of evaluating different offerings. This report provides visibility into where

companies apply their BI products and summarizes the strengths and challenges of leading vendors as

well as smaller ones that typically receive less press but still offer outstanding value to their customers.

The BI Survey also provides a detailed quantitative analysis of why customers buy business intelligence

(BI) tools, what problems they experience with the tools and their success rates in meeting project

objectives.

The BI Survey is not based on anecdotal accounts or personal opinions, unlike much analyst research,

neither is it intended to be a measure of market shares. It does not attempt to forecast future trends -

indeed it often provides evidence that undermines the reliability of many such forecasts.

The range of products included in The BI Survey 17 has grown to 42 this year, wider than ever before.

It includes not just products from well known BI giants, but also specialist tools from much smaller

vendors focused on specific markets or cloud-only SasS BI solutions.

The findings from The BI Survey 17 are presented in several documents, each focusing on a specific

set of results from The Survey.


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Document Description

The BI Survey 17 - The Results (this

document)

An overview and analysis of the most important product-related findings and topical results from The BI Survey 17.

The BI Survey 17 - Best Practices Provides advice to buyers of BI software as well as users and administrators of existing BI solutions based on the results of our analysis.

The BI Survey 17 - Sample, Products and Methodology

Provides details of the sample and an overview of our methodology including details of our calculation methods.

The BI Survey 17 - KPIs and Dashboards

This document provides descriptions of the KPIs we use in The BI Survey, including calculation methods.

The BI Survey 17 - Vendor

Performance Summaries

A series of executive reports on each product featured in The BI Survey 17. Each report contains a product review by BARC’s analyst team plus a summary of the relevant product-related results from The Survey.

Figure 1: Overview of The BI Survey 17

Figure 2: Screenshot from The BI Survey Analyzer web app

The BI Survey Analyzer contains information on all The BI Survey results and key performance

indicators (KPIs). This online tool allows users to carry out their own analysis. In The BI Survey Analyzer,


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the entire sample can be analyzed and it is also possible to filter results by region, company size and

other criteria. The tool allows users to export reports.

Figure 3: Screenshot from The BI Survey Analyzer web app


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Business Benefits

Organizations investigate, select and deploy business intelligence (BI) and analytics software to better

understand markets, businesses or operations; and to capitalize on the improved understanding.

Growing the impact of an organization or making it more efficient are the ultimate goals of BI projects,

and these are scored in The BI Survey as business benefits.

To discover more details about the different types and levels of achievement of business benefits, The

BI Survey asks key questions about the benefits produced by BI projects. Respondents are asked to

indicate the level of achievement gained from a list of eleven potential benefits.

The eleven benefits comprise four advantageous goals (see first column in Figure 4):

revenue growth (Green);

cost reduction (through six different answers, Blue);

critical reporting from operational systems (through three answers, Red); and

better performance (Yellow).

Figure 4: Frequency of business benefits achieved (n=2,565; Green =

Revenue growth, Blue = Cost reduction, Red = Critical reporting, Yellow =

Performance improvement)

Hig

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10)

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6)

Lo

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2)

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Weighting 10 6 2 -2 0

Faster reporting, analysis or planning 66% 24% 4% 1% 4% 8.11

More accurate reporting, analysis or planning 59% 29% 5% 2% 5% 7.73

Better business decisions 50% 32% 6% 2% 10% 7.04

Improved employee satisfaction 45% 35% 9% 3% 8% 6.66

Improved data quality 45% 33% 10% 4% 8% 6.62

Improved operational efficency 41% 37% 9% 2% 11% 6.42

Improved customer satisfaction 34% 32% 11% 5% 18% 5.51

Increased competitive advantage 24% 31% 14% 5% 25% 4.46

Reduced costs 20% 30% 19% 11% 21% 3.93

Increased revenues 17% 27% 15% 9% 33% 3.41

Saved headcount 14% 24% 21% 16% 25% 2.96


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Levels of Achievement

The levels of positive achievement are High, Moderate and Low. It is also possible to score a benefit

question as ‘Not achieved’. Where the benefit was not measured, the respondent can score it as ‘Do

not know’.

Figure 5: To what level have you achieved the following benefits with BI?

(n=2,565)

In 2017, The BI Survey once again validates that a significant majority of organizations have achieved

business benefits from their BI implementations. In this section, the goals of the business benefits will

be addressed in order of their business impact, starting with revenue growth, followed by cost reduction,

critical reporting and performance improvement.

Revenue Growth

More than half (59 percent) of respondents report ‘Increased revenues’ for their companies as a result

of using BI software products and services.

The percentage of respondents who said their organizations had achieved “high” revenue growth

increased from 13.6 percent in 2016 to 17 percent in 2017, a 25 percent improvement year over year.

66%

59%

50%

45%

45%

41%

34%

24%

20%

17%

14%

24%

29%

32%

33%

35%

37%

32%

31%

30%

27%

24%

4%

5%

6%

10%

9%

9%

11%

14%

19%

15%

21%

5%

5%

11%

9%

16%

4%

5%

10%

8%

8%

11%

18%

25%

21%

33%

25%

Faster reporting, analysis orplanning

More accurate reporting, analysisor planning

Better business decisions

Improved data quality

Improved employee satisfaction

Improved operational efficency

Improved customer satisfaction

Increased competitive advantage

Reduced costs

Increased revenues

Saved headcount

High Moderate Low Not achieved Do not know


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High growth of revenues is defined by the OECD, Eurostat and the U.S. Federal Reserve as being over

20 percent for an organization, and it is interesting to see BI software playing a role in such growth.

Moderate growth of revenue is defined by the same economic authorities as an ‘above-market rate’ of

five to 15 percent, and respondents reporting this stayed roughly steady at 27 percent.

Low growth is defined as being “at-market” OECD rates of one to 5 percent. In the BI Survey 17, low

revenue increases were reported by 15 percent of respondents.

There is reassurance too for the risk-averse reader wondering if BI can be a defensible investment, as

only 9 percent of respondents reported that increased revenues were not achieved. This number is also

likely echoed in the very low number of BI products discontinued (only 14 percent, and even a minority

of that for business reasons).

Lastly there are clear opportunities for improvements in measuring business benefits, as this year

33 percent of respondents indicated that ‘increased revenues’ were not yet measured. BI leaders

wanting to grow their businesses will note that their peers are measuring and reporting material financial

success with BI projects. Detecting such business value in technology should be a welcome and familiar

project to the office of the chief financial officer (CFO), where assistance in measuring financial returns

to the business will be found, especially in the area of capital budgeting as part of financial planning and

analysis (FP&A). Elsewhere in this results document it will be seen that the finance department is often

reported as the most deployed use case for reporting.

Cost Reduction

More than two-thirds (69 percent) of respondents managed to reduce costs to varying degrees. There

are many components to reducing cost measured in The BI Survey, and among the business benefits

there are arguably no less than five centered around saving money for an organization, including of

course ‘Reduced costs’.

‘Improved employee satisfaction’ is a factor in reducing employee turnover, where the costs of replacing

an employee are widely reported as over 50 percent of the employee compensation. This valuable KPI

was achieved by all but 3 percent of cases measured, and 45 percent noted high achievement.

‘Improved operational efficiency’ is a frequently achieved business benefit in The BI Survey this year,

with 78 percent of respondents scoring this KPI with high or moderate results. Only 2 percent of people

failed to achieve this goal. It is heartening to remember the demographic rise in The BI Survey of

operations department users over the last few years matched with significant results here in ‘improved

operational efficiency’.

‘Saved headcount’ is another great way of avoiding cost, especially with more automation of unpopular

and highly repetitive tasks. This benefit was achieved by 59 percent of people, although with a broader

spread of results, and with 16 percent of respondents failing to achieve the goal. The overall result is

that this benefit has the lowest score in the weighted benefits index, but it is still positive, suggesting

that it is a worthwhile endeavor, but harder to achieve.

‘Increased competitive advantage’ is another cost-saving KPI, most usually associated with the sales

and marketing business functions, where it can have many applications. Increased marketing

messaging or visibility effectiveness are examples, and every fractional percent of marketing cost saving

can significantly help a business. For the sales function, increased win rates, pricing effectiveness and

reduced discounting will all help in lowering the cost of sales reported.


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Critical Reporting

Of all the business benefits measured in The BI Survey, it is arguable that the most critical of all are the

ability to run the business with accurate reporting and make better business decisions. Both of these

benefits are achieved at very high rates.

‘More accurate reporting, analysis, or planning’ is the second most frequently achieved business benefit,

and has the second highest weighted score in the BBI as well, which weights higher value results. More

than half (59 percent) of respondents achieved a high level of benefit and 29 percent a moderate level.

Only 2 percent did not achieve this goal at all.

‘Improved data quality’ might be considered as a contributing attribute of ‘More accurate reporting’. A

vast majority (88 percent) of respondents achieved the goal. It is interesting to note that a material

improvement in data quality can enable whole classes of applications for multiple business departments.

Only 2 percent of respondents did not achieve ‘better business decisions’ with their project. More than

three-quarters (82 percent) of respondents scored this a high or moderate achievement.

Performance Improvement

2017 sees The BI Survey audience reporting high benefits from ‘faster reporting, analysis, or planning’.

This has the highest level of achievement of any benefit in the Survey as 94 percent of respondents

reached a positive result, with 66 percent reporting high benefits. Only 1 percent of respondents did not

achieve this goal. For an audience under extreme time pressure such as front line workers at any level

from the most junior to the most senior, a BI application that gives faster results is highly valuable. In

some cases of real-time monitoring, faster results enable the whole use case. As BI is used in ever more

business departments, and by more operational workers, this measure will continue to be important.

A Note on Measurement

Technology buyers continue to demonstrate that they highly value BI technology, especially when they

are able to achieve material business benefits. Continued investment in measuring the achievement of

business benefits is likely to be very popular with buyers, and encourage ever more investment in BI

technology and deployment of ever more applications.

It is interesting to see that measurement of benefits has some patterns. In current BI technology, it has

become much easier to detect issues that relate to the technology itself, such as data quality, accuracy

or performance. These matters are well known in product support for generating a good number of

cases. These cases are costly for the customer, the implementer and the technology vendor. Product

tools for detecting and measuring these matters help in improving case avoidance, resolution times and

product improvement. The results save money for all.

However, there is potential opportunity in using the BI technology itself to measure business benefits

that might be considered ‘under-measured’ in 2017. The opportunity could be worth a great deal, as the

least measured KPIs are also those tied to the most obvious sources of growing revenue and reducing

cost. This can be seen in the image below, where ‘increased revenue’ is measured least of all. Benefits

related to cost savings are also measured less.


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Performance improvement). Highlighted: Measurement patterns of business

benefits, indicating opportunities for measuring material positive benefit.

A Note on Risk and Failure

While the opinion above focuses on opportunity and measurement, this section will briefly comment on

the risk of not achieving goals, as it has a material impact on the business benefits index (BBI). It is very

important to retain perspective in the face of such risks, and where possible make attempts to control

and hedge risks of failure. In short, it is worth noting that the vast majority of projects achieve the

business benefits measured.

As can be seen in Figure 7, a good number of respondents indicated that they had not saved headcount

(16 percent) and reduced costs (11 percent). Increasing revenue had a non-achievement rate of

9 percent. The other benefits scored lower.

The perspective to be considered would be a ratio of success versus failure. Even in the case of the

highest rate of non-achievement (16 percent), the rate of achievement (combining High, Moderate and

Low) would be 59 percent. This indicates that even projects where the rate of success is relatively low

are over three times more likely to succeed than fail.

Hig

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10)

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6)

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Reduced costs 20% 30% 19% 11% 21% 3.93


Saved headcount 14% 24% 21% 16% 25% 2.96


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Performance improvement). Highlighted: Not achieved

The Business Benefits Index (BBI)

A scoring system is used to derive a composite weighted score for each benefit, based on the level of

benefit achieved or not achieved. This system is called the BBI (Business Benefits Index). For further

information on the calculation methods used, see the ‘Sample, Products and Methodology’ document.

The weighting system is deliberately designed to be dimensionless in order to be consistent for

comparison of KPIs across different dimensions, as will be seen below.

Figure 8 below shows the overall breakdown of responses to the business benefits questions. The levels

of achievement, along with their weightings, are shown on the horizontal axis and aggregated in the BBI

‘Weighted score’ column to the right.

Hig

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10)

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Saved headcount 14% 24% 21% 16% 25% 2.96

Reduced costs 20% 30% 19% 11% 21% 3.93











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Performance improvement)

In order of weighted score the BBI will be used in a range of different analyses, so it is good to examine

the order of achievement and the most significant factors in the BBI.

1. Overall, ‘faster reporting, analysis or planning’ is scored highest of all, with more than 94 percent

of respondents stating the benefit had been achieved, and a BBI score of 8.11. The industry’s

continuing implementation of performance-improving enhancements such as leveraging

memory, columnar data sources and smarter cache handling contributes to this high

achievement rate.

2. Respondents who had reported achievement of ‘more accurate reporting, analysis and

planning’ number 93 percent with a BBI score of 7.73, placing second. It is possible this is

related to enhancements in data preparation and joining technologies in a wide range of the

software products surveyed. Information on these will be found in the Vendor Performance

Summary documents.

3. Fully 88 percent of respondents said that ‘better business decisions’ were achieved. The ability

to make better business decisions is a highly desirable benefit. However, it is one that cannot

be accurately scoped when developing a project’s business case. While all BI projects would

hope to gain this benefit, few would be cost-justified against the possibility that it ‘might’ one day

be achieved. Furthermore, the level of benefit might be difficult to qualify. Nevertheless a BBI

score of 7.04 puts this benefit in third place.

4. ‘Improved employee satisfaction’ moves into fourth place this year, up one place from 2016,

with a BBI score of 6.66. Respondents achieving this benefit number 89 percent.

Hig

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10)

Mo

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6)

Lo

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2)

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Reduced costs 20% 30% 19% 11% 21% 3.93


Saved headcount 14% 24% 21% 16% 25% 2.96


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5. ‘Improved data quality’ is a benefit that can be measured in some BI software. Although often

one of the more difficult factors to achieve in a BI project, it ranked fifth with a BBI score of 6.62

and 88 percent achievement.

6. ‘Improved operational efficiency’ scored 6.42 and ranks sixth. Possibly this is related to the

maturity of projects started over the last few years where there was significant growth of new

projects for operations departments. 87 percent of respondents marked this benefit as

achieved.

7. ‘Improved customer satisfaction’ can be seen as an ‘external effect’ of BI and it is good to see

the benefit scores 5.51 and ranks seventh. 77 percent of respondents achieved the benefit.

8. The next BBI is ‘increased competitive advantage’ which scores 4.46, and ranks eighth with

69 percent achievement.

9. ‘Reduced costs’ scores 3.93, with an achievement rate of 69 percent, ranking ninth due to a

higher rate of non-achievement, which has a negative impact on the BBI.

10. ‘Increased revenues’ is arguably the highest prized benefit of all, and is harder to achieve. This

year the BBI score is 3.41 and the percent achieved is 58 percent. The rank is 10th as the ‘not

achieved’ rate is lower than ‘saved headcount’.

11. ‘Saved headcount’ scores a BBI of 2.96 and ranks eleventh, with an achievement rate of

59 percent.

The overall BBI average is 5.71 this year. This is a significant improvement from a historical perspective.

Back in 2011 the overall BBI average was 4.89. While the benefits have changed somewhat, the

aggregate score improvement is noteworthy.

Industry Achievement of the Business Benefits Index

Respondents in different industries score their achievements somewhat differently. The important

aspect of the data is that all scores are very strongly positive without much differentiation in the scores.

Figure 9: BBI by industry (n=2,555)

While some scores are a little lower than others, there is no “bad” score. For example, it is possible that

the utilities sector had a greater focus on moderating costs in a highly regulated business environment

with less flexibility.

6.0

6.0

5.8

5.8

5.7

5.6

5.6

5.4

5.2

Transport

IT

Retail/Wholesale

Financial Services

Services

Manufacturing

Telecommunications

Public sector and Education

Utilities


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Satisfaction with the BI Project Experience

The range and complexity of BI projects surveyed is very wide. Smooth deployment and ongoing

operations of more complex projects require the participation of the vendor, users, information

security/compliance specialists, help desk support personnel, and sometimes third parties. Agile project

methodology combined with BI products offering more self-service features can result in shrinking

overall rollout times. Nevertheless issues like data quality, governance and resource constraints persist,

which account for over a third of serious issues encountered by business users.

Figure 10: Please rate the following aspects of your project with your product

(n=2,501)

Figure 10 provides a broad picture of how projects performed on five measures of project success. The

satisfaction rates are improved again this year, and seen across the board. Good user satisfaction

overall rose to a 70 percent average. Notably, ‘Good’ user satisfaction with the implementation of both

technical and business aspects improves to 75 percent from 70 percent and 71 percent respectively.

Correspondingly ‘Poor’ scores declined from an average of 5 percent to 4 percent.

75%

75%

68%

67%

63%

23%

22%

27%

29%

30%

User satisfaction with implementation ofbusiness aspects

User satisfaction with implementation oftechnical aspects

Completion within the budget originally set

Satisfaction of administrators with technicalimplementation

Completion within the timeframe originallyspecified

Good Moderate Poor


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Project Success Surveyed by Product

This year there is an additional view of these five different aspects of project success, with a score on a

scale of 0 (poor) to 10 (good), and the scoring joined with the product surveyed (see Figure 11).

It is worth remembering that a majority of respondents had a good experience, and that the number of

respondents who scored a vendor and product as a poor experience was exceedingly small at just

4 percent on average.

It is also worth noting that the complexity of projects is not equal among the products surveyed.

Nevertheless it is useful for buyers and vendor product managers to see the spread of scores among

the different aspects of project success as a weighting factor in the decision-making process.

Of particular note are the high scores across the board for these vendors (in alphabetical order): cubus

(highest score is 9.7, lowest score is 8.8), Phocas (highest score is 9.7, lowest score is 9.0), Pyramid

Analytics (highest score is 9.4, lowest score is 8.5), and Yellowfin (highest score is 9.6, lowest score is

9.1).

Of note generally is that projects are very successful, with very high levels of user satisfaction. The fact

that user satisfaction is higher with the business aspects rather than the technical aspects is just as it

should be. While timeframe and budget do not score as high, none of the scores reported here are bad.


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Figure 11: Project success surveyed by product (n=2,316; 10=good, 0=poor)

User

satisfaction

with

implementation

of technical

aspects

User

satisfaction

with

implementation

of business

aspects

Satisfaction of

administrators

with technical

implementation

Completion

within the

timeframe

originally

specified

Completion

within the

budget

originally set

Bissantz 9.3 9.3 8.9 7.9 8.2

BOARD 8.7 8.4 7.9 7.3 7.5

CALUMO 8.4 8.0 8.1 7.4 6.6

Carriots Analytics (Envision) 8.4 8.1 8.3 8.1 8.0

Chartio 8.6 8.1 7.5 7.0 6.8

Cubeware 8.2 8.1 8.1 7.3 7.6

cubus 9.0 9.7 9.0 8.9 8.8

CXO-Cockpit 8.9 9.5 8.9 8.9 8.5

Cyberscience 8.6 8.9 8.3 8.0 7.4

DigDash 9.2 8.6 8.3 8.3 8.3

Dimensional Insight 8.6 8.4 8.6 8.3 7.8

Domo 8.2 8.5 8.7 6.7 7.0

Dundas 9.0 9.0 8.6 7.7 7.1

Entrinsik 8.7 8.0 8.1 7.3 7.5

IBM Cog Analytics 6.7 7.2 6.7 6.1 5.7

IBM Plan Analytics 8.9 9.2 8.3 7.3 6.9

Infor 7.9 8.8 7.7 7.2 7.8

Information Builders 8.3 8.0 8.0 7.6 7.4

Jedox 8.7 9.1 8.1 8.2 8.7

Longview Analytics 9.1 9.4 8.5 8.3 7.6

MicroStrategy 8.4 8.3 8.4 6.9 7.2

MIK (prevero) 8.9 9.2 8.1 7.7 8.9

MS Excel 7.9 7.6 6.5 6.8 7.0

MS Power BI 8.3 8.3 7.1 7.2 7.7

MS SSRS 8.2 7.9 7.5 7.1 7.9

Oracle BI 7.1 6.9 6.3 6.1 5.9

Phocas 9.6 9.7 9.7 9.1 9.0

prevero (prevero) 9.1 9.8 9.1 7.8 7.8

Pyramid Analytics 8.6 8.5 9.4 8.9 9.4

Qlik Sense 8.7 9.0 7.8 8.5 7.9

QlikView 9.0 8.9 8.3 7.6 7.6

SAP BEx 6.4 6.8 5.6 5.2 5.3

SAP BO Analysis 7.6 7.8 7.2 6.0 6.9

SAP BO Design St. 7.9 7.9 6.3 6.0 6.8

SAP BO WebI 6.3 7.2 6.3 5.3 6.1

SAS Enterprise BI 7.4 7.4 6.1 4.4 4.6

Sisense 8.9 8.2 8.3 8.3 7.9

Tableau 8.1 8.1 7.0 7.6 6.7

TARGIT 9.0 9.1 8.4 8.3 8.0

TIBCO Spotfire 7.8 8.1 6.9 7.6 5.4

Yellowfin 9.3 9.2 9.1 9.3 9.6

Zoho Reports 8.8 8.4 8.1 8.0 8.1


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Deployment

This section focuses on BI deployment, analyzing product usage and penetration in companies of

different sizes and geographical location. The BI Survey 17 compares how widely products are

deployed, where they are applied, and analyzes the frequency of BI usage across business functions.

When analyzing The BI Survey deployment statistics or planning BI initiatives at your own organization,

it helps to distinguish between different types of users and the BI activities they perform. For example,

do you need a product that can generate formatted reports for 1,000+ users? Or do you have 300 users

who just need to view metrics on a smartphone - reducing demands for self-service creation but

increasing the need for responsive or native mobile user interfaces?

BI Product Penetration by Product

Multiple factors including planning, product fitness, and even business function impact BI penetration

rates across the enterprise. For example, if a product focuses on analytics for a specific department

such as legal or finance, you would expect lower total user counts. The same would be true if a company

has a market focused on small businesses instead of large enterprises.

In Figure 12, Information Builders and MicroStrategy have risen above Microsoft Excel as the products

used for BI by the most people in an organization.

It is very useful to compare Figure 12 with Figure 13, which shows the percentage product penetration

as a percentage of total employees. In many ways the results are almost inverted.

In this case, one can see that although Zoho, Chartio, Sisense and Phocas have a somewhat lower

number of users per organization, the percentage of employees in those organizations using the product

is among the highest in the Survey. It is also interesting to see Information Builders above Microsoft

Excel in both charts.

Also of interest is the growth in penetration overall from 2016 to 2017. Last year the highest mean

percentage was 40 percent. This year that has risen by 5 percent (year-over-year) to 42 percent

penetration.


- 19 -

Figure 12: How many people in the organization are using the product

surveyed, ranked by product (n=2,323)

Mean Median

Information Builders 3593 400

MicroStrategy 2703 250

MS Excel 2633 100

SAP BEx 2018 450

IBM Cog Analytics 1495 200

SAP BO WebI 1371 200

Longview Analytics 1310 475

QlikView 1285 100

TIBCO Spotfire 1244 200

SAP BO Analysis 935 50

SAP BO Design St. 919 55

Domo 595 30

Qlik Sense 577 50

IBM Plan Analytics 537 125

Tableau 502 57

Oracle BI 493 100

MS SSRS 481 60

Dundas 415 30

SAS Enterprise BI 413 150

CALUMO 339 30

DigDash 268 15

Dimensional Insight 216 128

Infor 199 100

cubus 190 100

BOARD 179 60

Pyramid Analytics 179 73

CXO-Cockpit 167 55

MS Power BI 164 30

TARGIT 161 91

Yellowfin 141 35

MIK (prevero) 119 50

Cubeware 108 70

prevero (prevero) 97 42

Sisense 97 12

Entrinsik 81 43

Phocas 74 48

Bissantz 70 25

Chartio 63 25

Cyberscience 62 25

Jedox 54 25

Carriots Analytics (Envision) 20 5

Zoho Reports 8 5


- 20 -

Figure 13: Percentage of product users / total employees (n=2,322)

Mean Median

Chartio 42% 32%

Domo 32% 30%

Information Builders 30% 11%

Sisense 28% 17%

Phocas 27% 22%

Zoho Reports 27% 17%

MS Excel 26% 13%

BOARD 23% 9%

TARGIT 22% 13%

MS SSRS 22% 14%

Pyramid Analytics 21% 15%

IBM Cog Analytics 21% 10%

Dimensional Insight 20% 12%

Yellowfin 19% 13%

MicroStrategy 18% 10%

Longview Analytics 18% 7%

Dundas 17% 10%

MS Power BI 16% 7%

DigDash 15% 9%

prevero (prevero) 15% 10%

Jedox 15% 8%

QlikView 15% 7%

Oracle BI 14% 10%

TIBCO Spotfire 14% 6%

CALUMO 14% 10%

Entrinsik 13% 10%

Cyberscience 12% 7%

Bissantz 11% 8%

SAP BO WebI 11% 6%

Qlik Sense 10% 3%

cubus 10% 4%

Carriots Analytics (Envision) 10% 5%

SAS Enterprise BI 10% 4%

Cubeware 10% 6%

Infor 10% 4%

SAP BEx 9% 7%

Tableau 9% 3%

MIK (prevero) 8% 4%

SAP BO Design St. 7% 2%

SAP BO Analysis 6% 2%

IBM Plan Analytics 6% 3%

CXO-Cockpit 4% 1%


- 21 -

BI Product Penetration by Region

When these figures are aggregated over all products and segmented by geographic region, an

interesting picture emerges of global growth in penetration.

Mean penetration rates vary somewhat by region. North America (steady on 26 percent) ranks equal to

Asia Pacific (26 percent, up four from 22 percent last year). Europe is third (21 percent, up from

20 percent last year) and South America is fourth (at 15 percent up from 11 percent last year). A growing

number of new BI use cases along with increased self-service combined with good results with business

value and project satisfaction provide strong motivation for companies to expand BI access to more

employees.

Figure 14: Percentage of BI users in company, by region (n=2,513)

BI Broadens its Audience

There is a remarkable consistency of the different types of people working in business departments that

use BI. Most particularly, a large number of people identify themselves as working in the finance

department or as part of the management organization of the company.

This year, as every year, a wide range of different departments are represented. There is a good amount

of consistency to these trends over time, and most lines remain steady. There is good growth continuing

to be seen in operations, and only some slight downturns.

The sustained leadership of the finance organization in using BI is potentially where departments

aspiring to broaden or deepen their use of BI may find allies with expertise, encouragement and

experience.

2017 results in The BI Survey at department level in Figure 16 reinforce the message of Figure 15 in

more detail.

Figure 16 reinforces the trend lines seen earlier, with some more specific percentages for each

department. In some companies it is clearly the normal position to use BI in many departments.

26%

26%

21%

15%

15%

17%

12%

7%

Asia and Pacific

North America

Europe

South America

Mean Median


- 22 -

Figure 15: All departments that use BI as reported by each respondent,

timeline view (n=changing basis)

Figure 16: Departments using BI in 2017 (n=2,600)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Finance

Management

Sales

IT

Operations

Marketing

Human resources

Procurement

Logistics

Service

R&D

83%

65%

58%

57%

54%

44%

35%

31%

31%

30%

15%

7%

5%

Finance/Controlling

Management

Sales

IT

Operations/Production

Marketing

Human resources

Service

Procurement

Logistics

R&D

Legal

Other


- 23 -

The Selection Process

While vendors continue to extend efforts to make the BI software selection and acquisition process

easier, companies still spend significant time and energy trying to make the best decision. Subscription

licensing and trial offerings have helped by minimizing risk in some cases, but these advantages have

been outweighed by new challenges stemming from sorting through an increasing range of new

offerings. This section is designed to inform buyers about the types of information evaluated by survey

respondents that make the biggest difference. It will also cover the factors that ultimately influenced their

selection decisions.

Drivers for Investing in a Product Selection Process

For BI Survey respondents selecting a BI product can be a major activity. BI teams have learned that a

BI product can be an enduring and valuable investment, so it is important to carefully consider the

available options.

How enduring can these BI products be? Figure 17 shows the timeframe of use.

Figure 17: Since when has your company been using your BI product?

(n=2,440)

While there is a range of responses it is clear that once a decision is made, a product can last for many

years. Fully 30 percent of respondents indicated the product had been in use for over eight years.

Product replacement is also a comparatively rare event. BI software has been in use for a very long time

by many companies. On occasion, some products have been in use for decades. One might imagine

that the “replacement” market for ‘new software to displace old’ would be moderately active. In 2017 this

part of the market is seen to be no more than 14 percent of the total (see Figure 18).

5%

14%

13%

9%

8%

6%

6%

6%

4%

30%

2017

2016

2015

2014

2013

2012

2011

2010

2009

Before 2009


- 24 -

Figure 18: Has your organization replaced or abolished any BI products in

the last 12 months? (n=2,479)

Figure 18 shows that this part of the market is a clear minority of the total market. Even within the

14 percent some fraction will be a complete ‘abolishment’ instead of a replacement. The reasons given

for the deprecation of BI products are quite numerous, and have held steady over the years.

Figure 19: Reasons for replacing/abolishing the BI system, timeline view

(n=changing basis)

While there are many reasons, lack of flexibility has consistently scored over 10 percent above the rest

of the reasons.

Given the ‘stickiness’ of a BI product, companies choose carefully.

14%

86%

Yes

No

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

2015 2016 2017

Software was not flexibleenough

Software difficult to use

Missing key product features

Company politics

Too expensive

Outdated/Discontinued by thevendor

Query performance too slow

Product could not handle ourdata volumes

Bad support

Lack of interest from businessusers

Unable to get/analyze datafrom some systems

Unreliable software

Poor data quality


- 25 -

Selection Methods

The BI Survey has a simple question set to determine the investment of time and energy in the selection:

is the evaluation formal, and is it competitive or single-product?

Figure 20: Selection method over time (n=changing basis)

Competitive evaluations remain very widely used at around three times the incidence of no evaluation

or a single-product evaluation. For the simpler and non-competitive selections, it may be that the BI

product serves a single, very well defined use case with little effective competition, or that the new

project is an extension of an adjacent project where an existing BI product has proven a very good fit

over a long time period (see Figure 17 for the longevity of BI product use).

0%

10%

20%

30%

40%

50%

60%

70%

2010 2011 2012 2013 2014 2015 2016 2017

Competitive evaluation

No formal evaluation

Single product evaluation


- 26 -

Reasons for Purchase

The drivers for selecting a BI product remain broadly consistent. Functionality is the most important

factor by a significant margin.

In 2017 ‘price-performance ratio’ moves into second place at 40 percent, while ease of use and flexibility

retain top five positions. Other reasons have broadly retained their scores from last year.

Figure 21: Reasons to buy a BI product (n=2,332)

51%

40%

37%

36%

35%

32%

28%

19%

18%

17%

15%

14%

14%

13%

11%

8%

8%

6%

5%

5%

3%

Functionality

Price-performance ratio

Ease of use for report recipients

Flexibility

Ease of use for report designers

Fast query performance

Predefined data connection

Large data handling capacity

Ability to support large numbers of users

Availability of people skilled in the toolset

Availability of local support

Vendor or product reputation

Completed 'proof of concept' faster or better

High innovative capacity of the vendor

Corporate standard

Size/financial stability of the vendor

Bundled with another product

Good vendor relationship

Deployment option

International focus of the software

Other

Cost-relatedProduct-related Vendor-related


- 27 -

Plans for Employing BI Products

Given the importance of functionality in the selection process it is important to understand what buyers

plans are for using BI products. The upcoming priorities scored in The BI Survey are a good aggregated

vision of plans coming in the next 12 months, in the longer term, or not required.

Figure 22: Do you use or plan to use your product for the following tasks?

(n=2,568)

As in 2016, over the next twelve months, the top three planned investment areas are “advanced

analysis” (20 percent), “dashboards/scorecards” (14 percent) and “budgeting and planning”

(13 percent). This correlates with continued BI product releases from vendors featuring more advanced

visualizations that support forecasting and relationship analysis with less need for dedicated and deep

in-house statistics and visualization experts. Overall, the aggregate relative positions of most of these

different use cases has not changed significantly.

84%

78%

70%

68%

37%

29%

19%

7%

10%

11%

14%

13%

10%

20%

4%

7%

8%

14%

12%

28%

5%

8%

12%

10%

35%

48%

33%

Standard/Enterprise reporting

Ad hoc query

Basic data analysis

Dashboards/Scorecards

Budgeting and planning

Financial consolidation

Advanced analysis

In use Planned to use within 12 months Planned in the long-term Not required


- 28 -

Products Evaluated

In The BI Survey 17, the list of products surveyed provides an interesting snapshot of the breadth of

vendors included in competitive evaluations.

Figure 23: Which BI products did your organization evaluate for acquisition?

(n=2,444)

For the third year, QlikView is once again the most included product in competitive evaluations,

appearing in 39 percent of selection processes, up from 37 percent in 2016. Tableau stays in second

position and is now included in 35 percent of evaluations, strongly rising from 29 percent last year (an

improvement of around 20 percent). Microsoft Power BI is also included much more frequently than in

2016, now at 26 percent (the same as Microsoft Excel), up 8 percent from 18 percent. These results

reaffirm the trend from last year of the top positions being occupied by very user-friendly offerings with

desirable functionality for a good number of users, while not always requiring extensive IT support.

39%

35%

26%

26%

23%

21%

21%

18%

16%

15%

14%

14%

13%

11%

10%

10%

9%

8%

7%

7%

7%

6%

6%

6%

6%

6%

6%

5%

5%

5%

5%

Qlik QlikView

Tableau

Microsoft Power BI

Microsoft Excel

IBM Cognos Analytics

Microsoft SSAS

Microsoft SSRS

Qlik Qlik Sense

MicroStrategy

SAP BW

SAP BO Web Intelligence

SAP BO Analysis

IBM Planning Analytics

Oracle BI

Microsoft SharePoint

SAP BO Lumira (Designer)

SAP Crystal Reports

SAP BEx

SAP BO Lumira (Discovery)

BOARD

Bissantz DeltaMaster

Cubeware

Jedox BI Suite

TIBCO Spotfire

Infor BI

Oracle Essbase

SAP BPC

SAS Intelligence Platform

Pentaho

SAP BW IP

Oracle Hyperion Planning


- 29 -

Key Resources in the Evaluation Process

While many resources form part of the evaluation process, those used most to influence the final

decision are usually of most interest.

In 2017 the order of influence is seen below. Varying types of trusted third parties appear to be the most

important influencers on buyers.

Figure 24: Which resources had the biggest influence in deciding which

product your company selected? (n=2,203)

There is quite a marked use of external resources. The ‘colleagues and peers’ segment may also include

communities of practice such as professional associations by vertical industry, horizontal business

function (e.g. finance), or maybe by technology interest such as data visualization or advanced analysis.

29%

24%

15%

14%

13%

13%

10%

9%

8%

7%

5%

4%

3%

6%

Information from colleagues/peers

External consultants

Product reviews from analyst firms

Technical literature

Vendor events

Vendor Web sites

IT events & trade shows

Search engines

Customer feedback surveys

Events focused on business users

Crowd software review platforms

Events presented by analyst firms

Social networks

Others


- 30 -

Products Acquired Post Evaluation

Following the evaluation process, the decision is made and the products are acquired. The BI Survey

asks which products are acquired, but this should not be thought of as an exact correlation with market

share.

Figure 25: Which of the following BI products has your organization

acquired? (n=2,530)

There is an interesting change in 2017: QlikView rises just above Microsoft Excel, while Excel drops by

5 percent year over year. Qlik continues to invest in customer enablement resources such as its

community and marketplace, which could account for this continued strength in evaluation and the top

ranking this year.

Microsoft occupies the next four positions with Power BI moving up 3 percent year over year.

Tableau, MicroStrategy, SAP and IBM all maintain strong positions in these rankings as highly capable

products used in a wide variety of organizations.

17%

17%

15%

13%

13%

12%

10%

9%

9%

8%

7%

7%

6%

6%

6%

5%

5%

4%

4%

4%

4%

3%

3%

3%

3%

3%

3%

3%

3%

3%

3%

Qlik QlikView

Microsoft Excel

Microsoft SSAS

Microsoft Power BI

Microsoft SSRS

Tableau

SAP BW


MicroStrategy

Qlik Qlik Sense

SAP BO Analysis



SAP BEx

Microsoft SharePoint

IBM Planning Analytics

SAP Crystal Reports


Oracle BI

Oracle Essbase


SAP BW IP

SAP BPC

SAS Intelligence Platform

Cubeware

Longview Analytics

Entrinsik Informer

BOARD

Infor BI

Oracle Hyperion Smart View for Office



- 31 -

Implementation, Support and Challenges

Once the evaluation, selection and acquisition processes are completed, work begins on the

implementation. Success often hinges on many factors: from functional engagement to product fit to

technical resource availability. Timely implementation success of BI projects requires a combination of

customer and vendor resources as well as leadership that can align company processes with a new BI

product.

As The BI Survey results demonstrate, a decent implementation with responsive support is crucial as

users begin navigating interfaces and spotting data issues. The ability to more seamlessly incorporate

BI software into existing operations ultimately makes or breaks implementations. Earlier we saw that

user satisfaction with BI is high. In Figure 26, it is also clear that the normal situation is for no serious

problems to be encountered.

Figure 26: What, if any, are the most serious problems you have encountered

implementing your product? Timeline view (n=changing basis)

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2013 2014 2015 2016 2017

No significant problems

Lack of resources on theproject team

Unclear requirements

Data migration

Software-related issues

Tight deadline

Training related issues

Lack of support frommanagement

Customization of the product

Costs higher than expected

Lack of expertise of theimplementation partner

Lack of project management

Lack of resources of theimplementation partner

Lack of communication in theproject team


- 32 -

Figure 26 shows that while the greatest likelihood is that no serious problems will be encountered, that

score is less than 50 percent. However, the trend line is back to moving in a positive direction for buyers

and implementers. Among the serious problems that are encountered, the overall trends are down or

flat across all types of problem reported. Survey responses also indicate that a wide range of different

serious problems may be encountered. Notably, the most likely is a lack of resources in the project team,

although the reasons for the lack of resources are not disclosed. It is possible for example that cost isn’t

the major factor, given the business benefits garnered by many projects, but that a lack of availability of

skilled resources is the main issue. Certainly lack of resources on the part of the implementation partner

is not a problem with a high score. At less than 5 percent, it is the lowest scored serious problem.

Figure 27 provides a similar spread of scores on serious problems (or the lack of them) for business

users.

Figure 27: What, if any, are the most serious problems your business users have encountered

in the use of your product? Timeline view (n=changing basis)

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2011 2012 2013 2014 2015 2016 2017

No significant problems

Slow query performance

Lack of interest from businessusers

Poor data quality

Poor data governance

Administrative problems

Company politics

Software not flexible enough

Product can not handle datavolume

Unreliable software

Software too hard to use

Missing key features

Unable to get data fromsystems

Product security limitations

Handle large number of users


- 33 -

Once again the highest score is for ‘no significant problems’, at less than 50 percent but trending in a

positive direction. Of note here is that when problems do occur, they are sometimes of a different nature

to the implementation problems. In this instance, slow query performance is scored as the most

frequently occurring problem, although it is on a downward trend.

Query performance does vary quite widely in different types of projects. Figure 28 shows the range and

median of typical query response times.

Figure 28: What is the typical query response time that end users see in your

BI product? (n=2,414)

The majority of respondents see response times of less than 5 seconds (52 percent). Moreover, the

median time for all respondents is just 5.71 seconds. While longer query response times are reported

as longer than that, this score is not yet directly linked to performance expectations for a product. It does

however provide context for the query performance problems seen by business users in Figure 27.

It is worth remembering at this point that while problems do occur, they can be resolved, especially

where there is good support from the implementer and the vendor.

Support Quality

Figure 29 shows scores on the quality of support from vendors and implementers.

In the case of both the vendor and implementer, support scores are high. The score for vendor support

is at 66 percent for ‘excellent’ and ‘good’ combined, and for implementers at 65 percent for ‘excellent’

and ‘good’ combined. Substandard scores for ‘not very good’ and ‘very poor’ are very low indeed for

vendors and implementers at just 7 percent and 4 percent respectively. It is also worth noting that

7 percent and 14 percent of respondents respectively say that vendor and implementer support has not

been used so far. While this could point to an an outstanding experience, it might also indicate a project

in the earliest stages of implementation.

9%

43%

21%

13%

6%

5%

1%

1%

1%

Less than 1 second

1 to less than 5 seconds




1 to less than 5 minutes



30 minutes or more


- 34 -

Figure 29: How do you rate the product support provided by the vendor and

the implementer of the product? (n=2,557/2,511)

Implementation Time

The median implementation time for projects is just under four months. It is well worth remembering that

larger or more complex projects are highly likely to take longer than the median time. Earlier in this

report, project implementation time was shown to be a significant source of business user satisfaction.

Good satisfaction with the timeframe of projects is well over 60 percent, and poor satisfaction with the

timeframe was a tiny minority.

Figure 30: How long did it take to implement the BI aspect of the application

from software purchase to initial rollout? (n=2,198)

Project timeframes for BI measured in years rather than months clearly do still exist, but the size and

complexity of such projects is sometimes astronomical, involving multiple large organizations; changes

in regulatory or other standards that must be observed; large, numerous, and sometimes very dirty data

sources; internationalization of results and interfaces, and other complexities besides. In these cases,

a long project is likely. It will be interesting to see in years to come if the growing adoption of an agile

technology implementation methodology reduces the median and ‘long’ times further.

One final view of the impact of implementation times is through the lens of the Business Benefit Index

(BBI)

28%

29%

38%

36%

19%

16%

5% 7%

14%

Vendor

Implementer

Excellent Good Satisfactory Not very good Very poor None used so far

15%

28%

26%

19%

10%

2%

1%

Less than 1 month

1 to 3 months

3 to 6 months

6 to 12 months

1 to 2 years

2 to less than 3 years

3 years or more


- 35 -

Figure 31: Implementation time by BBI Score (n=2,198)

Figure 31 shows there is a fair spread of BBI scores. Decent benefits are obtained by projects with the

longest implementation times, but the score increases by around 50 percent for the shortest

implementation times.

6.8

6.3

6.0

5.3

4.9

4.7

4.6

Less than 1 month

1 to 3 months

3 to 6 months

6 to 12 months

1 to 2 years

2 to less than 3 years

3 years or more


- 36 -

BI Trends

The landscape of BI capabilities is very wide and rich, and encompasses a range of standard reporting

as well as some more innovative technology trends. In this section, readers will discover the range of BI

trends scored in The BI Survey, and how their current use, planned use and general feasibility has

changed over time.

The trends are

Data discovery / visualization

Self-service

Collaboration

Visual design standards

Mobile BI

Cloud BI

Sensor data analysis

Spatial / Location analysis

BI with real-time data

Text data analysis

In the analysis of growing BI trends, all are increasing in use, with some increasing at a slightly faster

rate.

Figure 32: Use of BI trends over time (n=changing basis)

0%

10%

20%

30%

40%

50%

60%

70%

2011 2012 2013 2014 2015 2016 2017

Self-service

Data discovery/visualization

Collaboration


Visual Design Standards

Mobile BI

Spatial/Location analysis

Text data analysis


Cloud BI


- 37 -

Data discovery and self-service, both of which are designed to empower business users, continue to

score the highest use. A majority of respondents use these capabilities, and self-service has become

the first to reach 60 percent adoption.

Below the two leading trends, there is a big 20 percent drop down to the next set of trends, with the next

highest score being for collaboration.

For each trend, respondents continue to aspire to employ technologies at increasing rates. In all cases

the rates do increase, albeit at a slower pace than planned. In 2017 there is a distinct uptick in

collaboration use, greater than the rise of any other trend over the last year.

Figure 33: Use and planned use of collaboration (n=changing basis)

Figure 33 details the rise of collaboration since 2011. The use of collaboration has tended positively in

four of the last six surveys, and in 2017 the combined current use and use planned within twelve months

has passed 50 percent for the first time. Most importantly there is a big jump of 7 percent in current use,

which is the largest year over year change in the use of any BI trend in The BI Survey 17. This jump of

7 percent is fully half of the planned use being added to current use, which contrasts with other trends

in which less of the planned use translates to new current use.

Collaboration capabilities are popular with both technical and business users as they have become

familiar with social media and instant messaging. This familiarity may provide some demand for this

trend. While the exact implementation varies from product to product, few of the underlying technologies

are hard to develop or deploy, which might make for easier conversion from planned use to current use.

By their very nature, these capabilities can benefit from network effects, whereby the current use can

rapidly spread from person to person in a connected network. Adoption of collaboration may also

accelerate by adding to business benefits through better in-context communication of needs between

technology and business users. For example, a business user might be able to better understand and

23% 26% 29% 29% 29%36%

12%11%

11% 13% 14%

15%17% 13%16% 14% 13%

14%

48% 50%44% 44% 44%

36%

2011 2012 2013 2014 2015 2016 2017

Not in use/ Notrequired

Planned in thelong-term

Planned within 12months

In use


- 38 -

use a dashboard by reading annotations on the dashboard itself, rather than reading descriptions in an

email. From a CIO perspective, the proportion of employees using BI products in organizations has also

been steadily rising in recent years, which makes effective collaboration all the more important.

Sometimes there can be a large amount of planned use, which translates into actual use at a lower rate.

An interesting example of this would be in mobile BI, where the trend is very clearly upward in recent

years.

Figure 34: Use and planned use of mobile BI (n=changing basis)

In 2017 use of mobile BI increased a full 5 percent year over year from 23 percent to 28 percent, an

improvement of around 20 percent. The attractiveness of the capability is clear in the use planned within

12 months, which has never been less than 22 percent since 2011. While the trend to use mobile is

strong, the conversion of planned use to current use is rather less (approximately 50 percent lower)

than in the collaboration trend.

Deployment of mobile BI is valuable when done well, and can improve business benefits when BI can

be used in context, for example in a manufacturing plant, or in a field sales or marketing setting.

However, to achieve these goals does require some planning, sometimes some development, and some

sensitivity to the range of devices and operating systems which might run the mobile UI. These factors

can be tricky to handle, especially where a more fully featured and native mobile experience is expected.

The trend continues to grow though, and the complexities in development and deployment are gradually

becoming easier to address as the technology matures, and the skilled resources become more readily

available.

The growth of awareness and importance of deploying parts of the BI and data stack in the cloud

continue. Some vendors in The BI Survey are cloud-native and cloud-only with no possibility of using

the software on-premises. Material positive investments in cloud platforms continue among many large

8%13% 16% 18% 21% 23%

28%

22%

32% 26% 24%22%

24%23%

27%5%

23% 26% 24%22%

22%

43%50%

35% 32% 33% 31% 27%

2011 2012 2013 2014 2015 2016 2017

Not in use/Notrequired

Planned in thelong-term


In use


- 39 -

BI vendors such as Microsoft, Oracle, SAP and IBM. In addition there are also large and popular

offerings from Amazon AWS and the Google Cloud Platform.

In this environment, BI use of the cloud continues to rise. In Figure 35, respondents were asked a

general question to assess the feasibility of cloud-based BI.

In 2017, 60 percent of respondents felt the cloud-based deployment of a BI solution would be ‘feasible’

or ‘very feasible’ in their organization. Only 14 percent responded with the lowest ‘not feasible at all’.

Figure 35: Generally, how feasible do you think a cloud-based deployment of

a BI solution would be for your organization? (n=2,242)

As with all the BI trends, adoption and plans to adopt have changed over the years. In Figure 36, the

vertical bars represent respondents scoring their current use and planned use.

Figure 36: Use and planned use of cloud BI (n=changing basis)

2017 represents a big jump for cloud, albeit from a modest base. The increase from 12 percent to

16 percent current use is a 33 percent improvement. This improvement also indicates a 50 percent

23%

37%

26%

14%

Very feasible

Feasible

Not very feasible

Not feasible at all

5% 7% 10% 11% 12% 16%9% 6%

6%7% 8%

8%4%

11%12%

12% 13%

14%

82%76% 73% 69% 68%

62%

2012 2013 2014 2015 2016 2017

Not in use/Notrequired

Planned in the long-term


In use


- 40 -

conversion of planned use to current use, which is uncommon. While the clear majority of respondents

do not yet have a use case or requirement, attitudes are changing.

In Figure 37, the data for 2017 indicates a slight shift in attitudes toward feasibility, where the number

indicating ‘very feasible’ improved by 5 percentage points. As BI vendors continue to develop and

enhance their cloud product offerings, and customers document their positive technical and business

experiences, one might expect these numbers to shift further towards indicating increased adoption.

Figure 37: Generally how feasible do you think a cloud-based deployment of

a BI solution would be for your organization? Timeline view (n=2,184/2,242)

Cloud is also of interest because of its correlation with business benefits. In Figure 38, respondents

scored their business benefits and also their use of cloud. The results are clear. Cloud BI is the leading

trend with regard to high achievement of business benefits.

Figure 38: Trends in use by BBI score (n=2,505)

18%23%

37%37%

31%26%

15% 14%

2016 2017

Not feasible at all

Not very feasible

Feasible

Very feasible

6.9

6.8

6.8

6.7

6.7

6.6

6.5

6.5

6.5

6.3

6.2

6.2

Cloud BI/BIaaS

Spatial/Location analysis

Text data analysis

Embedded BI



Visual design standards

Mobile BI

Collaboration

Visual analysis

Data preparation

Self-service BI


- 41 -

While the spread of BBI scores is very tight with just 0.7 between the highest and lowest, cloud BI is still

a leader.

The trend towards cloud environments continues apace in the wider software industry. As of mid-2017,

many large software vendors adopted strongly positive positions on moving to the cloud. Oracle, SAP,

Teradata and IBM have joined front-runners Amazon, Microsoft and Google to launch cloud platforms

that host a variety of their own and third-party applications. These vendors are working with customers

of all sizes to help them understand the value of cloud computing and develop cloud migration plans.

Many smaller companies are cloud-first and cloud-native. While security is a concern, the data and data

management is likely to reside locally, with the BI on a hybrid or public cloud.

In short, the trend may be inexorable, with smaller companies and newer companies particularly likely

to adopt first. The positive experiences of the adopters of cloud technology are also having an impact.

In the BARC study “BI and Data Management in the Cloud: Issues and Trends”, published in January

2017, 87 percent of respondents rated their success in the cloud as high or moderate. Possibly we are

nearing a point where cloud will be adopted for a BI project as soon as it makes business or technical

sense to make the shift; that is, there is now less debate about the merits, and more debate about the

feasibility and date of the move.


- 42 -

Regional Perspective

The global BI software market has some clear characteristics and the aggregated data has significant

value in creating a picture of the state of the industry in a given year. However, some questions in the

survey reveal remarkable disparities between regions of the world. Such disparities span a wide range

of questions in The BI Survey, from usage problems to implementation problems to reasons for buying

the software. There are also some remarkable differences in the amount of data in the databases used

with BI products, and the capabilities and trends in use in different regions.

Penetration Rates

To start with, there are some differences in the penetration rates of BI software. Figure 39 indicates the

penetration as a percentage of total employees using the BI product.

Figure 39: Percentage of BI users in a company by region (n=2,513)

Asia and Pacific and North America are tied on a mean score of 26 percent of the company‘s employees

using BI. In contrast South America is at 15 percent, which is not significantly smaller but still represents

a regional difference. There’s a slightly bigger difference in the median percentage of employees using

BI. South America is at 7 percent, Europe is at 12 percent, and North America is leading on 17 percent,

which is well over double the lowest score.

26%

26%

21%

15%

15%

17%

12%

7%

Asia and Pacific

North America

Europe

South America

Mean Median


- 43 -

Predictive Analytics

While South America may have a lower penetration rate, its users rate the importance of predictive

analytics significantly higher than users in every other region.

Figure 40: How important is it that your company’s business intelligence tool

includes predictive analytics features? Regional view (n=2,201)

While predictive analytics is important or very important in a majority of regions globally, there is a clear

outlier in the importance in South America. Respondents scoring predictive analytics as very important

number 51 percent in South America, which is well over double the rates in Europe and North America.

South America has a decent population of data scientists, mathematicians (one recently won the Fields

Medal in 2014) and statisticians. The continent also has a good number of positive stories about using

advanced and predictive analytics to make a big difference to reform organizations, grow businesses

and protect the environment. In the last few years, the ways that predictive analytics can provide an

opportunity to leap ahead have become more widely understood, and this year The BI Survey results

show South America is keen to take advantage of the trend.

There are also some interesting regional differences in the use of predictive analytics methods.

Figure 41: Which predictive analytics methods do you use in your company?

Regional view (n=2,027)

Figure 41 reveals disparities in the usage rates for all the predictive analytics methods examined in The

BI Survey. Variations of 30 to 50 percent are quite common between the level of use of methods in the

different regions. Of greatest note is that classification methods are now in use at 50 percent of

respondents’ companies in South America, but only in 29 percent of respondents’ companies in Europe.

51%

36%

28%

16%

39%

45%

43%

44%

7%

16%

23%

32%

5%

8%

South America

Asia and Pacific

North America

Europe

Very important Important Not very important Not important at all

EuropeAsia and

Pacific

North

America

South

America

Clustering/Segmentation 30% 34% 32% 24%

Trend analysis/Automated forecasting 31% 44% 43% 36%

Discovery of correlations 24% 28% 35% 32%

Classification 29% 36% 38% 50%


- 44 -

Cloud Feasibility by Region

The global aggregate scores for the feasibility of cloud-based deployment of BI solutions continue to

improve this year. However, there are quite large differences to be seen between regions.

Figure 42: Generally, how feasible do you think a cloud-based deployment of

a BI solution would be for your organization? Regional view (n=2,242)

It is very interesting to see that Asia and Pacific respondents are the most cloud-friendly, with 41 percent

stating that a cloud BI deployment in their organization would be ‘very feasible’. This is significantly

higher than even North America which scores 33 percent on the same basis. The lowest rates by far

are seen in Europe, where non-feasibility is scored at double or triple the numbers elsewhere in the

world.

The logistical and regulatory environments are quite different from region to region, which may account

for a good part of these regional differences. Asia and Pacific are proving keen adopters of new

technology, much like North America. Datacenters, however, are not in every country, and resources

are an issue in some countries. Europe has additional supervision and regulation, with tough penalties

for transgression. These regional differences are likely to persist to some extent in 2017 and 2018 as

security and data protection laws are passed in the large global trading areas.

Trends in Use by Region

The usage rates of trends in the different regions of the world show some disparities. Readers from the

regions may have observations around the usage scored by the respondents. For practical purposes

though, this is a map for communities of practice by trend. For example, in South America there are

likely to be many more skilled resources and practitioners with experience in visual analysis as it is in

use by 79 percent of respondents there. There may well be a decent population too with skills and

experience in self-service BI and data preparation. In contrast, cloud BI is less popular at present. In

fact those top three trends are the most adopted everywhere, albeit at slightly different rates. As

discussed above, collaboration is clearly rising rapidly in adoption, and potentially the benefit of large

populations of users with the same language may spur adoption in North America and Asia and Pacific.

41%

33%

22%

15%

37%

38%

46%

35%

16%

19%

23%

31%

5%

9%

9%

18%

Asia and Pacific

North America

South America

Europe

Very feasible Feasible Not very feasible Not feasible at all


- 45 -

Figure 43: BI trends in use by region (n=2,505)

Amount of Data Used with BI

Respondents worldwide shared the amount of data in the databases used with their BI products. Once

again, there is quite a disparity in the amount of data used with BI products from region to region.

Figure 44: What is the amount of data in the database used with your BI

product? Regional view (median GB) (n=1,972)

North American respondents show the largest amount of data in use at a median of 270 GB. Europe is

20 percent lower, and South America and Asia Pacific are both below 100 GB in 2017. It will be

interesting to see the impact on these data volumes given the high adoption of BI trends such as sensor

data analytics and text analytics in Asia and Pacific and South America, as these trends are both

frequently associated with larger data volumes.

EuropeAsia and

PacificNorth America South America

Self-service BI 59% 61% 61% 66%

Visual analysis 53% 65% 67% 79%

Data preparation 48% 63% 65% 58%

Visual design standards 32% 30% 21% 36%

Collaboration 29% 49% 49% 33%

Mobile BI 24% 47% 29% 42%

BI with real-time data 23% 47% 44% 40%

Spatial/Location analysis 20% 28% 23% 34%

Embedded BI 20% 39% 30% 32%

Sensor data analysis 16% 23% 19% 34%

Cloud BI/BIaaS 12% 34% 21% 12%

Text data analysis 11% 35% 32% 33%

270

216

97

72

North America

Europe

South America

Asia and Pacific


- 46 -

Usage Problems

A good proportion of respondents in all geographic regions recorded no significant problems with their

BI usage. However, problems do persist.

Figure 45: Usage problems by region (n=2,431)

Reading Figure 45 as a potential buyer or implementer shows the areas of risk that should be contained.

The list of problems on the left of the table is all too familiar to implementers. Once again, there is a

clear opportunity to harvest the fruits of technology and business teams working closely together, not

just to avoid the risk of ‘lack of interest’, but also to ensure project success, and drive the business

benefits that are the ultimate goal. While an absence of significant problems is not yet a majority, project

success and business benefits are achieved by the majority this year.

Implementation Problems

Many respondents in all regions report no significant implementation problems. However, certain

problems appear to affect regions to differing degrees.

Data migration remains a frequent problem in Europe with a score of 19 percent. In South America by

contrast it scores just 6 percent. Lack of resources on the project team also scores 19 percent in Europe,

and just 6 percent in South America. In contrast, in South America the biggest problem is ‘costs higher

than expected’ at 15 percent, whereas elsewhere it is scored at 8 percent or less.

EuropeAsia and


No significant problems 38% 41% 45% 38%

Query performance too slow 16% 16% 12% 10%

Poor data quality 14% 11% 7% 10%

Lack of interest from business users 14% 12% 11% 19%

Poor data governance 12% 8% 12% 10%

Software not flexible enough 9% 8% 7% 12%

Company politics 8% 10% 9% 10%

Cannot handle our data volumes 8% 7% 6% 7%

Software difficult to use 8% 3% 6% 7%

Unreliable software 8% 6% 6% 3%

Administrative problems 8% 13% 12% 16%

Missing key product features 6% 8% 6% 4%

Unable to get or analyze data 4% 7% 8% 4%

Security limitations 2% 4% 4% 1%

Cannot handle large numbers of users 1% 4% 1% 6%


- 47 -

Figure 46: Implementation problems by region (n=2,390)

Reasons to Purchase

Reasons to purchase BI vary somewhat from one global region to another. Figure 47 shows all the

different reasons for purchase and the percentage of respondents from each region who cited each

reason as one contributing to a decision to purchase BI. Please note that respondents were able to

choose more than one reason to buy, which is why the numbers in the columns add up to much more

than 100 percent.

While BI functionality is the main reason for purchase worldwide, there are some interesting changes

from region to region for other reasons for purchase. For example, fast query performance is a significant

reason for purchase in South America at 48 percent, but in North America it is just 29 percent. Large

data handling capacity is only scored at 17 percent in Europe whereas in South America it is scored at

34 percent, exactly double. Corporate standards drive only 5 percent of purchases in Asia and Pacific,

yet the same reason in South America is over three times higher at 18 percent.

EuropeAsia and

Pacific

North

America

South

America

No significant problems 37% 37% 47% 41%

Lack of resources on the project team 19% 15% 9% 6%

Data migration 19% 10% 8% 6%

Unclear requirements 18% 16% 7% 11%

Tight deadline 15% 11% 6% 7%

Software-related issues 13% 13% 14% 13%

Lack of support from management 9% 9% 6% 8%

Customization of the product 8% 10% 9% 6%

Costs higher than expected 7% 8% 7% 15%

Training-related issues 6% 16% 16% 14%

Lack of expertise of the implementation partner 6% 7% 6% 7%

Lack of project management 5% 8% 5% 8%

Lack of resources of the implementation partner 4% 4% 2% 4%

Lack of communication in the project team 3% 6% 2% 4%


- 48 -

Figure 47: Reasons to buy. Regional view (n=2,330)

EuropeAsia and


Functionality 53% 48% 46% 55%

Price-performance ratio 39% 39% 44% 24%

Ease of use for report recipients 37% 34% 38% 31%

Flexibility 36% 32% 36% 33%

Fast query performance 32% 36% 29% 48%

Predefined data connection 30% 18% 26% 27%

Ease of use for report designers 28% 48% 45% 36%

Availability of people skilled in the toolset 18% 21% 15% 13%

Large data handling capacity 17% 21% 22% 34%

Ability to support large numbers users 16% 22% 21% 28%

Availability of local support 15% 21% 10% 19%

High innovative capacity of the vendor 15% 14% 8% 12%

Completed 'proof of concept' faster or better 14% 13% 14% 10%

Vendor or product reputation 13% 10% 15% 13%

Corporate standard 13% 5% 7% 18%

Size/financial stability of the vendor 10% 6% 6% 4%

Bundled with another product 8% 5% 10% 3%

Good vendor relationship 6% 9% 8% 3%

International focus of the software 5% 6% 3% 9%

Deployment option 4% 6% 9% 3%


- 49 -

Use With Data Sources

The Survey asks questions around the front-end tools in use for reporting on widespread back-end data

sources.

Front-end Products for Microsoft SQL Server Analysis Services (SSAS)

The most popular front-end clients for visualizing and analyzing data from SSAS all come from Microsoft

with Reporting Services leading the pack at 56 percent. It’s only after fourth place that Tableau appears

with 10 percent, followed by Bissantz DeltaMaster with 8 percent and QlikView with 7 percent. Often

times, Microsoft combines its enterprise BI tools on the same distribution and license making them easy

to access.

Figure 48: Front-end products for Microsoft SQL Server Analysis Services

(SSAS) (n=380)

Front-end Products for IBM Cognos TM1

In The BI Survey 17, IBM retains the front-end leadership for its enterprise planning and database

platform that it won back in 2016. The 2015 leader however remains close as Microsoft Excel (with add-

in) is still used to access TM1 by 47 percent of respondents. Interestingly, Information Builders jumps

up to take fourth place. Having different client tools and feature sets for accessing core enterprise

applications like TM1 is critical and will help TM1 remain as a top in-memory data layer. After Tableau,

the next most used tool for accessing TM1 is QlikView.

56%

53%

42%

36%

10%

8%

7%

6%

6%

6%

4%

4%

3%

3%

Microsoft SSRS

Microsoft Excel stand alone

Microsoft Excel with Add-In

Microsoft Power BI

Tableau


Qlik QlikView

Longview Analytics

Cubeware

Pyramid Analytics

MicroStrategy



Dundas


- 50 -

Figure 49: Front-end products for IBM Cognos TM1 (n=131)

Front-end Products for Oracle Essbase

Most BI and analytics with Oracle Essbase is still accomplished with five tools: Microsoft Excel with Add-

in (50 percent), Oracle Hyperion Smart View (42 percent), cubus (27 percent), Oracle BI (26 percent),

and Oracle Hyperion Planning (19 percent). Having developed its OLAP capabilities against SSAS,

Microsoft Excel with Add-in demonstrates its value in virtually any BI solution portfolio, and this year

jumps up to the top-ranked position. Founded in 1993, cubus’ OLAP roots continue to shine in Oracle

environments.

48%

47%

18%

15%

11%

11%

10%

8%

5%

5%

5%

4%

4%

4%

IBM Cognos BI/IBM Cognos Analytics


IBM Cognos TM1 Perspectives/ Analysisfor Excel (CAFE)/

Planning Analytics for Excel (PAx)

Information Builders WebFOCUS


Tableau

Qlik QlikView

IBM Cognos TM1 Web/PlanningAnalytics Workspace

Cubeware

Longview Analytics

Qlik Qlik Sense

Microsoft Power BI

Microsoft SSRS

MicroStrategy Analytics Platform


- 51 -

Figure 50: Front-end products for Oracle Essbase (n=101)

Front-end Products for SAP BW

Leading, but by 3 percent less than last year, is SAP BEx, which remains the top choice for analysis

with SAP BW. BEx is a suite of tools covering query and report design as well as scheduled distribution.

It also includes a browser front end for ad hoc queries and an Excel plug-in. In The BI Survey 17, SAP’s

BO Analysis, WebI and Lumira score 46 percent, 39 percent and 33 percent respectively. Up two places

to fifth position is the first non-SAP product (Microsoft Excel with Add-in).

Figure 51: Front-end products for SAP BW (n=250)

50%

42%

27%

26%

19%

9%

9%

6%

5%

5%

3%

3%

3%


Oracle Hyperion Smart View for Office

cubus outperform

Oracle BI


CXO-Cockpit

Longview Analytics

SAP Crystal Reports

IBM Cognos BI/IBM Cognos Analytics


Qlik QlikView


Tableau

59%

46%

39%

33%

25%

24%

18%

17%

17%

14%

11%

10%

7%

5%

5%

5%

4%

4%

SAP BEx

SAP BO Analysis




SAP BW IP

SAP BPC


SAP Crystal Reports

Qlik QlikView

Tableau


Longview Analytics

SAP Predictive Analytics

MicroStrategy Analytics Platform

SAP BO Cloud

Microsoft Power BI

Qlik Qlik Sense


- 52 -

Amount of Data in Databases for BI

Virtually all companies face increasing BI-related data storage requirements. While trends such as the

Internet of Things (IoT) and sensor data analysis in telecommunications or manufacturing explode data

storage requirements for specific industries, any company seeking better answers to questions around

competition, customer or market sentiment, and costs will encounter new data-related requirements,

either from a growing single source or from multiple sources.

Regardless of the driver, the result will include increases in the amount of data that companies need to

capture or process and store to satisfy new storage requirements. Many companies now leverage cloud-

based options that enable on-demand access to storage resources. The cloud works well for companies

who have reached capacity with their on-premises relational databases as well as those analyzing

unstructured text or large files where Hadoop or NoSQL systems are more effective.

Figure 52: What is the amount of data in the databases used with your BI

product? (n=1,972)

In 2017, 38 percent of respondents have 500 GB of data or more. 49 percent have 1 to 500 GB of data.

At the upper end, 26 percent have 1 TB to 500 TB+ compared to 26 percent in 2016. The median is just

a little over 200 GB.

2%

5%

5%

9%

7%

11%

7%

7%

8%

12%

13%

5%

5%

1%

2%

Less than 50 MB

50 to less than 500 MB

500 MB to less than 1 GB

1 to less than 5 GB

5 to less than 10 GB





500 GB to less than 1 TB

1 to less than 5 TB

5 to less than 10 TB



More than 500 TB


- 53 -

Data Volume by Industry

The data requirements and opportunities can be quite different by industry as can be seen in Figure 53.

Figure 53: Median data amount in GB used in BI products, by industry

(n=1,966)

By industry, financial services, telecommunications, transport and retail/wholesale consume the largest

data volumes for BI. Transport jumped up two places this year from a median of 362 to a new high of

412.5. With the manufacturing and utilities segments indicating more aggressive adoption plans for

sensor data, it will be interesting to see how the ranking order continues to evolve in the coming years.

Usage of BI Across Departments by Region

Due to geography, business departments with the same functional responsibilities can have unique BI

feature requirements stemming from local business practices, cultural differences or other factors.

From Figure 54, here is a list of similarities and differences in BI usage across a common set of business

functions around the world.

Worldwide, finance departments deploy BI more frequently than any other departments.

In North America, finance/controlling, IT, management and operations/production teams deploy

BI more frequently than other departments.

In South America, management teams deploy BI less frequently than other regions. However,

sales teams in South America lead the world in deploying BI.

Europe leads the world in deploying BI for finance departments, which also have the highest

deployment of all.

IT departments in North America leverage BI more often than in other regions.

Buyers, vendors and implementers who invest the time to understand and address these local

preferences may uncover opportunities to increase addressable markets and improve project outcomes.

573

469

413

286

191

186

147

135

83

Financial Services

Telecommunications

Transport

Retail/Wholesale

Manufacturing

Public sector and Education

Services

IT

Utilities


- 54 -

Figure 54: Departments using BI, by region (n=2,579)

Europe Asia and Pacific North America South America

Finance/Controlling 89% 70% 72% 76%

Management 70% 54% 61% 40%

Sales 63% 54% 48% 69%

IT 54% 54% 63% 60%

Operations/Production 51% 46% 60% 58%

Marketing 43% 37% 48% 51%

Procurement 37% 23% 24% 8%

Human resources 36% 34% 33% 31%

Logistics 35% 15% 22% 38%

Service 33% 28% 30% 28%

R&D 14% 13% 19% 11%

Legal 7% 7% 9% 11%

Other 3% 6% 8% 6%


- 55 -

Product-based Research Findings and Analysis

In this section the research switches mode to a view of the survey data by individual products. In the

tables of data that follow, the most meaningful mode of reading the percentages is horizontally by

vendor.

Products by Region

In Figure 55, each product shows the respondents using it in the survey filtered by region. For example,

Domo has 100 percent of its respondents in the survey from North America, and zero from anywhere

else. Bissantz in complete contrast has 100 percent of its respondents from Europe.

Figure 55: Products in use by region (n=2,369)


- 56 -

For all of the products in Figure 55, it is well worth remembering that the survey respondents tend to

come more from Europe than elsewhere, and rather less from Asia Pacific and South America.

Nevertheless it is interesting to see that MicroStrategy has the highest representation in South America,

where it has very slightly more respondents even than in North America.

It is also worth remembering that some companies are not yet set up to do business in a geographic

region, and consequently may have a very low number.

Industry Sectors Represented by Product

In Figure 56, we see the same list of vendors as before, but this time the respondents’ data is segmented

in the columns by the industry sector they identified as theirs. In this instance there is less selection bias

by the population of respondents in a geography, or by a vendor not doing much business in a region.

The standout results are interesting. For example, the respondents using Entrinsik are much more

numerous in the public sector and education, which makes sense as the company has a strong heritage

in serving educational establishments. Survey respondents in the services industry sector use a very

wide range of tools in aggregate.

It is also very interesting to see that across the table the telecommunications industry sector has far

fewer respondents overall.


- 57 -

Figure 56: Industry sectors by product (n=2,361)

Financial

ServicesManufacturing

Public sector and

EducationRetail/ Wholesale Services Telcos Transport Utilities IT

Bissantz 2% 31% 6% 26% 19% 0% 5% 1% 0%

BOARD 4% 43% 2% 16% 10% 4% 2% 2% 8%

CALUMO 10% 8% 24% 10% 35% 0% 2% 0% 2%

Carriots Analytics (Envision) 0% 3% 3% 0% 16% 0% 0% 72% 6%

Chartio 3% 8% 25% 3% 20% 3% 3% 0% 18%

Cubeware 1% 51% 4% 21% 10% 1% 6% 0% 0%

cubus 17% 23% 6% 6% 9% 3% 0% 20% 3%

CXO-Cockpit 13% 35% 0% 6% 23% 0% 0% 3% 3%

Cyberscience 10% 58% 5% 10% 10% 3% 3% 0% 0%

DigDash 3% 6% 22% 19% 16% 3% 3% 0% 25%

Dimensional Insight 0% 8% 24% 42% 18% 0% 8% 0% 0%

Domo 11% 4% 7% 14% 25% 0% 4% 0% 21%

Dundas 0% 11% 6% 3% 31% 6% 0% 11% 20%

Entrinsik 0% 2% 88% 2% 3% 2% 0% 0% 3%

IBM Cog Analytics 26% 12% 9% 12% 17% 5% 3% 0% 10%

IBM Plan Analytics 0% 47% 3% 9% 16% 3% 13% 3% 6%

Infor 7% 58% 0% 7% 7% 2% 9% 4% 4%

Information Builders 24% 6% 15% 9% 24% 0% 9% 3% 3%

Jedox 9% 24% 6% 6% 18% 0% 9% 6% 15%

Longview Analytics 9% 43% 2% 11% 9% 0% 9% 9% 6%

MicroStrategy 25% 7% 9% 28% 18% 1% 2% 3% 6%

MIK (prevero) 8% 30% 8% 24% 11% 0% 3% 0% 5%

MS Excel 14% 23% 6% 14% 23% 3% 3% 2% 8%

MS Power BI 10% 14% 9% 9% 28% 3% 6% 3% 16%

MS SSRS 8% 5% 24% 13% 32% 2% 2% 2% 8%

Oracle BI 14% 14% 20% 6% 9% 0% 6% 6% 11%

Phocas 3% 18% 0% 65% 3% 0% 0% 3% 0%

prevero (prevero) 3% 21% 0% 10% 7% 3% 0% 41% 0%

Pyramid Analytics 19% 17% 14% 11% 8% 0% 3% 6% 6%

Qlik Sense 16% 20% 5% 3% 31% 0% 3% 3% 13%

QlikView 13% 26% 6% 12% 17% 1% 5% 2% 12%

SAP BEx 6% 54% 3% 12% 9% 3% 3% 6% 2%

SAP BO Analysis 15% 38% 9% 3% 9% 9% 6% 3% 6%

SAP BO Design St. 3% 26% 6% 6% 21% 3% 3% 21% 9%

SAP BO WebI 12% 20% 12% 22% 17% 5% 4% 4% 3%

SAS Enterprise BI 56% 0% 17% 0% 6% 6% 3% 0% 6%

Sisense 21% 13% 0% 5% 18% 0% 3% 3% 16%

Tableau 17% 12% 12% 0% 17% 1% 4% 9% 12%

TARGIT 0% 25% 11% 32% 14% 0% 5% 2% 9%

TIBCO Spotfire 15% 19% 0% 4% 27% 0% 4% 12% 15%

Yellowfin 9% 20% 11% 9% 23% 3% 3% 0% 9%

Zoho Reports 2% 16% 4% 11% 36% 7% 2% 0% 13%


- 58 -

Trends in Use by Product

In Figure 57, there are some clear patterns on three trends in widespread use: Self-service BI, visual

analysis and data preparation. Even more interesting though are the outlier scores on less used trends

by product. For example, it is very interesting to see which products are in use for some specific trends

such as collaboration, which increased so much in the last twelve months.

Reading horizontally by product it is impressive to see leadership with Domo on cloud BI, collaboration

and BI with real-time data. Mobile BI has CXO-Cockpit at 80 percent use. Visual design standards has

Bissantz leading with 68 percent, and spatial/location analysis is most used by 59 percent of

respondents with Pyramid Analytics.

Buyers with a strong use case for a very specific trend will read this table with care as they consider

which products are actually widely in use in 2017 for the trends they require. There are some striking

outlier scores where trends have not been adopted by any users of certain products. There are other

trends where the leading scores can be 20 percent higher than the median.


- 59 -

Figure 57: Trends in use by product (n=2,318)

Sensor data

analysis

Text data

analysis

Cloud

BI/BIaaSCollaboration Mobile BI

BI with real-

time data

Self-service

BI

Visual design

standards

Spatial/

Location

analysis

Visual

analysis

Data

preparationEmbedded BI

Bissantz 10% 1% 9% 22% 18% 11% 66% 68% 38% 64% 61% 17%

BOARD 19% 9% 9% 36% 17% 33% 63% 31% 11% 65% 47% 5%

CALUMO 16% 16% 50% 60% 41% 47% 57% 10% 8% 47% 62% 32%

Carriots Analytics (Envision) 24% 20% 38% 68% 19% 37% 50% 20% 26% 60% 54% 38%

Chartio 21% 21% 45% 67% 34% 54% 75% 17% 21% 75% 68% 28%

Cubeware 17% 2% 2% 19% 18% 14% 64% 37% 11% 45% 41% 15%

cubus 19% 9% 9% 29% 9% 15% 68% 26% 18% 34% 50% 13%

CXO-Cockpit 21% 23% 12% 57% 80% 10% 53% 36% 4% 70% 13% 17%

Cyberscience 7% 65% 3% 42% 16% 51% 54% 3% 0% 50% 63% 13%

DigDash 50% 26% 21% 24% 24% 40% 37% 8% 29% 73% 44% 15%

Dimensional Insight 19% 37% 13% 28% 21% 31% 53% 18% 22% 68% 71% 8%

Domo 23% 54% 75% 70% 70% 84% 69% 21% 35% 77% 81% 16%

Dundas 27% 32% 12% 47% 20% 46% 34% 27% 12% 78% 52% 39%

Entrinsik 17% 51% 8% 70% 14% 83% 77% 13% 6% 57% 76% 46%

IBM Cog Analytics 14% 15% 6% 21% 24% 22% 64% 25% 16% 52% 43% 30%

IBM Plan Analytics 14% 11% 10% 31% 6% 15% 52% 33% 3% 41% 68% 8%

Infor 14% 9% 2% 26% 14% 16% 64% 30% 7% 26% 50% 11%

Information Builders 26% 21% 7% 24% 32% 56% 59% 11% 27% 53% 48% 34%

Jedox 9% 3% 18% 24% 18% 21% 59% 30% 12% 24% 37% 11%

Longview Analytics 19% 10% 9% 20% 30% 42% 31% 64% 9% 48% 23% 10%

MicroStrategy 18% 13% 10% 32% 44% 34% 74% 43% 33% 66% 45% 28%

MIK (prevero) 9% 10% 4% 30% 13% 3% 71% 38% 10% 55% 75% 15%

MS Excel 17% 25% 15% 41% 18% 22% 63% 26% 18% 60% 73% 34%

MS Power BI 15% 17% 41% 51% 53% 26% 60% 27% 35% 74% 55% 20%

MS SSRS 18% 18% 6% 20% 18% 35% 39% 23% 13% 53% 29% 28%

Oracle BI 20% 10% 10% 28% 10% 47% 69% 13% 19% 42% 37% 8%

Phocas 37% 45% 23% 64% 67% 27% 75% 0% 18% 63% 57% 30%

prevero (prevero) 12% 11% 18% 33% 11% 32% 62% 32% 4% 20% 47% 18%

Pyramid Analytics 9% 9% 11% 56% 33% 37% 72% 38% 59% 88% 55% 42%

Qlik Sense 24% 31% 21% 47% 44% 27% 69% 33% 32% 76% 62% 36%

QlikView 23% 31% 13% 40% 34% 38% 59% 34% 30% 77% 64% 27%

SAP BEx 8% 5% 7% 13% 13% 10% 48% 23% 6% 27% 52% 19%

SAP BO Analysis 16% 6% 6% 16% 12% 19% 65% 17% 3% 13% 34% 14%

SAP BO Design St. 6% 6% 13% 13% 45% 26% 48% 42% 27% 43% 8% 8%

SAP BO WebI 18% 13% 8% 20% 14% 34% 65% 23% 14% 32% 25% 22%

SAS Enterprise BI 24% 16% 10% 25% 9% 9% 66% 26% 27% 48% 78% 21%

Sisense 28% 18% 31% 46% 28% 39% 47% 17% 25% 68% 65% 37%

Tableau 22% 18% 15% 36% 28% 21% 59% 42% 38% 79% 46% 20%

TARGIT 15% 15% 11% 42% 43% 36% 67% 19% 21% 79% 50% 42%

TIBCO Spotfire 17% 38% 23% 46% 22% 48% 76% 30% 48% 67% 20% 0%

Yellowfin 18% 24% 24% 57% 51% 62% 79% 13% 29% 61% 53% 33%

Zoho Reports 13% 31% 38% 57% 23% 28% 42% 3% 0% 55% 65% 42%


- 60 -

Median Data Volume (GB) in the Databases Used by BI Product

The amount of data used is stunningly different from top to bottom, but the markets addressed are quite

different too. SAS Enterprise BI, as its name suggests, is used by large enterprises, whereas Zoho

Reports is used by smaller businesses (ZOHO is an acronym for zero-office-home-office).

Of most interest are the bands of median database volume. For example, only five products have a

median over 2000 GB. Just four are between 1000 and 2000 GB. Four again are between 1000 GB and

500 GB. Thirteen products have a median between 100 GB and 500 GB. Nine are between 50 GB and

100 GB, and eight further products lie below the 50 GB mark.

Figure 58: What is the amount of data in the databasesused with your BI

product? (median GB), by product (n=1,828)

Median GB

SAS Enterprise BI 3964

MicroStrategy 2786

SAP BEx 2571

SAP BO Design St. 2250

TIBCO Spotfire 2100

SAP BO WebI 1526

Information Builders 1500

Domo 1125

IBM Cog Analytics 1010

Oracle BI 719

SAP BO Analysis 703

Longview Analytics 563

MS SSRS 500

Dimensional Insight 375

Pyramid Analytics 342

TARGIT 289

Average of all products 212

QlikView 193

Cyberscience 175

CALUMO 163

Bissantz 155

Yellowfin 155

Chartio 146

Tableau 125

Sisense 118

Cubeware 108

Infor 75

Entrinsik 72

cubus 69

prevero (prevero) 65

Dundas 62

IBM Plan Analytics 55

Phocas 55

CXO-Cockpit 53

MS Excel 53

Carriots Analytics (Envision) 27

BOARD 25

Qlik Sense 21

DigDash 18

MS Power BI 14

MIK (prevero) 7

Jedox 6

Zoho Reports 1


- 61 -

Clearly some of this distribution matches the population of organizations by size, where the larger an

organization is by revenue, or employees for example, the fewer of those organizations may exist as a

viable market for products. However, there are other interesting implications for data scale in use too,

where it is easier for products to address smaller data sizes, and more of them may succeed in the

market due to valuable comparative advantage by vertical, use case trend, geography, or other

capabilities.

Recommendation by Product

In considering the scores for recommendation, it is worth remembering that the vast majority of BI users

experience material success with the products they employ. Not everything is perfect, but their

satisfaction rates are high and many business benefits are achieved.

Figure 59: Recommendation by product (n=2,371)

Definitely Probably Maybe Probably not Definitely not

Bissantz 67% 29% 2% 2% 0%

BOARD 63% 25% 4% 8% 0%

CALUMO 65% 29% 6% 0% 0%

Carriots Analytics (Envision) 78% 16% 6% 0% 0%

Chartio 63% 33% 5% 0% 0%

Cubeware 36% 49% 7% 6% 1%

cubus 83% 11% 3% 3% 0%

CXO-Cockpit 74% 23% 3% 0% 0%

Cyberscience 55% 30% 13% 0% 3%

DigDash 78% 19% 0% 3% 0%

Dimensional Insight 84% 13% 3% 0% 0%

Domo 73% 17% 3% 7% 0%

Dundas 49% 40% 9% 3% 0%

Entrinsik 67% 31% 2% 0% 0%

IBM Cog Analytics 27% 39% 18% 13% 3%

IBM Plan Analytics 56% 31% 6% 6% 0%

Infor 13% 51% 33% 2% 0%

Information Builders 52% 33% 9% 3% 3%

Jedox 53% 44% 3% 0% 0%

Longview Analytics 53% 34% 9% 4% 0%

MicroStrategy 57% 34% 7% 2% 1%

MIK (prevero) 38% 54% 3% 3% 3%

MS Excel 48% 32% 16% 5% 0%

MS Power BI 63% 29% 6% 1% 0%

MS SSRS 54% 32% 10% 3% 2%

Oracle BI 17% 40% 34% 6% 3%

Phocas 94% 6% 0% 0% 0%

prevero (prevero) 62% 28% 10% 0% 0%

Pyramid Analytics 67% 22% 11% 0% 0%

Qlik Sense 75% 23% 2% 0% 0%

QlikView 65% 28% 6% 1% 0%

SAP BEx 3% 52% 22% 17% 6%

SAP BO Analysis 26% 41% 24% 9% 0%

SAP BO Design St. 32% 47% 12% 6% 3%

SAP BO WebI 17% 43% 25% 14% 0%

SAS Enterprise BI 39% 36% 14% 6% 6%

Sisense 58% 34% 3% 5% 0%

Tableau 57% 28% 11% 4% 0%

TARGIT 70% 27% 0% 2% 0%

TIBCO Spotfire 52% 22% 19% 0% 7%

Yellowfin 73% 19% 8% 0% 0%

Zoho Reports 64% 20% 13% 2% 0%


- 62 -

While there are some interesting variances to note, it is perhaps most useful to have Figure 59 as a

confirmation that most users would recommend the product they have been using. Indeed, every product

has a clear majority of respondents who would ‘definitely’ or ‘probably’ recommend the product. It is also

wise to remember that most users suggest a focus on functionality as the major factor in choosing a

product.

Reasons to Buy by Product

Figure 60 shows the reasons why organizations buy particular BI products. Functionality is dominant,

and then the picture becomes more variable by reason and by product. Among the least important

reasons appears to be whether the software has an international focus.

For some buyers, this chart may reinforce their perception of a product. For others, it may introduce new

ways of thinking about their options, or about the behavior of others, who may need to choose low price

over functionality. It may even be the case that there is no clear single reason why a product is chosen.

The BI Survey lens is here seen in one of its broadest applications, and Figure 60 repays close study

by reason and by product.

Finally, remember that a buyer may well have many more than one reason for buying a product.


- 63 -

Figure 60: Reasons to buy by product (n=2,157)

Functio

nality

Suppor

t for l

arge

numbers

of u

sers

Large

data handlin

g

capa

city

Eas

e of u

se fo

r

report

des

igner

s

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e of u

se fo

r

report

recipie

nts

Fast q

uery

perfo

rman

ce

Pre

defin

ed d

ata

connec

tion

Hig

h innova

tive

capa

city

of t

he

vend

or

Ven

dor or pro

duct

reputa

tion

Size/

finan

cial

stab

ility

Flexi

bility

Inte

rnat

ional

focu

s

of the

softwar

e

Pric

e-per

form

ance

ratio

Corp

orate

sta

ndard

Avai

labili

ty o

f loca

l

support

Pro

of of c

oncept'

fast

er o

r bet

ter

Ven

dor relatio

nship

Bundle

d with

anoth

er pro

duct

Deplo

ymen

t optio

n

Avai

labili

ty o

f

skilled p

eople

Bissantz 62% 9% 15% 22% 49% 38% 33% 48% 30% 4% 30% 4% 28% 6% 19% 14% 10% 1% 0% 24%

BOARD 50% 10% 10% 46% 44% 32% 28% 10% 6% 2% 56% 0% 40% 4% 24% 16% 14% 2% 2% 10%

CALUMO 59% 16% 14% 51% 24% 27% 3% 14% 11% 0% 27% 3% 68% 0% 35% 24% 19% 0% 8% 19%

Carriots Analytics (Envision) 25% 13% 9% 31% 41% 25% 16% 9% 22% 13% 47% 6% 16% 0% 19% 13% 6% 9% 9% 9%

Chartio 61% 17% 25% 44% 44% 25% 42% 0% 0% 3% 39% 6% 44% 0% 17% 11% 6% 0% 11% 17%

Cubeware 50% 12% 15% 18% 37% 18% 37% 10% 10% 5% 37% 3% 67% 5% 23% 12% 7% 10% 2% 13%

cubus 69% 22% 13% 38% 44% 53% 9% 22% 6% 3% 63% 0% 56% 0% 28% 22% 3% 0% 3% 31%

CXO-Cockpit 42% 16% 6% 65% 77% 42% 61% 26% 6% 0% 35% 0% 23% 0% 3% 32% 0% 0% 6% 6%

Cyberscience 26% 9% 12% 62% 38% 53% 53% 3% 9% 0% 32% 0% 44% 3% 3% 18% 0% 9% 0% 6%

DigDash 50% 3% 23% 57% 47% 43% 30% 10% 3% 0% 37% 13% 63% 3% 40% 17% 3% 3% 10% 7%

Dimensional Insight 52% 10% 31% 34% 31% 52% 3% 17% 24% 3% 41% 0% 52% 3% 28% 21% 17% 0% 10% 14%

Domo 41% 17% 34% 55% 59% 34% 55% 14% 3% 10% 31% 3% 21% 0% 7% 21% 7% 3% 14% 3%

Dundas 45% 23% 16% 55% 29% 29% 26% 6% 13% 3% 52% 3% 61% 0% 16% 19% 0% 0% 19% 13%

Entrinsik 30% 13% 11% 68% 47% 15% 43% 2% 15% 0% 32% 0% 62% 4% 17% 9% 15% 6% 2% 11%

IBM Cog Analytics 59% 33% 32% 16% 19% 18% 14% 14% 30% 28% 14% 14% 19% 22% 8% 15% 8% 9% 3% 9%

IBM Plan Analytics 52% 28% 34% 21% 31% 69% 17% 7% 10% 3% 52% 10% 14% 7% 21% 17% 3% 7% 3% 14%

Infor 51% 7% 7% 24% 32% 24% 37% 5% 5% 7% 39% 7% 46% 0% 29% 17% 10% 5% 0% 10%

Information Builders 68% 29% 26% 29% 29% 10% 26% 3% 16% 10% 52% 0% 55% 10% 23% 29% 10% 0% 0% 6%

Jedox 50% 3% 9% 32% 53% 26% 26% 24% 6% 0% 53% 0% 88% 0% 18% 18% 6% 0% 6% 18%

Longview Analytics 63% 11% 9% 37% 39% 17% 65% 11% 11% 0% 59% 0% 28% 9% 17% 24% 2% 4% 4% 17%

MicroStrategy 68% 39% 47% 26% 28% 40% 22% 18% 17% 10% 28% 9% 18% 12% 14% 15% 3% 2% 4% 13%

MIK (prevero) 53% 3% 7% 10% 33% 40% 33% 10% 7% 7% 53% 0% 57% 0% 27% 10% 17% 0% 0% 43%

MS Excel 28% 14% 7% 31% 40% 16% 13% 5% 8% 8% 35% 4% 52% 27% 7% 2% 3% 19% 3% 46%

MS Power BI 47% 12% 8% 41% 35% 25% 31% 13% 11% 8% 25% 2% 70% 14% 8% 8% 8% 20% 14% 23%

MS SSRS 25% 12% 14% 32% 26% 19% 19% 7% 16% 18% 21% 4% 54% 21% 11% 2% 4% 51% 5% 39%

Oracle BI 30% 21% 30% 33% 18% 24% 27% 6% 24% 15% 24% 3% 21% 21% 21% 15% 3% 12% 6% 21%

Phocas 55% 21% 15% 55% 48% 64% 30% 12% 15% 0% 33% 0% 36% 0% 24% 15% 12% 6% 3% 0%

prevero (prevero) 61% 11% 14% 25% 46% 25% 29% 7% 7% 7% 79% 0% 57% 0% 14% 11% 0% 0% 0% 14%

Pyramid Analytics 64% 19% 19% 47% 47% 22% 17% 11% 11% 3% 28% 0% 75% 6% 19% 25% 3% 0% 19% 17%

Qlik Sense 52% 25% 33% 42% 32% 60% 17% 10% 10% 7% 42% 8% 32% 15% 5% 17% 7% 2% 8% 18%

QlikView 59% 19% 34% 34% 44% 70% 24% 12% 9% 3% 44% 3% 28% 6% 9% 13% 5% 2% 1% 11%

SAP BEx 38% 30% 13% 2% 4% 8% 55% 9% 21% 32% 17% 13% 19% 42% 8% 2% 2% 28% 2% 9%

SAP BO Analysis 42% 6% 19% 16% 23% 10% 48% 16% 19% 32% 13% 6% 16% 42% 13% 6% 3% 23% 3% 29%

SAP BO Design St. 67% 30% 7% 33% 37% 20% 40% 17% 23% 23% 37% 17% 7% 30% 0% 3% 3% 23% 3% 7%

SAP BO WebI 43% 26% 7% 32% 35% 19% 25% 3% 26% 21% 13% 4% 15% 40% 13% 6% 3% 13% 1% 19%

SAS Enterprise BI 62% 31% 23% 12% 12% 38% 31% 12% 31% 12% 42% 12% 12% 27% 23% 12% 4% 8% 0% 23%

Sisense 68% 5% 24% 68% 32% 30% 30% 16% 8% 5% 46% 5% 46% 3% 14% 30% 8% 0% 19% 14%

Tableau 55% 15% 18% 49% 46% 28% 28% 23% 20% 8% 27% 6% 24% 8% 14% 24% 8% 1% 7% 17%

TARGIT 61% 16% 8% 45% 39% 37% 34% 16% 21% 5% 29% 3% 47% 0% 13% 18% 16% 5% 3% 13%

TIBCO Spotfire 62% 38% 19% 35% 42% 19% 19% 4% 8% 8% 54% 4% 19% 12% 8% 15% 8% 4% 4% 8%

Yellowfin 56% 19% 8% 58% 56% 6% 19% 19% 0% 3% 25% 0% 81% 0% 17% 14% 6% 6% 22% 11%

Zoho Reports 34% 20% 7% 41% 39% 11% 14% 14% 11% 14% 36% 9% 66% 0% 9% 5% 11% 27% 18% 18%


- 64 -

Project Success by Product

To reinforce the positive experiences seen in Figure 59, the scores in Figure 61 indicate the widespread

project success experienced by the vast majority of users. There are very few low scores in this table.

Figure 61: Project success surveyed by product (n=2,316; 10=good, 0=poor)

User

satisfaction

with

implementation

of technical

aspects

User

satisfaction

with

implementation

of business

aspects

Satisfaction of

administrators

with technical

implementation

Completion

within the

timeframe

originally

specified

Completion

within the

budget

originally set

Bissantz 9.3 9.3 8.9 7.9 8.2

BOARD 8.7 8.4 7.9 7.3 7.5

CALUMO 8.4 8.0 8.1 7.4 6.6

Carriots Analytics (Envision) 8.4 8.1 8.3 8.1 8.0

Chartio 8.6 8.1 7.5 7.0 6.8

Cubeware 8.2 8.1 8.1 7.3 7.6

cubus 9.0 9.7 9.0 8.9 8.8

CXO-Cockpit 8.9 9.5 8.9 8.9 8.5

Cyberscience 8.6 8.9 8.3 8.0 7.4

DigDash 9.2 8.6 8.3 8.3 8.3

Dimensional Insight 8.6 8.4 8.6 8.3 7.8

Domo 8.2 8.5 8.7 6.7 7.0

Dundas 9.0 9.0 8.6 7.7 7.1

Entrinsik 8.7 8.0 8.1 7.3 7.5

IBM Cog Analytics 6.7 7.2 6.7 6.1 5.7

IBM Plan Analytics 8.9 9.2 8.3 7.3 6.9

Infor 7.9 8.8 7.7 7.2 7.8

Information Builders 8.3 8.0 8.0 7.6 7.4

Jedox 8.7 9.1 8.1 8.2 8.7

Longview Analytics 9.1 9.4 8.5 8.3 7.6

MicroStrategy 8.4 8.3 8.4 6.9 7.2

MIK (prevero) 8.9 9.2 8.1 7.7 8.9

MS Excel 7.9 7.6 6.5 6.8 7.0

MS Power BI 8.3 8.3 7.1 7.2 7.7

MS SSRS 8.2 7.9 7.5 7.1 7.9

Oracle BI 7.1 6.9 6.3 6.1 5.9

Phocas 9.6 9.7 9.7 9.1 9.0

prevero (prevero) 9.1 9.8 9.1 7.8 7.8

Pyramid Analytics 8.6 8.5 9.4 8.9 9.4

Qlik Sense 8.7 9.0 7.8 8.5 7.9

QlikView 9.0 8.9 8.3 7.6 7.6

SAP BEx 6.4 6.8 5.6 5.2 5.3

SAP BO Analysis 7.6 7.8 7.2 6.0 6.9

SAP BO Design St. 7.9 7.9 6.3 6.0 6.8

SAP BO WebI 6.3 7.2 6.3 5.3 6.1

SAS Enterprise BI 7.4 7.4 6.1 4.4 4.6

Sisense 8.9 8.2 8.3 8.3 7.9

Tableau 8.1 8.1 7.0 7.6 6.7

TARGIT 9.0 9.1 8.4 8.3 8.0

TIBCO Spotfire 7.8 8.1 6.9 7.6 5.4

Yellowfin 9.3 9.2 9.1 9.3 9.6

Zoho Reports 8.8 8.4 8.1 8.0 8.1


- 65 -

The lowest scores in Figure 61 are still close to a 5.0 (the lowest of all is a single 4.4). For many vendors

the lowest score is above 7. Most notable is the pattern of highest scores for satisfaction both with the

technical and business aspects of projects.

Competition Trend

In Figure 62, there is a fascinating view of the market position of “most significant competitors”. Tableau

stands out, with close to 400 percent growth in competitive position from 2012 to 2017. Some of this

may be due to functionality, and good scores for ease of use, or a successful go-to-market strategy. But

while the causes will be a matter for debate, the fact remains that only one vendor is present with a

score over 50 percent, and the next score is 20 percent lower.

Despite this competitive presence, most other vendors are highly competitive and win most of the time.

Figure 62: Which vendors are the most significant competitors over time

(n=changing basis); Only vendors and resellers were asked

Product BI Survey 17 BI Survey 16 BI Survey 15 BI Survey 14 BI Survey 13 BI Survey 12 BI Survey 10 BI Survey 9

Tableau 56% 49% 40% 33% 20% 12%

Qlik 36% 49% 45% 40% 41% 39% 41% 21%

Microsoft 36% 30% 21% 23% 20% 23% 22% 26%

SAP 15% 21% 26% 30% 33% 36% 35% 32%

IBM 11% 16% 18% 20% 27% 32% 30% 28%

Oracle 10% 16% 14% 16% 17% 23% 27% 28%

MicroStrategy 9% 12% 11% 13% 14% 16% 15% 14%

SAS 6% 6% 8% 9% 6% 6% 7% 7%

BOARD 6% 4% 5% 5% 5% 5% 4% 2%


- 66 -

Summary

The BI market is immensely rich and varied. It is full of opportunities for buyers to explore, and for

vendors to exploit.

The business benefits examined at the beginning of this report will provide an impetus for continued

investment, growth and innovation. As the use cases for BI expand and become better understood,

there may be increasing consensus in how to use BI on the data generated in every organizational

function.

Beyond those simple and more obvious use cases, new drivers of value will be discovered by joining

data from different business functions such as customer lifetime value joining finance, services, sales

and marketing.

The trends of new product capabilities such as cloud will make it easier to deploy BI for niche edge

cases, where the value may be highly speculative. Advanced analysis and machine learning push at

entirely different and new boundaries where business cases and value are being imagined and explored

in utterly original ways.

As the data around us grows exponentially, so does the need for ever more BI. As this wave of rich and

valuable data fertilizes new opportunities all around us, BARC’s BI Survey will continue to serve as a

guide to this fascinating market as it stands, enabling many millions of people to make better decisions.


- 67 -

Authors of The BI Survey 17

Dr. Carsten Bange

Founder & CEO

BARC

Dr. Christian Fuchs

Senior Analyst

BARC

Larissa Seidler

Senior Analyst

BARC

Robert Tischler

Senior Analyst

BARC

Nikolai Janoschek

Research Analyst

BARC

Emmanuel Lartigue

Senior Analyst

CXP

Henry Eckerson

Research Analyst

Eckerson Group

Chris von Simson

Research Analyst

Eckerson Group

Copyright © BARC GmbH 2017. All rights reserved.

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