13 BI SURVEY Partici pan

Transcription

The BI Survey 13 – Complimentary Participant Summary
THE
1
BI SURVEY
13
The Customer Verdict
The world’s largest survey of
business intelligence software users
A summary of key findings from The BI Survey 13
produced exclusively for Survey participants
Participant Summary
Not to be quoted without permission from the publisher.
2
Table of contents
Survey background ................................................................................................... 3
Objectives for the data sample............................................................................... 3
A large and varied sample ..................................................................................... 3
Unbiased ............................................................................................................... 4
The Sample ............................................................................................................... 4
Sample size and make-up ..................................................................................... 5
Geographical distribution ....................................................................................... 6
Organization sizes by headcount ........................................................................... 6
Featured products ..................................................................................................... 9
Peer groups ......................................................................................................... 13
Vertical markets and products by industry ........................................................... 16
Business Benefits and the BBI ................................................................................ 18
BBI by peer group and vendor ............................................................................. 20
Goal Achievement ................................................................................................... 27
Deployment ............................................................................................................. 31
Usage in different company sizes ........................................................................ 31
Percentage of BI users in companies................................................................... 34
BI usage by department ....................................................................................... 36
The selection process ............................................................................................. 38
Reasons to buy .................................................................................................... 38
Implementing BI and Satisfaction ............................................................................ 43
Implementation time ............................................................................................ 43
Support ................................................................................................................ 46
Problems in BI projects ........................................................................................ 49
Trending topics in BI................................................................................................ 52
Our approach to analyzing trends ........................................................................ 53
The frequent inaccuracy of plans ......................................................................... 53
Status and plans for trending BI topics................................................................. 54
2
3
Survey background
The BI Survey 13 follows on from 11 successful editions of The BI - and former
OLAP - Surveys.
This edition has a wider range of products than in previous years, including not only
products from the well-known BI giants, but also specialist products from much
smaller vendors and open source vendors.
The BI Survey provides a detailed quantitative analysis of why customers buy BI
tools, what they use them for, how successful they are and why they eventually
abandon them. The Survey is based on the analysis of the real-world experience of
3,149 respondents.
The value of a survey like this depends on having a sufficiently large, well-distributed
and unbiased sample. This section describes the characteristics of the people who
took part in the study and how we recruited them.
Objectives for the data sample
We had a number of specific objectives when compiling the sample. It must:

Be large, for statistical reliability

Include viable samples from as many BI products as possible

Be well distributed

Be unbiased.
A large and varied sample
The BI Survey 13 has the largest sample of any survey of business intelligence
users available on the market. While a sample of 500 respondents may seem
impressive and statistically acceptable, the problem comes when trying to compare
sub-samples for, say, individual products.
The BI Survey has a rule that, as far as possible, only sub-samples containing 30 or
more data points should be reported. It is easy to get sub-samples larger than this
for the more widely-used products, but less easy for others. Sometimes it is
surprisingly difficult to find viable sample sizes for products even from large vendors,
such as Oracle. This means that the overall sample needs to be at least 1000 in
order to obtain useful sub-samples.
4
Unbiased
To produce unbiased results we encouraged all vendors to promote The Survey,
eliminating the risk of a small number of vendors encouraging their favored
customers to participate without our knowledge. This year a number of vendors
promoted The BI Survey 13 through their public websites, and many emailed not just
their customers, but also their prospects.
It transpires that many vendors’ mailing lists include not just their own customers, but
also prospective customers who may well be current or previous users of other
vendors’ products. This meant that we obtained adequate samples even from
customers of vendors who did not promote The Survey.
We thank the vendors for the professional way in which they collaborated in this
venture, and none attempted to influence the questionnaire or the analysis and
presentation of the data.
We are always aware that some vendors could be tempted to enter data themselves,
purporting to be genuine customers. Vendors are warned that if we discover or
examples of this practice, all entries that come via their invitation will be removed
from The Survey.
We apply increasingly stringent data cleansing rules, using a number of different
tests. This year we detected an increase in the number of examples of suspect data
that purported to be from user sites. All such data was removed from the sample.
The Sample
Most surveys are conducted or sponsored by an organization based in, and focused
on, one country. This means that most of the sample tends to be from that country.
However, BI is a worldwide market and we wanted, as far as possible, to capture a
large international sample. This not only presents a more accurate global picture, but
also allows international variations to be analyzed.
The three largest BI markets are the US, Germany and the UK, so The BI Survey 13
was produced as a collaboration between organizations in each of these countries,
and in partnership with publishers and vendors in these and other countries. It
features not just the well-known US products, but also products from other regions
including Europe and Australia.
4
5
Europe
64%
North America
23%
Asia and Pacific
9%
ROW
2%
South America
2%
Figure 1: Respondents anal yzed b y region (n= 2297)
The net result was an extraordinarily international panel. Respondents were located
in 69 countries. Five countries had 100 or more respondents, and eight had 50 or
more; 25 countries had ten or more respondents.
Sample size and make-up
Hundreds of thousands of people around the world were invited to participate in The
BI Survey 13, using dozens of email lists, magazines, social media and websites. As
in previous years, the questionnaire offered different sets of questions for vendors
and users (or consultants answering on behalf of a user). This seems to produce
better quality data as in the past some vendors pretended to be users when they
saw they were not being asked relevant questions. Participants from last year who
indicated that they would like to be part of our panel received a prefilled
questionnaire with answers from last year’s questions that had remained the same.
They were asked to update their responses, and then to answer the new questions
in this year’s Survey.
The results of the online data collected are shown in the following chart, with the
numbers of responses removed also displayed.
Total responses
Filtered during data cleansing
Remaining after data cleansing
Not yet considered buying
Total answering questions
Responses
3149
100%
-175
-6%
2974
94%
-106
-3%
2868
91%
Figure 2: Responses removed from the samples
The number of responses is split between users, consultants and vendors. Vendors
answered a different set of questions to those answered by end users. This
document focuses on the analysis of the user results.
6
User
Consultant
All users
Vendor/Reseller
Responses
2015
283
2298
570
70%
10%
80%
20%
Figure 3: Response t ypes for final anal ysis
Geographical distribution
One of the key objectives of The Survey is to achieve a geographically balanced
sample that reflects the current global market for BI products. Therefore we cover
four languages in the online questionnaire: English, German, French and Spanish.
Having a geographically-balanced sample has two major benefits:
Firstly, results of The Survey are more closely representative of the world market,
rather than being largely based on US experience, as is the case with many other
surveys.
In regions where knowledge of English is sparse, such as South America and much
of Asia and southern Europe, it is difficult to get good coverage and the BI market is
less mature in these countries. Since the fourth edition of The BI Survey, we have
significantly boosted the German sample by specifically targeting users in Germanspeaking countries, using a fully translated on-line questionnaire. A Spanish
language questionnaire was included to boost responses from Spain and Latin
America. We also used a French questionnaire, increasing our European coverage
even more.
Organization sizes by headcount
BI products are most commonly found in large organizations and a high percentage
of the responses we receive are from users in companies with more than 2500
employees. Nevertheless, responses from small organizations have been catching
up over the years.
The split between respondents from small and large enterprises is well-balanced this
year.
6
7
Less than 100
14%
100 - 2500
More than 2500
53%
33%
Figure 4: Frequenc y of emplo yee count in respondent organization (n=2266)
The following chart shows the median headcount of respondents’ companies
analyzed by the product they reported on. Of the products defined in the ‘Enterprise
Reporting’ and ‘Large International Vendors’ peer groups there was a higher median
number of employees in customer organizations than the sample average.
8
Oracle Essbase
SAP BO WebI
Oracle BI Foundation Suite
SAP BW
SAS
MicroStrategy
Arcplan
IBM Cognos TM1
IBM Cognos BI
Infor
Information Builders
Tableau
QlikTech
Microsoft SSAS
Microsoft SSRS
All
Pyramid
Cubeware
BOARD
Bissantz
Cyberscience
Dimensional Insight
Microsoft Excel
Jedox
Evidanza
TARGIT
Pentaho
Corporate Planning
Logi Analytics
Phocas
Yellowfin
9,500
5,000
4,500
4,500
3,750
3,500
3,490
3,000
2,250
1,600
1,600
1,300
1,225
1,000
1,000
1,000
838
800
690
600
500
500
500
400
275
250
225
200
150
125
88
Figure 5: Median emplo yee count of user organizations anal yzed b y product
Here we see one outlier on the high side: Oracle Essbase which is deployed in large
enterprises in our sample.
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9
Featured products
When grouping and describing the products featured in The BI Survey, we did not
strictly follow the naming conventions that the vendors use. In some cases, we
combined various products to make analysis more convenient. In those cases, we
named these groups of products as shown in Figure 6. Note that the names we use
in this document are our own and are not always the official product names used by
the vendors.
One of the key reasons for this is that the products we analyze are not necessarily
the latest version of the tool. Vendors often change the product name between
versions, making it difficult to have a single official name for several versions of the
same product.
Another reason is that we sometimes bundle related products into a single group to
increase the sample size, even if the vendor prefers to view them as distinct for
marketing reasons. In both of these cases, the point is not to challenge the naming
conventions of the vendor, but simply to reduce the complexity of the Survey findings
for the convenience of the reader. In some cases, we also shorten the names of the
products to improve the formatting of the charts.
We asked respondents explicitly about their experiences with products from a predefined list, with the option to nominate other products. This list is updated each year
and is based on the sample size of the products in the previous year, as well as
additional new products that we see in the market. In cases where respondents said
they were using an ‘other’ product, but from the context it was clear that they were
actually using one of the listed products, we reclassified their data accordingly.
We solicited responses on all surviving products with more than a minimal response
in the last Survey, plus a few others whose numbers have potentially grown to the
point where there is enough data to be analyzed.
The following table shows the products that we included in the detailed analysis. The
number of the ‘other’ responses is also included in this chart.
10
Product labels
Detailed product list
Respondents
Bissantz
arcplan Enterprise
insight/dynaSight
Bissantz DeltaMaster
BOARD
BOARD
74
Corporate Planning
Corporate Planner
34
Cubeware
Cubeware Cockpit
88
Cyberscience
Cyberscience Cyberquery
60
Dimensional Insight
Diver Solution
61
Evidanza
Evidanza
29
arcplan
IBM Cognos BI
IBM Cognos TM1
IBM Cognos BI (Cognos 8 and 10)
IBM Cognos Insight
IBM Cognos TM1
IBM Cognos Express
98
92
96
51
Infor
Infor10 ION BI
85
Information Builders WebFOCUS
52
Jedox
Logi Analytics
Microsoft Excel
Jedox Base
Jedox Premium
Logi Ad Hoc
Logi Info
Microsoft Excel (Excel only, with no
additional database)
127
41
44
Microsoft SQL Server Analysis
Services (SSAS) Ecosystem:
Microsoft Excel with MS SSAS
Microsoft SSAS
Microsoft PivotTables Services
145
Microsoft SharePoint Server Excel
Services
Microsoft SQL Server Reporting
Services (SSRS) with SSAS
Microsoft SSRS
Microsoft SQL Server Reporting
Services (SSRS)
50
MicroStrategy
MicroStrategy
128
Oracle Essbase
Oracle Essbase
Oracle Hyperion Planning
54
(including OBIEE)
47
Pentaho
Pentaho
36
Phocas
Phocas Professional
Phocas Express
68
Pyramid
Pyramid Analytics
45
QlikTech
QlikTech QlikView
178
SAP BO WebI
SAP BusinessObjects Web
Intelligence
71
10
11
SAP BW
SAP Business Explorer (BEx)
SAP BW Integrated Planning (SAP
BW IP)
SAP BW
103
SAP Web Application Designer
(WAD)
SAS Base
SAS Enterprise BI
SAS
36
SAS Visual Analytics
Tableau
Tableau Desktop
48
TARGIT
TARGIT BI Suite
62
Yellowfin
Yellowfin
30
TOTAL
2133
Others
See complete list
133
Figure 6: Products included in the sample (excluding don’t know )
The last five years have seen an increase in the proportion of German respondents.
This is partly due to cooperation with German vendors and the presence of strong
German subsidiaries of international vendors, reflected through products like
arcplan, Bissantz, BOARD, Cubeware and SAP.
Furthermore, this year we also included some new products such as Logi Analytics,
Pyramid Analytics and Corporate Planning.
The following table contains the products that had responses but are not included in
the detailed analysis. In the BI Survey Analyzer these products are grouped together
under the label ‘Others’.
12
Other products
Actuate BIRT Report Studio/ActuateOne
Adaptive Planning
Decisyon
Dodeca
Host Analytics
Jaspersoft
MeLLmo Roambi
Microsoft PowerPivot
Microsoft Power View
MIK
MyReport
Panorama
Prevero
Prevero Professional Planer (formerly Winterheller)
Prophix
Quantrix Modeler
Reportive
SAP BusinessObjects Analysis (for OLAP or Office)
SAP BusinessObjects Planning and Consolidation
SAP BusinessObjects Crystal Reports
SAP BusinessObjects Dashboard (formerly Xcelsius)
SAP BusinessObjects Desktop Intelligence
SAP BusinessObjects Explorer
Tagetik
Tibco Spotfire
Other BI product
Figure 7: Products in the sample but not in the detailed anal ysis
12
13
Peer groups
This year we reduced the number of peer groups in The Survey. Peer groups are
used to ensure similar products are compared against each other both in fairness to
the vendor and for the benefit of the end user. The groups are essential to allow fair
and useful comparisons of products that are likely to compete. The peer groups are
primarily based on the results from The Survey, how customers say they use the
product and our knowledge of the products.
14
Products by peer group
Arcplan
Bissantz
BOARD
Corporate Planner
Cubeware
Cyberscience
Dimensional Insight
Evidanza
IBM Cognos BI
IBM Cognos TM1
Infor
Jedox
Logi Analytics
Microsoft Excel
Microsoft SSAS
Microsoft SSRS
MicroStrategy
Oracle Essbase
Pentaho
Phocas
Pyramid
QlikTech
SAP BW
SAP BO WebI
SAS
Tableau
TARGIT
Yellowfin
Large international
vendors
Enterprise
Reporting
x
Dashboard
x
Ad-hoc analysis
OLAP Analysis
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Visual Analysis &
Data Discovery
x
Performance
Management
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Figure 8: Products b y peer group matrix
x
x
x
x
x
x
x
x
14
15 The BI Survey 13 – Complimentary Participant Summary
Peer groups are simply a guide to the reader to help make the products easier to
understand and to show why individual products return such disparate results. They
are not intended to be a judgment of the quality of the products. Some products
appear in more than one peer group.
The peer groups are defined using the criteria described in the following table. These
peer groups are used in a consistent way in our analysis as well as in The BI Survey
Analyzer.
Peer group
Large International Vendors
Description
The ‘Large International Vendors’ peer group includes
companies with annual revenues of $200m+ and a truly
international reach
Enterprise Reporting
The ‘Enterprise Reporting’ peer group includes products
that can provide standard formatted reporting in a large
scale enterprise situation (i.e. thousands of users served
by a scalable reporting server)
Dashboard
The ‘Dashboard’ peer group includes products that are
focused on creating advanced dashboards
The ‘Ad-hoc Analysis’ peer group includes products that
are focused on ad-hoc analysis
The ‘OLAP Analysis’ peer group includes products that
support analysis in dimensional and hierarchical data
models, usually supported by dimensional databases
OLAP Analysis
(e.g. financial controlling, performance measurement
systems) providing self-service capabilities to business
users
The ‘Visual Analysis & Data Discovery’ peer group
includes products that provide advanced visualization
features (e.g. heat maps, scatter plots). Usually these
Visual
Analysis
&
Data
tools support set-based analysis to filter and analyze
Discovery
properties of data sets with many records and many
attributes (e.g. customer segmentation for campaign
management)
The ‘Performance Management’ peer group includes
Performance Management
products that serve companies whose projects are
predominantly planning projects
Ad-hoc Analysis
Figure 9: Peer group descriptions
The BI Survey 13 – Complimentary Participant Summary 16
Vertical markets and products by industry
Our analysis shows that some products have a distinct vertical bias. The following
table provides an overview of product use by industry sector. Experience shows that
Financial Services
Manufacturing
Public sector
Retail & Wholesale
Services
Telecommunications
Transport
Utilities
Other
these values tend to vary from year to year.
Arcplan
4%
51%
8%
6%
17%
0%
4%
5%
4%
Bissantz
2%
48%
0%
18%
18%
0%
1%
2%
10%
BOARD
8%
57%
7%
5%
8%
1%
4%
3%
7%
Corporate Planning
0%
32%
6%
15%
24%
0%
6%
6%
12%
Cubeware
8%
44%
5%
13%
22%
1%
5%
0%
3%
Cyberscience
2%
63%
3%
3%
20%
3%
2%
0%
3%
Dimensional Insight
10%
23%
2%
23%
26%
0%
5%
0%
11%
Evidanza
0%
38%
0%
24%
31%
0%
3%
3%
0%
IBM Cognos BI
18%
32%
6%
9%
25%
2%
4%
1%
2%
IBM Cognos TM1
10%
29%
2%
10%
12%
6%
18%
6%
8%
Infor
7%
49%
0%
8%
12%
1%
6%
7%
9%
15%
19%
19%
4%
23%
2%
8%
4%
6%
Jedox
5%
35%
6%
9%
36%
2%
3%
2%
4%
Logi Analytics
7%
39%
5%
5%
22%
5%
7%
5%
5%
Microsoft Excel
11%
34%
5%
9%
20%
2%
5%
9%
5%
Microsoft SSAS
12%
38%
6%
7%
22%
5%
3%
4%
3%
Microsoft SSRS
16%
42%
8%
2%
16%
2%
0%
8%
6%
MicroStrategy
23%
22%
3%
16%
19%
7%
2%
4%
5%
Oracle Essbase
11%
30%
6%
22%
15%
9%
2%
2%
4%
17%
23%
6%
15%
21%
4%
6%
4%
2%
Pentaho
8%
25%
3%
8%
36%
8%
3%
6%
3%
Phocas
0%
49%
0%
37%
9%
1%
3%
0%
1%
Pyramid
42%
27%
0%
4%
20%
2%
0%
0%
4%
QlikTech
7%
41%
4%
12%
25%
2%
5%
2%
2%
SAP BW
8%
36%
5%
9%
17%
2%
7%
12%
5%
SAP BO WebI
20%
18%
7%
11%
14%
10%
8%
7%
4%
SAS
33%
14%
14%
0%
28%
0%
3%
3%
6%
Tableau
19%
13%
4%
4%
40%
8%
6%
4%
2%
TARGIT
3%
35%
6%
19%
13%
0%
5%
8%
10%
Yellowfin
7%
43%
3%
7%
27%
0%
3%
3%
7%
Total average
11%
36%
5%
11%
21%
3%
4%
4%
5%
Figure 10: Product use anal yzed b y industr y (n=2133)
Notable results from the ‘product use analyzed by industry’ table:

The manufacturing sector has the highest total average usage across all
products of 36%. This is followed by the services sector (21%). A little further
behind is Retail & Wholesale (11%) followed by Financial Services (11%)

57% of BOARD sites and 63% of Cyberscience sites are in the
manufacturing sector

42% of Pyramid Analytics sites are in the finance sector

37% of wholesale and retail sites surveyed use Phocas

40% of Tableau sites are in services
Business Benefits and the BBI
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. A scoring system is then used to derive a composite
weighted score for each benefit, based on the level of benefit 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.
Figure 11 shows the overall breakdown of responses to the Business Benefits
questions. The six levels of achievement, along with their weightings, are shown on
the horizontal axis. Results are RAG (red, amber, green) rated with green indicating
Weighting
Weighted score
Don't know
Got worse/more
expensive (-6)
Not achieved (-2)
Informally
suspected (3)
Formally
claimed, but not
verified (5)
Proven, but not
measured (8)
Proven and
quantified (10)
higher scores and red denoting lower scores.
Better business decisions
10
6
5
3
24.7% 41.4% 13.0% 11.7%
-2
2.2%
-6
0.1%
6.8%
6.74
Faster reporting, analysis or planning
42.7%
37.8%
7.9%
5.2%
2.3%
0.3%
3.9%
7.78
Improved customer satisfaction
16.1%
29.5%
13.1%
16.3%
5.2%
0.4%
19.6%
4.98
Improved data quality
27.6%
33.3%
10.8%
11.7%
7.1%
0.0%
9.6%
6.13
Improved employee satisfaction
20.8%
35.2%
13.2%
15.5%
5.3%
0.6%
9.3%
5.88
9.4%
11.1%
11.0%
20.1%
10.2%
0.2%
38.0%
2.76
More accurate reporting, analysis or planning
31.5%
39.3%
9.4%
10.5%
2.5%
0.2%
6.7%
7.01
Reduced external IT costs
15.3%
13.5%
7.3%
12.1%
16.8%
5.2%
29.7%
2.69
Saved business headcount
10.9%
9.2%
6.7%
12.0%
26.3%
1.1%
33.8%
1.93
Saved IT headcount
9.0%
7.9%
4.9%
9.0%
31.6%
3.3%
34.3%
1.22
Saved other non-IT costs
7.7%
10.2%
6.7%
14.6%
17.1%
1.0%
42.8%
1.96
Increased revenues
0
Figure 11: Frequenc y of Business Benefits (n= 2267)
Figure 12 shows business benefits listed in order of the most commonly achieved.
Faster reporting, analysis or planning
7.78
More accurate reporting, analysis or
planning
7.01
Better business decisions
6.74
Improved data quality
6.13
Improved employee satisfaction
5.88
Improved customer satisfaction
4.98
Increased revenues
2.76
Reduced external IT costs
2.69
Saved other non-IT costs
1.96
Saved business headcount
1.93
Saved IT headcount
1.22
Figure 12: BBI scores for Business Benefits achievement (n= 2267)
Figure 12 Key Findings:

Overall, ‘Faster reporting, analysis or planning’ is rated higher than accuracy
and more than 80 percent of respondents stated that faster reporting,
analysis and planning had been achieved. 71 percent reported that ‘More
accurate reporting, analysis and planning’ had been proven and 66 percent of
respondents said that ‘Better business decisions’ was another benefit likely to
be achieved. The ability to make better business decisions is a highly
desirable benefit. However, it is a benefit that cannot be accurately scoped
when developing a project’s business case. While all BI projects would hope
to gain this benefit, few projects would be cost-justified against the possibility
that it ‘might’ one day be achieved.

‘Improved data quality’ is a benefit that can be measured directly. Although
often one of the more difficult factors to achieve in a business intelligence
project, it ranked fourth in the list followed by ‘Employee satisfaction’ a benefit
that typically arises when users have fast access to data and reports.
‘Customer satisfaction’ can be seen as an ‘external effect’ of business
intelligence and it is good to see that 75 percent of respondents at least
suspect that they achieve this benefit.

The benefits with the lowest level of achievement were ‘Saved headcount in
IT departments’, ‘Saved headcount in business departments’, ‘Saved non-IT
costs’ and ‘Reduced external IT costs’. This is in line with our experience that
companies are still looking to extend their BI capabilities and adding
resources to implement and run them. If BI were a mature and saturated
market we would see more investment in replacing existing systems but until
this pattern changes we would not expect savings in headcount and costs to
move up the list of benefits.
This year the overall BBI decreased slightly from 4.62 (last year) to 4.46. In 2011 the
overall BBI average was 4.89. There could be many reasons for this downward
trend. Perhaps Business Intelligence users are becoming more skeptical, or more
realistic, about the benefits they can achieve and measure.
BBI by peer group and vendor
The difference between business benefit achievement according to the type of
product in use is reflected in the following chart. In order to compare product types
we group products into specific peer groups as explained in the ‘Sample, Products
and Methodologies’ document.
Visual Analysis and Data Discovery
4.70
Dashboard
4.67
Ad-hoc analysis
4.41
4.39
OLAP Analysis
4.38
4.36
4.27
Figure 13: Business benefits anal yzed b y peer group (n= 2133)
Visual Analysis & Data Discovery tools achieved the best BBI results. This reflects
their user-friendliness and flexibility in allowing end-users to quickly load data,
perform user analysis and take advantage of advanced visualization features.
Dashboard vendors rank second and Ad-hoc analysis products rank third. Again, the
user-friendly nature of these tools contributes to the business benefits achieved. This
is a result we have consistently observed in previous editions of The BI Survey.
Specialists, or smaller vendors, tend to achieve better BBI results than the larger
international vendors and Enterprise Reporting vendors. There is a straight
correlation between the size of the BI project (measured in data volume used or
number of users served) and the size of the tool vendor. With few exceptions,
smaller vendors serve smaller projects and products from larger vendors are used in
larger projects.
In our opinion, there are several reasons for the good results achieved by smaller
and specialist vendors:

Some customers may achieve more business benefits when working with
smaller vendors with whom they have a close working relationship, compared
to the more arm’s-length, impersonal relationship that users tend to have with
large vendors. Survey results show that users usually experience better
vendor support, satisfaction and recommendation working with smaller
vendors than they do with many of the large vendors.

The prospect of smaller vendors being removed from a company more
quickly if projects are not successful leads to better-than-average results for
the ones that remain in use. Conversely, large vendors are able to keep
customers with whom they have long-term, strategic relationships, even if
individual projects or products deliver disappointing results.

Products from small vendors are more likely to be selected in competitive
evaluations. Users expending more effort selecting solutions show a higher
level of benefit achievement. This could be because the extra diligence taken
to ensure that the product fits their needs increases the probability of the
project delivering business benefits. There is also the simple fact that some
projects are managed better than others.
But, for whatever reason, it is clear that the customers of small vendors such as
Yellowfin, Tableau, TARGIT and Arcplan are reporting more business benefits than
those of larger vendors such as SAP, Oracle, Microsoft and IBM. This said, several
products from large vendors report above-average BBI scores, most notably the
multidimensional databases Oracle Essbase, IBM TM1 and Microsoft SSAS.
Figure 14 shows the business benefits scores for all products in the Large
International Vendors peer group.
Oracle Essbase
Microsoft SSRS
Microsoft SSAS
MicroStrategy
QlikTech
IBM Cognos TM1
Microsoft Excel
SAP BW
IBM Cognos BI
Infor
SAS
Oracle BI Found. Suite
SAP BO WebI
5.12
4.83
4.76
4.71
4.48
4.46
4.30
4.07
3.98
3.97
3.83
3.80
3.62
3.32
Figure 14: Business benefits for the Large International Vendors peer group
(n=1140)
The Large International Vendors peer group includes companies with annual
revenues of more than $200 million and a truly international reach. These tools are
usually used in enterprise scenarios.
Information Builders is ranked first and the multi-dimensional database Oracle
Essbase comes second, while the traditional ROLAP tools from SAP and Oracle
deliver the least business benefits.
In previous editions of The BI Survey the in-memory technologies (either open
multidimensional databases or proprietary technologies like QlikTech’s QlikView)
have usually appeared in the top five. This trend has been bucked since a number of
the large vendors have invested in new database and caching technologies as well
as more user-friendly tools.
The correlation between business benefits and user self-service, as well as fast
query performance, has also been a feature of previous BI Surveys.
Arcplan
5.29
Yellowfin
5.28
5.12
Microsoft SSRS
4.76
MicroStrategy
4.48
Evidanza
4.11
SAP BW
3.98
IBM Cognos BI
3.97
SAP BO WebI
3.62
3.32
Figure 15: Business benefits for the Enterprise Reporting peer group (n=704)
The Enterprise Reporting peer group includes products that can provide standard
formatted reporting in a large-scale enterprise situation. Arcplan and Yellowfin, both
very flexible tools for designing reporting applications, rank highest in this peer
group, followed by the specialist reporting tools from Information Builders and
Microsoft. Users of the large vendors SAP, Oracle and IBM report lower business
benefits.
Tableau
5.56
Arcplan
5.29
Yellowfin
5.28
5.12
Dimensional Insight
4.99
BOARD
4.54
MicroStrategy
4.48
QlikTech
4.46
Evidanza
4.11
Logi Analytics
4.08
3.62
Figure 16: Business benefits for the Dashboard vendor peer group (n= 786)
The Dashboard peer group includes products geared towards creating advanced
dashboards.
Users of tools from small vendors report the highest business benefit scores in the
Dashboard peer group. This group is led by Tableau. Arcplan comes second
followed by Yellowfin. All three leading vendors focus on flexibility and visualization.
Pentaho
5.42
TARGIT
5.19
Oracle Essbase
4.83
Microsoft SSAS
4.71
Bissantz
4.66
BOARD
4.54
Insgesamt
4.38
IBM Cognos TM1
4.30
Jedox
4.26
Pyramid
4.04
Cubeware
4.00
SAP BW
3.98
IBM Cognos BI
3.97
Infor
3.83
Figure 17: Business benefits for the OLAP Anal ysis peer group (n= 1058)
The OLAP Analysis peer group includes products that support analysis in
dimensional and hierarchical data models. This peer group is crowded and highly
competitive with many vendors achieving good results. The peer group is led by
Pentaho followed by TARGIT, Oracle Essbase and Microsoft SSAS. It is interesting
to see the commercial open source vendor Pentaho, with its OLAP server Mondrian,
leading the field. The low overall business benefit achievement for Infor is surprising
since users rate the product highly in several other categories including product
satisfaction and self-service BI.
Tableau
5.56
TARGIT
5.19
Phocas
5.06
Dimensional Insight
4.99
Cyberscience
4.56
QlikTech
Pyramid
SAS
4.46
4.04
3.80
Figure 18: Business benefits for the Visual Analysis & Data Discover y peer group
(n=558)
The Visual Analysis & Data Discovery peer group includes products that provide
advanced visualization features. Tableau comes out top in this small peer group of
specialist vendors followed by TARGIT and Phocas. One of the most successful BI
vendors over the last five years, QlikTech has slipped back into the pack in sixth
place.
Arcplan
5.29
Oracle Essbase
4.83
Bissantz
4.66
BOARD
4.54
IBM Cognos TM1
4.30
Jedox
4.26
Evidanza
4.11
Microsoft Excel
4.07
Cubeware
4.00
Infor
3.83
Corporate Planning
3.80
Figure 19: Business benefits for the Performance Management peer group (n=776)
The Performance Management peer group includes companies whose products are
predominantly used in planning projects. The peer group is led by Arcplan, a vendor
that has invested heavily in its planning functionality in recent times, followed by
Oracle Essbase. Both tools show a high degree of flexibility in building up planning
applications. Bissantz, BOARD and IBM Cognos TM1 are in third, fourth and fifth
places respectively, also scoring excellent results.
Tableau
Yellowfin
TARGIT
Phocas
Dimensional Insight
Oracle Essbase
Microsoft SSAS
Bissantz
Cyberscience
MicroStrategy
IBM Cognos TM1
Microsoft Excel
Cubeware
SAP BW
IBM Cognos BI
Infor
Corporate Planning
SAS
SAP BO WebI
5.56
5.28
5.19
5.06
4.99
4.83
4.71
4.66
4.56
4.48
4.30
4.07
4.00
3.98
3.97
3.83
3.80
3.80
3.32
Figure 20: Business benefits for the Ad -hoc Anal ysis peer group (n=1356)
The Ad-hoc Analysis peer group is led by Tableau, with its highly visual and
interactive data analysis features. Users achieve a very high level of business
benefits with this approach. Yellowfin, TARGIT, and Phocas take second, third and
fourth places, also supporting ad-hoc analysis in an interactive and visually
appealing way.
Goal Achievement
The first step in a successful project of any description is to establish a clear set of
goals. In The BI Survey, goal achievement and business benefits mean different
things. The Survey does not measure whether a project was well conceived or useful
to the company. Instead, it measures the degree to which the stated goals were
attained, regardless of what the goals were and whether the project itself brought
any benefits.
The Goal Achievement Index (GAI) is based on the question: “To what extent has
the project achieved the business goals originally set?”, and responses are weighted
as shown in the following chart.
Level of goal achievement reported
Not met any goals at all
Weighting
-6
Not met any goals yet
1
Hardly met goals
3
Partially met goals
5
Largely met goals
7
Fully achieved goals
9
Exceeded goals
10
Figure 21: Responses and w eightings for the GAI
Goal achievement levels have remained relatively constant over the years. The
figures reveal that 43 percent of respondents reported their projects had fully
achieved or exceeded goals while 80 percent said they had at least largely met
business goals. This figure remains the same as in The BI Survey 12, while last year
saw an increase from 70 percent in 2011. At the other end of the spectrum only 4.6
percent reported they had not met any goals yet, or had met no goals at all.
Exceeded goals
18.2%
Fully achieved goals
24.5%
Largely met goals
37.4%
Partially met goals
Hardly met goals
14.0%
1.3%
Not met any goals
yet
Not met goals at all
4.4%
0.2%
Figure 22: Goal achievement (n=2203)
Figure 23 below breaks down goal achievement by product. It should be noted that
‘Hardly met goals’, ‘Not met any goals yet’ and ‘Not met goals at all’ from Figure 22
have been merged into one category called ‘Few if any goals met.’ The rows in this
table sum to 100 percent.
Products
Arcplan
Bissantz
BOARD
Corporate Planning
Cubeware
Cyberscience
Dimensional Insight
Evidanza
IBM Cognos BI
IBM Cognos TM1
Infor
Jedox
Logi Analytics
Microsoft Excel
Microsoft SSAS
Microsoft SSRS
MicroStrategy
Oracle Essbase
Pentaho
Phocas
Pyramid
QlikTech
SAP BW
SAP BO WebI
SAS
Tableau
TARGIT
Yellowfin
Few if any
goals met
Partially
met goals
Largely
met goals
Fully
achieved
goals
Exceeded
goals
GAI
4%
1%
3%
3%
2%
2%
0%
11%
12%
4%
8%
6%
5%
10%
18%
6%
6%
4%
4%
9%
9%
0%
27%
6%
5%
4%
9%
4%
3%
10%
5%
8%
8%
18%
14%
11%
15%
19%
21%
2%
13%
8%
14%
7%
29%
15%
21%
16%
8%
14%
14%
11%
9%
11%
18%
37%
20%
9%
25%
7%
33%
37%
30%
32%
45%
48%
37%
30%
45%
32%
39%
27%
26%
46%
45%
42%
38%
41%
30%
48%
43%
42%
27%
38%
51%
39%
29%
36%
28%
30%
32%
26%
41%
26%
24%
23%
22%
30%
16%
40%
32%
29%
32%
15%
8%
20%
23%
26%
26%
25%
17%
26%
25%
18%
18%
16%
17%
15%
25%
40%
25%
27%
18%
21%
15%
16%
27%
11%
6%
22%
8%
29%
23%
22%
0%
16%
13%
14%
32%
5%
17%
22%
11%
27%
8%
3%
26%
36%
18%
13%
8.04
8.13
8.03
7.62
7.44
7.63
7.93
7.04
6.49
8.18
6.88
7.96
7.80
7.22
5.45
7.19
7.08
7.38
8.15
6.91
7.06
7.95
6.02
7.60
6.46
7.00
7.26
7.96
7.38
7.47
Figure 23 : Goal achievement anal yzed b y product (n= 2073)

This year’s Survey again sees IBM Cognos TM1 and Oracle Essbase
attaining the highest goal achievement levels, only switching places
compared to last year. IBM TM1 has consistently achieved excellent ratings
in The BI Surveys and winning this important category crowns this success.

Both TM1 and Essbase are multidimensional databases and are among the
longest-lived and most mature solutions in the field. Third ranked Bissantz is
usually implemented as a front-end to a multidimensional database. The
positive impact on its goal achievement could be attributed to a strong core of
experienced users and consultants as well as a good level of understanding
of the product gleaned from successful implementations.

Last year’s Survey saw a remarkable improvement in Oracle Essbase’s goal
achievement. From its low scores in BI Surveys prior to 2012, Essbase has
risen to second place this year, just a fraction behind TM1. It should be noted
that many Essbase responses this year came not only from organizations
that simply use the Excel Add-in, but also from a number of companies using
the product in conjunction with specialist front-end tools such as Dodeca.
This factor seems to have had a positive impact on Essbase’s ratings.

Oracle and IBM results vary depending on the product. The multidimensional
databases Essbase and TM1 have high goal achievement ratings whereas
Oracle BI Foundation Suite and IBM Cognos BI do not fare very well. The
tendency for these products to feature in larger projects with more users and
higher data volumes, and a dependency on other software components (e.g.
a relational database that is outside the control of a BI solution) may explain
this.

Fourth and fifth ranked Arcplan and BOARD offer BI application development
platforms. The flexibility of this approach and the functionality of these
solutions seem to ensure good goal achievement ratings.

Tableau is the product that most regularly exceeded the goals originally set
with a remarkable 36 percent of respondents stating that this happened.
Tableau is one of the fastest growing products in the industry right now and
this might be a contributing factor to its success.

Microsoft Excel has the lowest goal achievement score. Users praise its
flexibility but when it comes to implementing professional BI applications the
shortcomings of this spreadsheet solution become obvious and goals often
cannot be met.

Pyramid Analytics projects show the highest rate of meeting only a few - if
any – goals. This fairly new solution might appear to customers to offer more
than it can currently deliver.

Other products with low goal achievement were SAP BW, IBM Cognos BI
and Infor, all mature products from large vendors. SAP BW and IBM Cognos
BI also often suffer from bad query performance in large projects, a key
reason why goals may not be met.
Dashboard
7.65
7.64
7.54
OLAP Analysis
7.37
Ad-hoc analysis
7.34
7.19
7.18
Figure 24: Goal achievement anal yzed b y peer group (n= 2133)
Goal achievement ratings also depend on the type of software selected and, just as
in last year’s Survey, Large Vendors and Enterprise Reporting tools had the lowest
goal achievement levels of all peer groups whereas Dashboard, Performance
Management and Visual Analysis & Data Discovery tools ranked highest. These
products are often used in smaller projects where it is easier to define goals,
implement the tool, and review whether, and to what extent, goals have been
achieved. Enterprise Reporting tools, on the other hand, often have to fulfill a broad
set of expectations and requirements that they might not always meet.
Deployment
This section focuses on BI deployment, analyzing product usage in small, medium
and large organizations. We compare how widely products are used and analyze the
frequency of BI usage in individual departments and business functions.
Usage in different company sizes
It is important to understand which products are best suited to which size of
company. Most products are best suited to either small, medium or large project
sizes. Figure 25 provides a general overview of the company size served by each
product.
Number of employees
Product
< 100
100 - 2500
> 2500
Arcplan
13%
33%
54%
Bissantz
16%
62%
22%
BOARD
11%
80%
9%
Corporate Planning
32%
59%
9%
Cubeware
9%
72%
19%
Cyberscience
15%
65%
20%
Dimensional Insight
18%
66%
16%
Evidanza
10%
90%
0%
IBM Cognos BI
5%
47%
48%
IBM Cognos TM1
2%
45%
53%
Infor
7%
58%
35%
10%
46%
44%
Jedox
18%
65%
17%
Logi Analytics
37%
49%
15%
Microsoft Excel
16%
66%
18%
Microsoft SSAS
17%
52%
31%
Microsoft SSRS
6%
65%
29%
MicroStrategy
6%
38%
55%
Oracle Essbase
2%
28%
70%
0%
43%
57%
Pentaho
39%
47%
14%
Phocas
47%
46%
7%
Pyramid
16%
62%
22%
QlikTech
15%
49%
35%
SAP BW
5%
34%
61%
SAP BO WebI
0%
39%
61%
SAS
14%
31%
56%
Tableau
15%
52%
33%
TARGIT
24%
69%
6%
Yellowfin
60%
33%
7%
Figure 25: Number of emplo yees in different compan y sizes b y product (n= 2132)
60 percent of Yellowfin and 47 percent of Phocas respondents are in the small
company bracket while the majority of vendors serve mainly mid-sized companies
with 100 to 2,500 employees. We also find among BI Survey respondents that large
vendors such as IBM, Oracle, SAS and SAP (but not Microsoft) sell mostly to large
organizations. Interestingly, the majority of MicroStrategy and Arcplan customers
also belong to this group.
Small number of users
Products
Median
Mean
Corporate Planning
5
8
Jedox
15
40
Yellowfin
15
63
Phocas
16
34
Logi Analytics
20
76
Pentaho
20
204
Cyberscience
25
72
Tableau
25
119
Pyramid
25
122
Evidanza
30
42
TARGIT
30
54
Bissantz
30
136
Figure 26: Small number of users b y product ( median=<30, n=changing bases)
A high deviation between median and mean indicates the presence of a minority of
very large user bases, far larger than the usual number of users reported. For
example, Pentaho, Bissantz, Pyramid Analytics and Tableau are typically rolled out
to 30 or less users but have a mean user base of over 100 (Figure 26).
Medium number of users
Products
Median
Mean
Cubeware
40
106
Microsoft SSAS
45
262
BOARD
50
128
Infor
50
216
Dimensional Insight
50
260
QlikTech
50
379
IBM Cognos TM1
70
264
Figure 27: Medium number of users b y product ( median= 30-100, n=changing
bases)
In the group of medium number of users (median 40-70) QlikTech also features in
projects with significantly higher numbers of users, with a mean of 379.
Typically supporting around 100-263 users Excel, SAP BO Web Intelligence,
Information Builders, SAP BW and MicroStrategy have a mean of over 1,000 users,
showing that some mass deployments are reported in this Survey (Figure 28).
Large number of users
Products
Median
Mean
Microsoft SSRS
100
383
Arcplan
100
445
100
781
SAS
117
350
MicroStrategy
150
1394
IBM Cognos BI
200
760
SAP BW
230
1582
Oracle Essbase
250
1306
SAP BO WebI
250
2131
Microsoft Excel
250
4115
263
1598
Figure 28: Large number of users b y product ( median= >100, n=changing bases)
Percentage of BI users in companies
The BI Survey has regularly found over the years that the number of users per
product bears little relation to the size of the company using the product. This
indicates that many products are used as departmental solutions rather than for
global usage across the company.
The product penetration figures below indicate the percentage of employees in
customer sites using BI products.
Microsoft Excel
Yellowfin
Logi Analytics
Dimensional Insight
Phocas
Pentaho
TARGIT
Microsoft SSRS
Pyramid
IBM Cognos BI
BOARD
Tableau
Microsoft SSAS
QlikTech
Cyberscience
MicroStrategy
Evidanza
SAP BO WebI
Arcplan
Bissantz
SAP BW
Jedox
Corporate Planning
Infor
Cubeware
Oracle Essbase
SAS
IBM Cognos TM1
61%
37%
32%
27%
26%
19%
19%
18%
18%
17%
17%
16%
16%
15%
13%
13%
12%
12%
12%
11%
11%
10%
10%
10%
10%
10%
9%
8%
6%
5%
Figure 29: Percentage of emplo yees using products at customer sites (n=2116)
Excel unsurprisingly leads the field with the highest penetration rate of users at
customer sites (61 percent), followed by Yellowfin (37 percent) whose high ranking
reflects the fact that a large percentage of its users fall into the small enterprise
category. Information Builders, Logi Analytics and Dimensional Insight are also used
by a significantly higher percentage of employees than most other tools. The vast
majority of BI products are used by between 10 and 19 percent of employees in enduser organizations.
BI usage by department
The Survey asked participants which business functions use BI applications in their
company.
Finance and Controlling
Management
48%
Sales
IT
Marketing
Production
Procurement
Logistics
Human resources
R&D
Legal
Other
76%
74%
21%
17%
20%
22%
16%
12%
7%
5%
4%
7%
2013
41%
45%
45%
36%
53%
55%
85%
65%
33%
32%
31%
2008
Figure 30: Frequenc y of business departments using BI 201 3 vs 2008
(n=1881/2019)
Figure 30 reveals the marked growth in BI between 2008 and 2013. The results
show that BI tool usage has become more pervasive and is no longer used solely in
finance departments or by senior management. That said, Finance and Controlling
remains the core business function using BI at 85 percent.
The highest increase can be seen in management: 74 percent of respondents stated
this year that management uses BI tools, up from 59 percent last year and 48
percent in 2008. This could be explained by a new breed of more IT-savvy
employees taking over management positions. The need for faster transparency
within businesses could be another reason why management as a user group has
seen such a fast increase in BI tool usage in the last five years.
Sales and IT both achieve high penetration rates of 65 and 55 percent respectively.
The results show a clear spread of BI across departments, with legal departments
being the least likely to use BI applications.
SAP BO WebI
TARGIT
Dimensional Insight
SAP BW
QlikTech
Cubeware
Cyberscience
Bissantz
Logi Analytics
BOARD
MicroStrategy
Arcplan
IBM Cognos BI
Infor
Pyramid
Total
Phocas
Microsoft SSRS
Tableau
Oracle Essbase
Evidanza
IBM Cognos TM1
Microsoft Excel
Microsoft SSAS
Jedox
Pentaho
Corporate Planning
SAS
6.00
5.47
5.08
4.98
4.92
4.81
4.81
4.73
4.63
4.61
4.61
4.49
4.47
4.41
4.38
4.29
4.28
4.28
4.22
4.06
3.98
3.94
3.66
3.65
3.45
3.26
3.01
2.97
2.79
2.67
Figure 31: Average number of departments using a product (n=2133)
The Survey asked how many departments use BI products in companies. Figure 31
shows how products such as SAP BO Web Intelligence, TARGIT, Information
Builders, and Dimensional Insight are commonly used across many departments.
Others, such as SAS, Corporate Planning, Pentaho and Jedox, are more likely to be
used in just a few departments. On average, BI products serve 4.28 departments, up
from 3.72 departments last year.
The selection process
Reasons to buy
The Survey asked organizations why they purchased a particular product.
Participants were provided with a list of options from which they could select up to
three answers. The chart below is an overview of the responses.
Functionality
47%
Ease of use for report recipients
42%
Fast query performance
36%
Price-performance ratio
34%
Ease of use for report designers
34%
Flexibility of the software
29%
Predefined data connection
26%
Large data handling capacity
21%
Proof of concept faster or better
19%
Ability to support large numbers of users
18%
Vendor/product reputation
15%
Availability of local support
15%
Vendor listed as corporate standard
10%
Innovative capacity of the vendor
10%
Bundled with another product
9%
Support for mobile devices
8%
Vendor relationship
7%
Size/Financial stability of the vendor
7%
International focus of the software
Other
External hosting or cloud offering
4%
2%
1%
Figure 32: Frequenc y of reasons to bu y (n=2141, multi response)
The chart in Figure 32 ranks the reasons why respondents purchased their BI tool in
order of frequency.
Functionality is top of the criteria list followed by Ease of use. The two least popular
product selection criteria this year are ‘International focus’ and ‘Cloud’. However, our
more detailed analysis of trending topics shows that Cloud BI is growing in
importance (see The BI Survey 13 Trending Topics series).
Performance
Management
Visual Analysis
and Data
Discovery
OLAP Analysis
Ad-hoc Query
Dashboard
Enterprise
Reporting
Large
Internnational
Vendors
Total
Functionality
49%
45%
49%
45%
50%
48%
44%
47%
Ability to support large numbers
of concurrent users
14%
13%
17%
19%
19%
25%
22%
19%
Large data handling capacity
15%
27%
18%
22%
26%
22%
25%
22%
Ease of use for report designers
31%
44%
29%
31%
42%
29%
28%
32%
Ease of use for report recipients
48%
49%
40%
42%
46%
34%
36%
42%
Fast query performance
36%
56%
35%
34%
39%
20%
35%
36%
Predefined data connection
32%
24%
27%
24%
25%
29%
22%
26%
Innovative capacity of the vendor
13%
8%
12%
10%
10%
11%
7%
10%
Vendor/product reputation
12%
11%
15%
18%
14%
21%
19%
16%
4%
3%
8%
7%
4%
11%
10%
7%
38%
29%
27%
24%
33%
23%
23%
28%
3%
2%
5%
5%
4%
7%
5%
5%
41%
28%
38%
30%
30%
22%
25%
31%
Vendor listed as corporate
standard
5%
3%
13%
14%
5%
20%
18%
12%
Availability of local support
18%
13%
16%
15%
11%
13%
11%
14%
Proof of concept faster or better
20%
24%
19%
19%
27%
17%
18%
20%
Vendor relationship
6%
7%
7%
7%
6%
7%
8%
7%
Bundled with another product
5%
5%
10%
11%
3%
12%
13%
9%
Support for mobile devices
6%
8%
4%
7%
13%
10%
7%
7%
External hosting or cloud offering
1%
2%
1%
2%
3%
2%
1%
1%
Other
2%
4%
3%
2%
2%
2%
1%
2%
Size/Financial stability of the
vendor
Flexibility of the software
International focus of the software
Price-performance ratio
Figure 33: Frequenc y of reasons to bu y anal yzed b y peer group (n=2015)
Figure 33 shows the frequency of reasons to buy by peer group. There are a number
of key findings here:

Functionality and ease of use for reporting recipients and report designers
are the most important reasons to buy BI products across all product peer
groups. Only enterprise reporting and large international vendors show a
lower frequency in these categories compared to the other peer groups.

Enterprise Reporting tools and large international vendors are more likely to
be selected than other tools/vendors because companies have them as a
corporate standard, they come bundled with another product or for their
ability to support large numbers of users.

Dashboard, visual BI/data discovery and performance management vendors
are seldom selected because they are company standards. Bundling is also
not an important factor here.

Surprisingly, large international vendors are not selected because they offer
local support although they have the biggest, most widespread support
organizations.

Price-performance ratio is not an important issue for buyers of enterprise
reporting products and large international vendors, but is far more so for
buyers of performance management and OLAP products.

The ability to handle large volumes of data is not really an issue in enterprise
reporting and large enterprise projects, whereas performance management,
visual BI/data discovery and dashboard products do consider data volume to
be an issue.

Support for mobile devices is popular with customer of dashboard and
enterprise reporting products. However, in all other peer groups it was
chosen as a top three criterion in fewer than 10 percent of cases. This result
was identical last year.

Very few respondents regard ‘External hosting or cloud offering’ to be
particularly important despite market hype, the emergence of start-up
companies in this area and growing functionality in existing tools.

Performance management products are selected in a completely different
way to all other peer groups. Predefined data connections, the flexibility of
the software, price-performance ratio and availability of local support are all
important considerations in this group and mentioned more often than for all
other peer groups. The ability to support large numbers of users, large data
handling capacity, the vendor being listed as a corporate standard, vendor
size and support for mobile devices are much less important compared with
the other peer groups.
Products
Large data handling
capacity
Ease of use for report
designers
Ease of use for report
recipients
Fast query
performance
Flexibility of the
software
Price-performance
ratio
Vendor listed as
corporate standard
Arcplan
13%
33%
38%
31%
56%
36%
4%
Bissantz
24%
24%
63%
52%
28%
21%
3%
BOARD
15%
54%
61%
31%
42%
38%
4%
Corporate Planning
6%
9%
67%
15%
55%
64%
0%
Cubeware
7%
27%
58%
21%
20%
56%
4%
Cyberscience
24%
58%
36%
44%
25%
35%
2%
Dimensional Insight
31%
47%
41%
52%
29%
34%
3%
3%
24%
48%
17%
34%
38%
0%
IBM Cognos BI
20%
24%
41%
16%
14%
13%
29%
IBM Cognos TM1
34%
32%
38%
72%
42%
18%
10%
Evidanza
7%
25%
30%
25%
37%
46%
9%
38%
42%
29%
21%
44%
23%
6%
Jedox
12%
37%
45%
44%
42%
62%
2%
8%
56%
36%
13%
49%
69%
3%
Microsoft Excel
8%
36%
54%
8%
33%
33%
18%
Microsoft SSAS
18%
23%
33%
43%
15%
56%
22%
Microsoft SSRS
8%
42%
31%
19%
21%
50%
25%
MicroStrategy
39%
32%
42%
30%
17%
17%
10%
Oracle Essbase
43%
22%
30%
61%
30%
13%
11%
26%
23%
19%
21%
9%
16%
35%
Pentaho
14%
31%
14%
14%
46%
80%
0%
Phocas
23%
34%
65%
78%
28%
28%
0%
Pyramid
13%
49%
49%
47%
27%
44%
4%
QlikTech
33%
41%
53%
72%
34%
22%
1%
SAP BW
19%
4%
7%
7%
7%
3%
41%
SAP BO WebI
14%
32%
40%
6%
12%
5%
37%
SAS
34%
20%
26%
31%
29%
11%
20%
Tableau
30%
72%
60%
38%
21%
28%
0%
TARGIT
17%
41%
40%
33%
31%
34%
5%
Yellowfin
17%
63%
70%
20%
27%
57%
0%
Infor
Logi Analytics
Figure 34: Frequenc y of reasons to bu y anal yzed b y product (selection)
Figure 34 shows a small subset of the product selection criteria by product. To carry
out your own detailed analysis of these results and analyze all the selection criteria
by product, please refer to our online BI Survey Analyzer tool.

‘Large data handling capacity’ is a frequently cited selection criterion for the
multidimensional databases Oracle Essbase and IBM Cognos TM1. Both are
known for handling the larger applications that are implemented with
multidimensional databases. The relational reporting engines from
MicroStrategy and Information Builders, which are often found in large scale
projects, also score highly in this area. It is interesting to see that QlikTech,
SAS, Dimensional Insight and Tableau are also often chosen for their
capacity to handle large data sets. This is rarely a consideration when
choosing to purchase Evidanza, Corporate Planning, Cubeware and Infor.

The products most regularly selected because of their ease of use for both
report designers and report recipients are Tableau, Yellowfin, Pyramid
Analytics, BOARD, QlikTech, Dimensional Insight and TARGIT. More
appealing in ease of use to report designers than report recipients are
Cyberscience, Logi Analytics and Information Builders. Conversely Corporate
Planning, Phocas, Bissantz and Cubeware are rated as more appealing in
ease of use to report recipients than report designers. SAS and Oracle BI
Foundation Suite are seldom chosen for ease of use, and SAP BW is almost
never chosen for this reason.

Fast query performance was an important consideration for buyers of
Phocas, IBM Cognos TM1, QlikTech and Oracle Essbase. SAP BO WebI,
SAP BW and Microsoft Excel were the three tools found to be least likely to
convince potential buyers with strong query performance.

Arcplan and Corporate Planning lead the field in flexibility, followed by Logi
Analytics, Pentaho and Information Builders.

Vendors that also have an open source offering – Pentaho and Jedox – rank
highly in price-performance as a selection criterion. But Logi Analytics,
Corporate Planning, Yellowfin, Cubeware and Microsoft SSAS are also able
to convince buyers with this argument.

Not many products are selected to become company standards. SAP BW
and SAP BusinessObjects WebI are often chosen for this reason, as well as
Oracle BI Foundation Suite (but not Oracle Essbase) and IBM Cognos BI (but
not TM1).
Implementing BI and Satisfaction
In this chapter, we examine various aspects of business intelligence software
implementations, such as the time taken to implement a BI project and the level of
satisfaction with vendor and implementer support.
Implementation time
Countless IT projects experience delays and data warehousing and BI projects are
by no means immune to this perennial issue. Participants were asked how long
implementation of the BI aspect of their project took from software purchase to initial
rollout.
Figure 35 shows the distribution of implementation times. The most frequent value
(modal value) is ‘three to six months’ (27 percent), followed by ‘one to three months’
(25 percent). Ten percent of respondents reported initial implementation times of
over a year and two percent took over two years.
The Survey found that 64 percent of projects are rolled out within six months,
compared to 71 percent last year.
Less than 1 month
12%
1 to 3 months
25%
3 to 6 months
27%
6 to 12 months
22%
1 to 2 years
10%
2 to less than 3 years
2%
3 years or more
2%
Figure 35: Implementation time distribution (n=2018)
Implementation time depends largely on the product being implemented (Figure 36).
Naturally, the complexity of the product and/or the project will impact on project
length and certain products lend themselves better to more complex projects, thus
increasing their average implementation time. Clearly other factors such as the
number of data sources, the volume of data and the number of users or departments
served also have an influence here.
Products
Phocas
Logi Analytics
Pyramid
Yellowfin
Tableau
TARGIT
QlikTech
Jedox
BOARD
Dimensional Insight
Pentaho
Bissantz
Corporate Planning
Microsoft Excel
Arcplan
Cyberscience
Total
Microsoft SSAS
Cubeware
Evidanza
Oracle Essbase
Infor
SAP BO WebI
IBM Cognos TM1
MicroStrategy
IBM Cognos BI
SAS
Microsoft SSRS
SAP BW
Mean
Median
2.7
3.5
3.5
3.6
4.1
4.5
4.6
4.9
5.7
5.9
6.0
6.3
6.5
6.5
6.5
6.6
7.2
7.7
7.7
7.9
8.1
8.8
8.9
9.0
9.1
9.2
9.4
9.8
10.0
12.7
14.2
1.9
2.9
2.8
2.8
1.9
3.3
3.2
3.4
4.5
3.8
5.3
4.2
3.6
2.3
4.5
3.0
4.6
4.9
6.4
5.0
4.4
6.5
6.1
7.5
5.0
7.4
7.4
7.6
8.3
9.0
10.7
Figure 36: Implementation time in months anal yzed b y product (n=1898)

Phocas, a query and analysis tool intended for self-service applications
administered by business users has the quickest implementation time
(average 2.7 months, median 1.9 months).

Logi Analytics a flexible BI development tool that can easily be embedded in
other applications ranks second with an average implementation time of 3.5
months and a median of 2.9 months.

Pyramid Analytics, a relatively new BI solution which has a modern
architecture and currently only works with Microsoft SQL Server databases
ranks equal second for average implementation time but has a median of 2.8
months. Its user interface is very Microsoft-like: this approach might also help
to reduce implementation time.
3.0
Dashboard
4.3
4.4
Ad-hoc analysis
5.0
OLAP Analysis
5.0
6.2
7.0
Figure 37: Average implementation time in months anal yzed b y product peer
group (median) (n=1672)
Figure 37 analyzes implementation time by product peer groups. Visual Analysis &
Data Discovery tools clearly rank at the top (i.e. their projects are shorter). These
peer groups contain products that offer more self-service capabilities, a factor that
can lead to shorter implementation times. Large International Vendors and
Enterprise Reporting Vendors are better suited to more ambitious projects that take
longer to implement.
Less than 1 month
61%
1 to 3 months
47%
3 to 6 months
34%
6 to 12 months
26%
1 to 2 years
17%
2 to less than 3 years
17%
3 years or more
7%
Figure 38: Percentage of sites reporting no significant problems, b y
implementation time (n=1983)
A longer implementation time increases the number of issues a project is likely to
encounter (Figure 38). Evidence from The Survey indicates that projects achieving a
successful rollout within six months report significantly fewer problems. Extended
implementation periods increase risk and reduce the chances of achieving benefits.
Some factors seem to have a bigger influence on rollout times than others. For
instance, Survey data shows that smaller companies roll out their applications faster
than medium or large companies. Smaller, simpler projects appear to reduce the
likelihood of issues affecting the project such as company politics and changing
requirements.
There will always be instances where unexpected problems arise but a solid, well
thought out project plan and an experienced project management team should be
able to negate the effects of the unexpected. Where projects are so large they
become unwieldy, they should be broken down into smaller projects and managed
as part of a program.
Support
Good support is crucial for project success. The following chart shows that business
benefits depend very much on the quality of support. This result is in line previous
editions of The BI Survey.
Excellent
Very poor
5.53
2.55
Figure 39 : BBI anal yzed b y vendor support qualit y (n=2092)
Ad-hoc analysis
Enterprise
Reporting
Dashboard
OLAP Analysis
Performance
Management
Visual Analysis and
Data Discovery
Large International
Vendors
Excellent
31%
24%
40%
29%
33%
44%
20%
Good
39%
41%
38%
42%
42%
38%
41%
Satisfactory
19%
22%
15%
19%
16%
13%
25%
Not very good
4%
6%
3%
3%
4%
2%
6%
Very poor
3%
3%
2%
2%
2%
1%
3%
None used so far
4%
3%
2%
5%
4%
3%
6%
Vendor Support
Figure 40: Vendor support satisfaction anal yzed b y product peer group (n=1967)
Vendor support ratings vary by peer group (Figure 40). In general, customers of
large international vendors reported much lower support quality than those using
small and medium-sized vendors or niche BI vendors. The same is true for
Ad-hoc analysis
Dashboard
OLAP Analysis
Performance
Management
Visual Analysis and
Data Discovery
Large International
Vendors
implementer support satisfaction (Figure 41).
Excellent
34%
28%
37%
34%
38%
37%
28%
Good
35%
36%
32%
36%
38%
32%
36%
Satisfactory
12%
14%
10%
12%
11%
10%
14%
Not very good
4%
5%
3%
3%
2%
2%
4%
Implementer
support
Very poor
2%
2%
1%
2%
1%
1%
2%
None used so far
13%
15%
17%
13%
10%
18%
15%
Figure 41: Implementer support satisfaction anal yzed b y product peer group
(n=1841)
Dimensional Insight, Pyramid Analytics and Bissantz received the best overall
vendor support ratings from their customers. The top three vendors in each peer
group were:
Peer Group
Top three products by Vendor Support
Large international
Information Builders, SAS, MicroStrategy
vendors
Yellowfin, Arcplan, Information Builders
Dashboard
Dimensional Insight, Logi Analytics, Tableau
Ad-hoc Analysis
Dimensional Insight, Bissantz, Cyberscience
OLAP Analysis
Pyramid Analytics, Bissantz, BOARD
Visual Analysis & Data
Dimensional Insight, Pyramid Analytics, Cyberscience
Discovery
Performance
Arcplan, Bissantz, Oracle Essbase
Management
Figure 42: Top three vendors in each peer group for vendor support (n=1967)
Problems in BI projects
Query performance has been the most commonly cited product-related problem in all
but one of the previous editions of The BI Survey, the exception being two years ago
when it was eclipsed by ‘poor data quality’. Furthermore, sites that used good query
performance as a key selection criterion were more successful in business terms
than those that did not.
Despite the advances in technology with better caches, in-memory databases and
much more powerful hardware, users still think that slow query performance is the
biggest problem in BI implementations. While we see average absolute query
response times fall each year, we suspect that the average user’s perception of what
is an ‘acceptable’ response time and when it becomes a problem are rising at least
at the same rate.
No significant problems
36%
Slow query performance
15%
Poor data quality
15%
Lack of interest from business users
14%
Requirements changed
13%
Could not agree on requirements
12%
Company politics
12%
Administrative problems
12%
Poor data governance
10%
Software not flexible enough
8%
Unreliable software
8%
Unable to get data from some systems
7%
Software too hard to use
6%
Missing key features
6%
Can not handle data volume
Product security limitations
Can not handle number of users
Other
5%
3%
2%
1%
Figure 43: Frequenc y of problems reported in BI projects (n=2225)
Company politics
Could not agree on
requirements
Lack of interest from
business users
Poor data quality
Product can not handle
number of users
Product can not handle
data volume
Product security
limitations
Software not flexible
enough
Unable to get data from
some systems
Unreliable software
Arcplan
7%
11%
22%
5%
1%
3%
15%
0%
2%
16%
15%
5%
5%
5%
5%
3%
41%
Bissantz
11%
7%
18%
21%
0%
8%
23%
0%
0%
15%
13%
0%
5%
5%
4%
1%
43%
BOARD
9%
9%
10%
14%
10%
1%
13%
7%
6%
13%
17%
6%
1%
9%
7%
12%
39%
Corporate Planning
6%
6%
6%
12%
0%
6%
0%
3%
15%
21%
6%
0%
0%
0%
6%
3%
52%
Cubeware
8%
13%
26%
20%
1%
8%
12%
0%
8%
30%
16%
1%
5%
5%
6%
7%
28%
Cyberscience
10%
10%
2%
15%
8%
5%
8%
2%
3%
3%
5%
5%
12%
8%
12%
2%
46%
Dimensional Insight
12%
17%
7%
22%
2%
13%
7%
2%
0%
7%
7%
3%
7%
8%
5%
3%
47%
Evidanza
7%
3%
31%
7%
3%
0%
14%
3%
3%
31%
14%
0%
7%
10%
7%
17%
34%
IBM Cognos BI
21%
14%
13%
21%
5%
18%
20%
2%
8%
29%
9%
0%
12%
16%
6%
5%
16%
IBM Cognos TM1
12%
18%
6%
12%
2%
20%
14%
4%
8%
18%
14%
4%
6%
2%
6%
14%
26%
Infor
7%
2%
11%
13%
6%
8%
12%
5%
6%
18%
11%
0%
2%
5%
2%
17%
41%
14%
18%
8%
8%
4%
6%
18%
0%
0%
8%
8%
6%
8%
4%
4%
8%
49%
Jedox
10%
6%
12%
14%
4%
4%
6%
2%
6%
10%
10%
1%
3%
3%
3%
14%
52%
Logi Analytics
7%
5%
5%
10%
5%
5%
10%
2%
7%
10%
2%
7%
5%
5%
5%
5%
56%
Microsoft Excel
7%
7%
7%
12%
21%
29%
10%
7%
31%
14%
5%
12%
7%
12%
12%
7%
17%
Microsoft SSAS
15%
19%
16%
13%
1%
15%
19%
4%
2%
15%
20%
8%
3%
3%
8%
3%
30%
Microsoft SSRS
14%
16%
8%
18%
8%
16%
26%
0%
4%
10%
10%
4%
8%
18%
10%
2%
30%
MicroStrategy
18%
18%
8%
24%
6%
9%
20%
2%
3%
14%
15%
2%
9%
9%
7%
11%
23%
Oracle Essbase
Oracle BI Found.
Suite
Pentaho
11%
20%
15%
9%
7%
19%
7%
2%
2%
7%
13%
0%
2%
9%
7%
11%
31%
18%
18%
18%
18%
11%
5%
25%
0%
5%
18%
30%
5%
9%
20%
11%
11%
16%
12%
18%
15%
26%
9%
18%
21%
0%
3%
21%
6%
12%
3%
0%
0%
18%
29%
Phocas
9%
6%
1%
4%
3%
1%
4%
0%
0%
1%
4%
1%
0%
4%
7%
0%
76%
Pyramid
2%
7%
0%
5%
5%
5%
7%
0%
0%
0%
2%
0%
2%
5%
2%
7%
67%
QlikTech
17%
10%
17%
10%
5%
17%
28%
4%
5%
7%
17%
7%
4%
2%
6%
6%
31%
SAP BW
11%
13%
25%
21%
9%
12%
14%
1%
6%
38%
20%
1%
19%
21%
7%
6%
8%
SAP BO WebI
10%
10%
3%
19%
16%
20%
13%
3%
6%
23%
11%
1%
11%
21%
11%
23%
24%
SAS
3%
25%
8%
8%
3%
8%
19%
8%
0%
14%
19%
3%
22%
14%
6%
8%
28%
Tableau
17%
19%
2%
9%
9%
13%
17%
0%
4%
9%
9%
6%
0%
6%
15%
2%
49%
TARGIT
14%
5%
10%
14%
10%
7%
14%
2%
3%
24%
14%
2%
7%
3%
8%
2%
36%
Yellowfin
10%
20%
10%
3%
7%
0%
13%
0%
0%
3%
3%
3%
0%
13%
7%
3%
47%
Products
Figure 44: Frequenc y of problems anal yzed b y product (n=2082)
50
Analyzing reported problems by product reveals an interesting - and in some cases
worrying - picture (Figure 44). While users of Phocas, Pyramid Analytics and Logi
Analytics report just a few problems, a shocking 92 percent of SAP BW and 84
percent of Oracle BI Foundation Suite and IBM Cognos BI users have problems with
either their software or their project.
Figure 44 shows the problems most likely to be encountered with each product,
while Figure 45 shows the list of problems crosstabbed with the seven peer groups
Ad-hoc analysis
Enterprise
Reporting
Dashboard
OLAP Analysis
Performance
Management
Visual Analysis
and Data
Discovery
Large
International
Vendors
used in The BI Survey 13.
12%
14%
13%
11%
9%
12%
14%
Company politics
13%
14%
13%
11%
9%
11%
14%
Could not agree on requirements
12%
14%
13%
15%
15%
8%
13%
Lack of interest from business users
16%
16%
13%
16%
13%
11%
15%
6%
7%
5%
5%
4%
6%
7%
12%
10%
9%
11%
8%
10%
14%
Poor data quality
Products
14%
18%
18%
14%
12%
16%
18%
Product can not handle number of users
2%
1%
2%
2%
3%
2%
3%
Product can not handle data volume
5%
4%
3%
5%
6%
3%
5%
18%
21%
11%
19%
17%
8%
17%
13%
14%
14%
14%
13%
11%
15%
Product security limitations
3%
3%
5%
3%
2%
4%
4%
7%
10%
5%
6%
4%
6%
8%
Software not flexible enough
9%
14%
7%
7%
6%
5%
10%
Unable to get data from some systems
7%
7%
7%
6%
5%
8%
7%
Unreliable software
7%
9%
7%
8%
10%
4%
9%
33%
26%
36%
33%
39%
45%
26%
Figure 45: Frequenc y of problems anal yzed b y peer group (n=2093)
Trending topics in BI
The BI Survey collates a vast amount of data and its impressive sample size enables
us to carry out comprehensive analysis of trending topics; outshining competing
studies which tend to concentrate on single topics.
It should be noted that this document provides an overview of a selection of trending
topics from this year’s Survey. In-depth analysis of The Survey findings can be found
in our detailed ‘BI Survey 13 Trending Topics’ series.
Business intelligence is a large and growing market, and has shown increasing signs
of splitting into smaller vertical and horizontal markets in recent years. In The BI
Survey, we focus our attention on products that cover a wide spectrum of industries
and functionality. Focused analysis of Survey data is backed up by our independent
expert knowledge of the software market (gained through consulting work with
customers on their strategic issues in BI), and from the detailed product and market
analysis that we publish in ‘The BI Verdict’.
Business intelligence software exists in an environment of rapid technological
change. Software technology itself is changing too and improvements in hardware
are the main driver of technical change - such as the emergence of mobile devices
with high resolution screens - and the continuing development towards storing more
data in RAM.
The BI Survey studies more than just user attitudes to software; it analyzes market
trends and the latest buzzwords to gauge their impact on end-users. The Survey is
not only interested in user perception of recent trends, but also the plans
organizations have in place regarding new technologies and the importance of
specific functionality in their product selection processes.
Our approach to analyzing trends
In The BI Survey 13 we have incorporated a two step approach to trend analysis;
First we asked users which of the trending technologies they had already adopted, or
had plans to adopt, in their use of business intelligence.
Then we asked whether these trends were a consideration when selecting BI
software. This question explores the link between functionality and the selection
process, revealing whether the functionality vendors provide is perceived as an
important differentiator influencing buyer behavior.
The frequent inaccuracy of plans
The question of ‘planned use’ finds both users and vendors habitually overestimating
the rate, by 50 percent or more, at which new product ideas impact user experience.
The Survey’s unique long-term approach asks participants each year whether they
use – or plan to use – a given technology. By comparing responses with those from
previous years The Survey examines whether expectations for new technologies
were actually met.
A good example of this is our study of extranets, a buzzword of some importance in
the early days of The Survey. However, it has since faded into obscurity as the
concept has established itself as a technical issue that is dealt with on a much lower
level than is interesting to vendors marketing toolsets aimed at business users.
Providing business intelligence functionality to external users never gained the
popularity that had been expected. This may have been caused by the sensitivity of
the data that BI products tend to handle and the perceived lack of need to involve
outsiders in strategic decision-making processes, especially at a level detailed
enough to require the provision of access to an analysis tool.
For years, we also asked users whether they accessed their BI software using the
web (that is, with a browser) and if so, which browser they used. Results indicated
that most BI users (54 percent) access their BI tool with a browser and that users
constantly overestimated their future usage of the web. We found that small
companies avoid web-based BI, and recent Surveys showed little change in usage
from year to year.
In recent years, the renaissance of full client installations with self-service tools, such
as QlikTech, suggests a demand for software installed on a client and affirms the
continuity of our findings on web-based BI deployment.
The inaccuracy of plans has been exemplified again in recent BI Surveys (including
this year’s) when looking at planned usage of ‘mobile BI’ (see below). In fact, we
have never seen such an overestimation before.
Trends were not chosen on the assumption that they were likely to be used, but on
the basis of how often they are discussed – by vendors and market analysts alike –
and how frequently they are searched for on the internet. One of our key goals was
to distinguish between trends that really bring about changes in the way BI software
is used and the so-called trends that are just plain hype.
Status and plans for trending BI topics
The BI Survey 13 asked questions about eight major hot topics with high visibility in
the BI market. Collaboration and Mobile BI are featured for the third year in a row,
and Cloud BI/BiaaS for the second year. Self Service BI, information design,
analyzing sensor/log data, analyzing text/unstructered data and analyzing social
media data were added to this year’s Survey.
Figure 46 provides an overview of the results for actual and predicted usage over 12
months and the long term. Past editions of The BI Survey have shown that the actual
level of implementation ‘in 12 months time’ is always lower than the projected level
shown in the chart below. However, the chart still provides an interesting picture of
current usage and plans.
Self Service
51%
Collaboration
26%
Information Design
25%
Analyzing sensor/log
data
Mobile BI
Analyzing
text/unstructured data
Cloud BI/BIaaS
22%
16%
In use
11%
10%
13%
54%
5% 7%
67%
26%
7% 5% 11%
14%
Planned within 12 months
23%
50%
10% 11%
12% 6% 12%
Analyzing social
4%5%
media data
15%
23%
35%
69%
77%
76%
Planned in the long-term
Not required
Figure 46 : Significance of trending topics in BI (n=2265)
Copyright © BARC GmbH 2013. All rights reserved.
Business Application Research Center - BARC GmbH
Berliner Platz 7
97080 Würzburg
Germany
+49 (0)931 880651-0

13 BI SURVEY Partici pan

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