Small and Mid-Sized Enterprise Business Intelligence ...marketo.tibco.com/rs/221-BCQ-142/images/2016-sme-bi-market-study... · September 15, 2016 Dresner Advisory Services, LLC 2016

September 15, 2016

Dresner Advisory Services, LLC

2016 Edition

Small and Mid-Sized Enterprise Business Intelligence Market Study

Wisdom of Crowds®

Series

Licensed to TIBCO

2016 Small and Mid-Sized Enterprise Business Intelligence Market Study

http://www.dresneradvisory.com Copyright 2016 Dresner Advisory Services, LLC

2

Disclaimer

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

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

demonstrations, and proof-of-concept applications.

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

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

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

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

included in the reports as a result of its content.

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

http://www.dresneradvisory.com/



3

Business Intelligence: A Definition We define Business intelligence (BI) as “Knowledge gained through the access and analysis of business

information.”

Business Intelligence tools and technologies include query and reporting, OLAP (online analytical

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

dashboards for performance monitoring.

Howard Dresner, The Performance Management Revolution: Business Results Through Insight and Action

(John Wiley & Sons, 2007)




4

Introduction This year we celebrate the ninth anniversary of Dresner Advisory Services! Our thanks

to all of you for your continued support and ongoing encouragement.

Since our founding in 2007, we have worked hard to set the “bar” high—challenging

ourselves to innovate and lead the market—offering ever greater value with each

successive year.

Our first market report in 2010 set the stage for where we are today. Since that time, we

have expanded our agenda and have added new research topics every year since. For

2016 we are on track to release 15 major reports, including our recent flagship BI

report—in its seventh year of publication!

In addition, our ongoing coverage of key topics such as embedded BI, big data analytics

and advanced and predictive analytics, we have added new topics including Collective

InsightsTM (blending collaboration and governance) and systems integrators.

For this, our fourth SME market study report, we created a focused, detailed report

examining business intelligence in small and mid-sized organizations. In particular, we

consider how their deployments and views differ from each other and from larger

organizations. Also included is an SME buyer’s guide for 24 BI software vendors.

We hope you enjoy this report!

Best,

Howard Dresner Chief Research Officer Dresner Advisory Services




5

Contents Business Intelligence: A Definition .................................................................................. 3

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

Benefits of the Study ....................................................................................................... 7

A Consumer Guide ...................................................................................................... 7

A Supplier Tool ............................................................................................................ 7

External Awareness .................................................................................................. 7

Internal Planning ....................................................................................................... 7

About Howard Dresner and Dresner Advisory Services .................................................. 8

About Jim Ericson ........................................................................................................... 9

Survey Method and Data Collection .............................................................................. 10

Executive Summary ...................................................................................................... 12

Study Demographics ..................................................................................................... 13

Geography ................................................................................................................. 13

Functions ................................................................................................................... 14

Vertical Industries ...................................................................................................... 15

Analysis and Trends ...................................................................................................... 17

How SMEs Differ ........................................................................................................... 18

Technology Priorities Changing ................................................................................. 18

Departments/Functions Driving Business Intelligence ............................................... 20

Departmental Drivers .............................................................................................. 20

User Roles Targeted for Business Intelligence .......................................................... 22

Objectives for Business Intelligence .......................................................................... 24

Business Intelligence Objectives by Function ......................................................... 26

Penetration of Business Intelligence .......................................................................... 27

SME Success with Business Intelligence ................................................................... 30

SMEs and State of Data ............................................................................................ 32

SMEs and Action on Insight ....................................................................................... 34

Business Intelligence Market Models for SMEs ............................................................ 37

Customer Experience Model for SMEs ...................................................................... 37




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Vendor Credibility Model for SMEs ............................................................................ 39

SME Business Intelligence Buyers’ Guide .................................................................... 41

Cloud Platform Support – Vendors (B – I) .................................................................. 41

Cloud Platform Support – Vendors (K – M) ................................................................ 42

Cloud Platform Support – Vendors (O – Q) ............................................................... 43

Cloud Platform Support – Vendors (S – Y) ................................................................ 44

Mobile Platform Support Vendors (B – I) ................................................................... 45

Mobile Platform Support Vendors (K – O) .................................................................. 46

Mobile Platform Support Vendors (P – Y) .................................................................. 47

Traditional Platform Support Vendors (D – I) ............................................................. 48

Traditional Platform Support Vendors (L – O) ............................................................ 49

Traditional Platform Support Vendors (P – R) ............................................................ 50

Traditional Platform Support Vendors (S – Y) ............................................................ 51

Other Dresner Advisory Services Research Reports .................................................... 52

Appendix - The 2016 Wisdom of Crowds® Business Intelligence Market Survey

Instrument ..................................................................................................................... 53




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Benefits of the Study The DAS Small and Mid-Sized Enterprise Business Intelligence Market Study provides

a wealth of information and analysis—offering value to both consumers and producers

of business intelligence technology and services.

A Consumer Guide

As an objective source of industry research, consumers use the DAS Small and Mid-

Sized Enterprise Business Intelligence Market Study to understand how their peers use

and invest in business intelligence and related technologies.

Using our trademark vendor performance measurement system, users glean key

insights into BI software supplier performance, enabling:

Comparisons of current vendor performance to industry norms

Identification and selection of new vendors

A Supplier Tool

Vendor Licensees can use the DAS Small and Mid-Sized Enterprise Business

Intelligence Market Study in several important ways, for example to:

External Awareness

- Build awareness for the business intelligence market and supplier brand, citing the

DAS Small and Mid-Sized Enterprise Business Intelligence Market Study trends and

vendor performance

- Create lead and demand generation for supplier offerings through association with

the DAS Small and Mid-Sized Enterprise Business Intelligence Market Study brand,

findings, webinars, etc.

Internal Planning

- Refine internal product plans and align with market priorities and realities as

identified in the DAS Small and Mid-Sized Enterprise Business Intelligence Market

Study

- Better understand customer priorities, concerns, and issues

- Identify competitive pressures and opportunities




8

About Howard Dresner and Dresner Advisory Services The DAS Small and Mid-Sized Enterprise Business Intelligence Market Study was

conceived, designed and executed by Dresner Advisory Services, LLC—an

independent advisory firm—and Howard Dresner, its President, Founder and Chief

Research Officer.

Howard Dresner is one of the foremost thought leaders in business intelligence and

performance management, having coined the term “Business Intelligence” in 1989. He

has published two books on the subject, The Performance

Management Revolution – Business Results through Insight

and Action (John Wiley & Sons, Nov. 2007) and Profiles in

Performance – Business Intelligence Journeys and the

Roadmap for Change (John Wiley & Sons, Nov. 2009). He

lectures at forums around the world and is often cited by the

business and trade press.

Prior to Dresner Advisory Services, Howard served as chief

strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he

led its business intelligence research practice for 13 years.

Howard has conducted and directed numerous in-depth primary research studies over

the past two decades and is an expert in analyzing these markets.

Through the Wisdom of Crowds® Business Intelligence market research reports, we

engage with a global community to redefine how research is created and shared. Other

research reports include:

- Advanced and Predictive Analytics

- Big Data Analytics

- Business Intelligence Competency Center

- Cloud Computing and Business Intelligence

- Collective InsightsTM

- Embedded Business Intelligence

- End User Data Preparation

- Enterprise Planning

Howard (www.twitter.com/howarddresner) conducts a weekly Twitter “tweetchat” on

Fridays at 1:00 p.m. ET. The hashtag is #BIWisdom. During these live events the

#BIWisdom community discusses a wide range of business intelligence topics.

You can find more information about Dresner Advisory Services at

www.dresneradvisory.com.


http://advancedanalyticsreport.com/

http://www.bigdataanalyticsreport.com/

http://www.biccreport.com/

http://www.cloudbi.report/


http://collectiveinsights.report/


http://embeddedbireport.com/

http://enduserdataprep.report/


http://enterpriseplanningreport.com/

http://enterpriseplanningreport.com/

http://www.twitter.com/howarddresner




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About Jim Ericson Jim Ericson is Vice President and Research Director with Dresner Advisory Services.

Jim has served as a consultant and journalist who studies end-user management

practices and industry trending in the data and information management fields.

From 2004 to 2013 he was the editorial director at Information Management magazine

(formerly DM Review), where he created architectures for user and

industry coverage for hundreds of contributors across the breadth of

the data and information management industry.

As lead writer he interviewed and profiled more than 100 CIOs,

CTOs, and program directors in a 2010-2012 program called “25

Top Information Managers.” His related feature articles earned

ASBPE national bronze and multiple Mid-Atlantic region gold and

silver awards for Technical Article and for Case History feature

writing.

A panelist, interviewer, blogger, community liaison, conference co-chair, and speaker in

the data-management community, he also sponsored and co-hosted a weekly podcast

in continuous production for more than five years.

Jim’s earlier background as senior morning news producer at NBC/Mutual Radio

Networks and as managing editor of MSNBC’s first Washington, D.C. online news

bureau cemented his understanding of fact-finding, topical reporting, and serving broad

audiences.




10

Survey Method and Data Collection For this SME study, we sampled data sets from the 2016 Wisdom of Crowds Business

Intelligence Market Survey. Dresner Advisory Services defines “small enterprise” as an

organization with between one and 100 employees, “mid-sized enterprise” as an

organization with between 101 and 1,000 employees, and “large enterprise” as an

organization with more than 1,000 employees. We constructed the study from a survey

instrument to collect data and used social media and crowdsourcing techniques to

recruit participants.

Figure 1 – SME study sample

31% 30%

39%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Small (1 - 100) Mid (101 - 1000) Large (1001+)

SME Study Sample




11

Executive

Summary




12

Executive Summary

Small and mid-sized enterprises prioritize a wide span of BI technologies and

initiatives. Top priorities include "nuts and bolts" dashboards, data discovery, and

reporting. Compared to large peers, small organizations are more interested in

cloud, open source, social media analysis, and less so in data warehousing (p. 18).

Top SME technology priorities have remained consistent over time, but sentiment

has cooled somewhat (p. 19).

While operations and executive management are the largest drivers of BI at

organizations of any size, sales, and to a lesser effect marketing, take bigger roles at

SMEs (p. 20). SME BI driver momentum across all functions has cooled somewhat

over time (p. 21).

SMEs (with thinner management ranks), are most likely to target BI toward

executives and have higher interest in customer empowerment (p. 22- 23).

Small organizations are more likely to focus outwardly with BI initiative objectives

(revenues, competitive advantage); large organizations are geared more toward

operational efficiency (p. 24-26).

Current BI penetration is much higher and future plans are much more aggressive at

small compared to large organizations (p. 27-29).

Estimates of success with BI are highest at small organizations; expectations of BI

success for the most part decrease as organization size increases (p. 30).

Organizational estimates of trustworthy data and ability to take action based on data

decrease as organization size increases (p. 31-35)




13

Study Demographics The respondents studied in this SME survey provide a cross-section of geographies,

functions, organization sizes, and vertical industries. We believe this is a representative

sample and more useful indicator of true market dynamics. We constructed cross-tab

analyses using these demographics to identify and illustrate important industry trends.

Geography

In our 2016 study, 56 percent of respondents are located in North America, which

includes the United States, Canada, and Puerto Rico (fig. 2). EMEA organizations

account for 29 percent of respondents. Asia Pacific (10 percent) and Latin America (4

percent) are the other regions represented.

Figure 2 – SME geographies represented

North America, 56.4%

Europe, Middle East and Africa, 29.1%

Asia Pacific, 10.4%

Latin America, 4.1%

SME Geographies Represented




14

Functions

Information technology (29 percent) and executive management (28 percent) are the

functions most represented in the study. Ten percent of respondents represent the

business intelligence competency center (BICC), which in the SME market can include

dedicated BI resources as well as formal organizational departments (fig. 3). Finance

and marketing/sales are the next most represented.

Measuring attitudes and behaviors by function helps us develop analyses comparing

and contrasting the plans and priorities of the different departments within

organizations.

Figure 3 – SME functions represented

29% 28%

10% 8%

6% 4% 4%

3% 2%

6%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

SME Functions Represented




15

Vertical Industries

Vertical industry distribution in our 2016 study is led by consulting followed by

technology and healthcare (fig. 4). Education, financial services, business services, and

manufacturing are the next most represented.

Figure 4 – SME vertical industries represented

14%

11%

10%

7% 7% 6% 6%

4%

3% 3% 3% 2% 2% 2% 1% 1% 1% 1% 1% 1%

11%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

SME Vertical Industries Represented




16

Analysis and

Trends




17

Analysis and Trends This report describes the small and midsized enterprise market for business intelligence

by its own characteristics, drivers, trends, and also by how it compares to the large

enterprise market.

In 2016 we sampled SME experience with business intelligence including the uptake of

technologies, year over year and future plans. As in our larger Wisdom of Crowds study,

we collected and analyzed data for SMEs surrounding functions driving business

intelligence, goals/objectives for BI, targeted user roles, current penetration, and future

plans for business intelligence deployment and organizational success.




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How SMEs Differ

Technology Priorities Changing

Much like large organizations, small and mid-sized enterprises prioritize a wide span of

BI technologies and initiatives (fig. 5). Top priorities common to organizations of any

size include "nuts and bolts" dashboards, data discovery, and reporting. However, there

are significant distinctions between small and large organizations elsewhere. Large

organizations are much more likely to prioritize data warehousing, and they have

somewhat more extensive reporting requirements. Large organizations are more

interested in data mining, enterprise planning, governance, and big data, among other

areas. Small and mid-sized organizations are predictably much more interested in cloud

computing and, to a lesser extent, open source and social media analysis. Generally,

we find small organizations are early adopters where risk exposure and time/cost of

implementation are lower.

Figure 5 - Technology priorities by organization size

1

1.5

2

2.5

3

3.5

4

4.5Dashboards

ReportingAdvanced visualization

Data discovery

End-user "self service"

Data warehousing

Data storytelling

Integration with operational processes

Data mining, advanced algorithms,…

Mobile device support

Enterprise planning/budgeting

Embedded BI (contained within an…

Software-as-a-Service and cloud…

Collaborative support for group-based…End-user data preparation and blending

GovernanceSearch-based interface

In-memory analysis

Location intelligence/analytics

Ability to write to transactional…

Big Data (e.g., Hadoop)

Pre-packaged vertical/functional…

Streaming data analysis

Open source software

Social media analysis (Social BI)

Text analytics

Cognitive BI (e.g., Artificial Intelligence-…

Complex event processing (CEP)

Internet of Things (IoT)Edge computing

Technology Priorities by Organization Size

Small (1 - 100) Mid (101 - 1000) Large (1001+)




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SME technology priority rankings have remained mostly consistent across four years of

study with some cooling of interest over time (fig. 6). We believe this reflects good

market awareness and some maturing of planning in response to BI provider marketing

and industry trends. Areas of sustained high interest include mainstays such as

dashboards, reporting, and advanced visualization. Data discovery gained momentum

in 2016. Interest in mobile, software as a service, end-user data preparation and

search-based interface have all diminished somewhat, though all remain in the top one-

half of respondent priorities. Pre-packaged applications, which we would expect to

appeal to the SME audience, fell most sharply across four years of study.

Figure 6 – SME technology priorities 2013 to 2016

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

DashboardsReporting

End-user "self service"

Advanced visualization

Data Discovery

Data Warehousing

Integration with operational processes

Mobile device support

Data storytelling

Data mining, advanced algorithms,predictive

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

Enterprise planning/budgeting

Software-as-a-Service and "Cloud"computing

End-user data preparation and blendingCollaborative support for group-based

analysisSearch-based interface

In-memory analysis

Governance

Location intelligence/analytics

Pre-packaged vertical/functionalanalytical applications

Ability to write to transactionalapplications

Big Data (e.g., Hadoop)

Text analytics

Social media analysis (Social BI)


Open source software

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

Complex event processing (CEP)

Internet of Things (IoT)

Edge computing

SME Technology Priorities 2013 to 2016

2013 2014 2015 2016




20

Departments/Functions Driving Business Intelligence

Our 2016 survey looks at the functions that drive business intelligence initiatives within

the organization. We asked respondents to specify whether a given function drives

business intelligence “always,” “often,” “sometimes,” “rarely,” or “never.” We used this to

create a weighted average on a one-to-five scale.

Departmental Drivers

While operations and executive management are the largest drivers of BI at

organizations of any size, sales, and to a lesser effect marketing, take bigger roles at

SMEs (fig. 7). Finance is a stronger driver in mid-sized and larger organizations and,

predictably, IT influence as a central BI authority grows with organization headcount.

BICC, supply chain, and human resource influence are stronger in large organizations,

which are more likely to support these roles.

Figure 7 – Functions driving business intelligence by organization size

1

1.5

2

2.5

3

3.5

4

4.5

Functions Driving Business Intelligence by Organization Size

Small (1 - 100) Mid (101 - 1000) Large (1001+)




21

Like BI technology priorities, SME drivers of business intelligence have mostly held rank

across four years of study, albeit with some cooling of interest (fig. 8). The obvious

exception to this trend is in operations, which gained influence and jumped three or four

places to become the top SME departmental driver in 2016. We expect the growth in

operational influence could be a nod to efficiency and establishment of leading

institutional BI best practices in SMEs. That said, SMEs still maintain a broad range of

drivers across roles and departments.

Figure 8 - SME drivers of BI 2013 to 2016

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

SME Drivers of BI 2013 to 2016

2013 2014 2015 2016




22

User Roles Targeted for Business Intelligence

Our survey asked which functions/roles are targeted with business intelligence

solutions. Respondents were able to designate these roles as either “primary,”

“secondary,” or “not applicable.” Among all organizations sampled, the majority

prioritized (in order) executives, middle managers, line managers, individuals,

customers, and suppliers.

Small and mid-sized enterprises, which by definition have smaller middle management

ranks than larger peers, are most likely to target executives over other roles (fig. 9).

SMEs are also less likely to target individual contributors but are most likely (almost 30

percent) to target customers with business intelligence. Suppliers remain the least

interested BI target, with less than 5 percent interest among organizations of any size

large or small.

Figure 9 - Primary targeted users for business intelligence by organization size

0%

10%

20%

30%

40%

50%

60%

70%

Executives Middlemanagers

Line managers Individualcontributors &professionals

Customers Suppliers

Primary Targeted Users for Business Intelligence by Organization Size

Small (1 - 100) Mid (101 - 1000) Large (1001+)




23

Across four years of study, BI targeting at SMEs has incrementally cooled, perhaps in

light of maturing practices that delivered existing constituencies of users (fig. 10).

Interest in BI targeting of customers nonetheless ticked up slightly in 2016 while

executive, manager, and individual targeting was lower year over year. (Fig. 10

combines small and mid-sized organizations, which have some differences in

sentiment.)

Figure 10 - SME targets for BI 2013 to 2016

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Executives MiddleManagers

Line Managers IndividualContributors &Professionals

Customers Suppliers

SME Targets for BI 2013 - 2016

2013 2014 2015 2016




24

Objectives for Business Intelligence

BI objectives are somewhat consistent across organizations of different sizes with

subtle differences (fig. 11). In a general sense, small organizations are a bit more likely

to focus outwardly (revenues, competitive advantage) and are more focused on

customers than larger peers. Among all organizations of any size, “better decision

making” is the most-cited objective and a goal that gains momentum with organization

size. Likewise, operational efficiency is a goal associated with scale and complexity of

business processes and thus is more likely to appeal to large organizations.

Figure 11 - Business intelligence objectives by organization size

1

1.5

2

2.5

3

3.5

4

4.5

5

Better decision-making

Improvedoperationalefficiency

Growth inrevenues

Increasedcompetitiveadvantage

Enhancedcustomer service

Business Intelligence Objectives by Organization Size

Small (1 - 100) Mid (101 - 1000) Large (1001+)




25

Across four years of data, SME sentiment toward all BI objectives has cooled slightly,

though prioritized rankings have held fast (fig. 12). "Better decision making" is

consistently the top choice (though less pronounced at SMEs than large enterprises as

noted in fig. 11, p. 24). No priority falls below a mean value of 3.5, indicating "very high"

importance.

Figure 12 – SME BI objectives 2014 to 2016

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Better decisionmaking

Improvedoperationalefficiency

Growth in revenues Increasedcompetitiveadvantage

Enhanced customerservice

SME BI Objectives 2013 - 2016

2013 2014 2015 2016




26

Business Intelligence Objectives by Function

Across SMEs (and our entire sample), "better decision making" is the perennial top BI

objective of organizations, which tells us that specific functions take an opportunistic

approach as market conditions change. In compartmentalized roles, it's predictable to

find marketing and sales concerned with growth and revenues while finance focuses on

efficiencies (fig. 13). BICC likewise takes above-mean interest in competitive advantage

as well as enhanced customer service.

Figure 13 – BI objectives by function: SMEs Only

1

1.5

2

2.5

3

3.5

4

4.5

5

Better decision-making Improved operationalefficiency

Growth in revenues Increased competitiveadvantage

Enhanced customerservice

Business Intelligence Objectives by Function: SMEs Only

Executive Management Marketing and Sales

Information Technology (IT) Finance

Business Intelligence Competency Center Mean




27

Penetration of Business Intelligence

As is historically the case, SMEs in 2016 report much higher levels of business

intelligence penetration than larger organizations, a finding that can be accounted for in

part by simple headcount (fig. 14). Not to underestimate this SME BI pervasiveness,

small enterprises (1-100 employees) are almost three times as likely as large

organizations to report the highest (81 percent or more) BI penetration and significantly

less likely to report the lowest levels of penetration. Mid-sized organizations (100-1,000

employees) also report their BI penetration is greater than large peers but by a much

smaller margin.

Figure 14 – Business intelligence penetration today by organization size

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

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

Business Intelligence Penetration Today by Organization Size

Small (1 - 100) Mid (101 - 1000) Large (1001+)




28

In addition to high existing BI penetration, small organizations are much more

aggressive in their plans to reach higher levels of BI penetration in 12, 24, and 36-

month time frames (fig. 15). This finding, in part, reflects the agility of small

organizations to adopt at lower cost and accept risk more easily than larger peers. To a

lesser degree, the sentiment to increase high-level penetration extends to mid-sized

peers. Large organizations are more likely to pursue future deployments focused on low

and mid-level enterprise BI strategies that "lift all boats."

Figure 15 – Planned business intelligence penetration through 2019 by organization size

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

In 12months

In 24months

In 36months

In 12months

In 24months

In 36months

In 12months

In 24months

In 36months

Small (1 - 100) Mid (101 - 1000) Large (1001+)

Planned Business Intelligence Penetration through 2019 by Organization Size

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




29

Across three years of data, SMEs have reported growing levels of BI penetration (fig.

16). As percentages for low-level penetration (<10 percent, 11-20 percent, 21-40

percent) have decreased and mid-level (41-60 percent) has flattened, the highest levels

of penetration (up to 81 percent or greater) increased in mean value over time. We

believe this trend helps affirm and validate our case that BI adoption is stronger at

SMEs than at larger organizations.

Figure 16 - SME BI penetration 2014 to 2016

0%

5%

10%

15%

20%

25%

30%

35%

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

SME BI Penetration 2014 - 2016

2014

2015

2016




30

SME Success with Business Intelligence

The likelihood of reporting "complete success" with business intelligence programs and

initiatives is, by far, most pronounced in small (1-100 employees) organizations (fig. 17).

Estimations of "complete success" thereafter decrease as organization size (along with

cost and complexity) increases. Mid-sized (100-1,000 employees) and larger

organizations are incrementally more likely to "agree somewhat" that their BI work has

been successful. Fewer than 10 percent of all organizations "disagree somewhat" that

BI has been successful, and only a handful (3 percent or less) "disagree" summarily.

Figure 17 - Success with business intelligence by organization size

0

0.1

0.2

0.3

0.4

0.5

0.6

Completely agree Agree somewhat Disagree somewhat Disagree

Success with Business Intelligence by Organization Size

Small (1 - 100) Mid (101 - 1000) Large (1001+)




31

Over time, SME estimates of BI success have wavered somewhat. The 2013 high-water

mark for "complete success" dwindled in 2015 before rebounding slightly in our 2016

survey (fig. 18). Correspondingly, the number that "somewhat agree" that their BI work

has been successful increased over time to offset this decline. That said, sentiment

regarding these two top levels of achievement have remained between 40 percent and

48 percent in the four years of our survey. We find it a positive that lower estimates

("somewhat disagree," "disagree") are low and have remained steady over time,

perhaps confined to a few insistent holdouts/doubters and perhaps a few hapless

organizations.

Figure 18 - SME and BI success 2013 to 2016

0%

10%

20%

30%

40%

50%

60%

Completely agree Agree somewhat Disagree somewhat Disagree

SME and BI Success 2013 - 2016

2013

2014

2015

2016




32

SMEs and State of Data

An organization's opinion of its state of data governance and consistency decreases as

the size of the organization increases (fig. 18). Well more than one-third of closer-knit,

small (1-100 employees) enterprises have the highest view of their governance being at

the level of “data as truth.” More than 40 percent of small and mid-sized organizations

claim "a common view of enterprise data," somewhat ahead of large organizations.

Large organizations are more likely than SMEs to report department-level or multiple

inconsistent data sources.

Figure 19 – Business intelligence and the state of data by organization size

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Small (1 - 100) Mid (101 - 1000) Large (1001+)

Business Intelligence and the State of Data by Organization Size

Data as "truth" - A common viewof enterprise data is available withcommon application of data,filters, rules, and semantics

A common view of enterprise datais available. However, parochialviews and semantics are used tosupport specific positions

Consistent data is available at adepartmental level. Conflicting,functional views of data causesconfusion and disagreement

We have multiple, inconsistentdata sources with conflictingsemantics and data. Information isgenerally unreliable and distrusted




33

SME estimations of their "state of data" has dwindled slightly across three years of

study (fig 20). Compared to the linear success at small versus mid-size and larger

organizations (fig. 19), within the SME realm (of both small and mid-sized organizations)

sentiment is less consistent. While "data as truth" held steady in 2015 and 2016, the

common but "parochial" sentiment fell as less coordinated, and "conflicting functional

views" gained a bit. Overall, these sentiments are nonetheless more positive than found

in large (>1,000 employees) organizations.

Figure 20 - SME state of data 2014 to 2016

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Data as "truth" - A common view of enterprisedata is available with common application of

data, filters, rules, and semantics

A common view of enterprise data is available.However, parochial views and semantics are

used to support specific positions

Consistent data is available at a departmentallevel. Conflicting, functional views of data

causes confusion and disagreement

We have multiple, inconsistent data sourceswith conflicting semantics and data.

Information is generally unreliable anddistrusted

SME State of Data 2014 - 2016

2014

2015

2016




34

SMEs and Action on Insight

Action on Insight is Dresner Advisory’s high-level self-assessment of BI best (and worst)

practices. As we would expect (based on other findings), small (1-100 employees)

organizations to report the most instances of "closed-loop processes” (fig. 21). We also

note that mid-sized organizations report fewer closed-loop processes than either small

or large organizations. Mid-sized organizations report more instances of “ad hoc

informal action” than either small or large organizations. A notable 15 percent of large

organizations (twice as many as small) report uncoordinated/parochial Action on Insight,

undoubtedly attributable to the greater complexities of scale.

Figure 21 – Business intelligence and action on insight by organization size

0% 10% 20% 30% 40% 50% 60% 70%

Insights are rarely leveraged

Uncoordinated/ parochial action (sometimes at theexpense of others)

Ad hoc (informal) action on insights across functions

"Closed loop" - Information is shared, teams work toprocess and act in a timely fashion. No formal

boundaries

Business Intelligence and Action on Insight by Organization Size

Large (1001+) Mid (101 - 1000) Small (1 - 100)




35

SMEs’ ability to take Action on Insight improved between 2015 and 2016 (fig. 22). The

top two capabilities ("closed-loop processes," "ad hoc/informal action") both improved

while the remaining lesser capabilities declined. In 2016, fewer than 10 percent of

respondents report "uncoordinated/parochial" action and less than 5 percent say

insights are "rarely leveraged." (For reasons that are not immediately explicable,

negative attitudes toward Action on Insight ebbed noticeably in 2015.)

Figure 22 - SME action on insight 2014 to 2016

0% 10% 20% 30% 40% 50% 60% 70%

Closed loop” processes ensure timely, concerted action

Ad hoc (informal) action on insights acrossfunctions

Uncoordinated/ parochial action (sometimes atthe expense of others)

Insights are rarely leveraged

SME Action on Insight 2014 - 2016

2014

2015

2016




36

SME Vendor

Rankings




37

Business Intelligence Market Models for SMEs

Starting in 2015 we developed two new models for examining and understanding the

business intelligence market. Using quadrants, we plotted aggregated user sentiment

into x and y axes. These ratings reflect the opinions of SME organizations, in contrast to

a broader community reflected in our “flagship” 2016 Wisdom of Crowds Business

Intelligence Market Study Report.

Customer Experience Model for SMEs

The customer experience model considers the real-world experience of customers

working with BI products on a daily basis (fig. 23). For the x axis, we included all vendor

touch points—including the sales and acquisition process, technical support, and

consulting services—into a single “sales and service” dimension. On the y axis, we

plotted customer sentiment surrounding product, derived from 12 product and

technology measures used to rank vendors. On the resulting four quadrants, we plotted

vendors based on these measures.

The upper-right quadrant contains the highest-scoring vendors and is named “overall

experience leaders.” Technology leaders (upper-left quadrant) identifies vendors with

strong product offerings but relatively lower services scores. Contenders (lower-left

quadrant) would benefit from varying degrees of improvement to product, services, or

both.

User sentiment surrounding outliers (outside of the four quadrants) suggests that

significant improvements are required to product and services.




38

Figure 23 – Vendor rankings based on Customer Experience Model for SMEs




39

Vendor Credibility Model for SMEs

The vendor credibility model considers how customers “feel” about their vendor (fig. 24).

The x axis plots perceived value for the price paid. The y axis combines integrity and

recommend measures, creating a “confidence” dimension. The resulting four quadrants

position vendors based on these dimensions.

The upper-right quadrant contains the highest-scoring vendors and is named “credibility

leaders.” Trust leaders (upper-left quadrant) identifies vendors with solid perceived

confidence but relatively lower value scores. Contenders (lower-left quadrant) would

benefit by working to improve customer value, confidence, or both.

User sentiment surrounding outliers (outside of the four quadrants) suggests that

significant improvements are required to improve perceived value and confidence.




40

Figure 24 - Vendor rankings based on Vendor Credibility Model for SMEs




41

SME Business Intelligence Buyers’ Guide

In this section we present a Business Intelligence Buyers’ Guide organized by key

platforms: Traditional, Cloud, and Mobile. For each vendor, we share data collected for

26 different areas of current capability.

An “X” indicates a feature that was available in a vendor’s product during Q1 2016.

Cloud Platform Support – Vendors (B – I)

Feature Birst Dimensional

Insight GoodData IBM Infor Information

Builders

Ability to write to transactional applications X X X X X X

Ad-hoc query X X X X X X

Advanced visualization X X X X X X

Big data support X X X X X

Cognitive computing X

Collaborative support X X X X X

Complex Event Processing (CEP) X X

Custom CSS X X X

Data storytelling X X X

Data integration / data quality tools / ETL X X X X

Data mining and advanced algorithms X X X X X

Data visualization X X X X X X

Embedded BI X X X X X X

End-user "self-service" X X X X X X

End-user data "blending" or "mashups" X X X X X X

In-memory support X X X X X X

Interactive analysis X X X X X X

Location intelligence/analytics X X X X X

Personalized dashboards X X X X X X

Pre-packaged vertical/functional analytical applications X X X X X X

Production reporting X X X X X X

Search-based interface X X X X

Social media analysis (Social BI) X X X X


Text analytics X X X

User/content governance X X X X X




42

Cloud Platform Support – Vendors (K – M)

Feature Klipfolio Logi

Analytics Looker Microsoft MicroStr

ategy

Ability to write to transactional applications X X

Ad-hoc query X X X X X

Advanced visualization X X X X X

Big data support X X X X


Collaborative support X X X X

Complex Event Processing (CEP) X X X X

Custom CSS X X X X

Data storytelling X X

Data integration / data quality tools / ETL X X

Data mining and advanced algorithms X X

Data visualization X X X X

Embedded BI X X X X X

End-user "self-service" X X X X X

End-user data "blending" or "mashups" X X X X X

In-memory support X X X X X

Interactive analysis X X X X

Location intelligence/analytics X X X X

Personalized dashboards X X X X X

Pre-packaged vertical/functional analytical applications X X X X

Production reporting X X X X

Search-based interface X X X


Streaming data analysis X X X






43

Cloud Platform Support – Vendors (O – Q)

Feature OpenText Oracle Pentaho Pyramid Analytics Qlik

Ability to write to transactional applications X X X





Collaborative support X X X X


Custom CSS X X

Data storytelling X X X X

Data integration / data quality tools / ETL X X X X X

Data mining and advanced algorithms X X X X

Data visualization X X X X X



End-user data "blending" or "mashups" X X X X

In-memory support X X X X

Interactive analysis X X X X X



Pre-packaged vertical/functional analytical applications X


Search-based interface X X



Text analytics X X X X

User/content governance X X X




44

Cloud Platform Support – Vendors (S – Y)

Feature SAP Sisense Tableau TIBCO Yellowfin

Ability to write to transactional applications X




Cognitive computing


Complex Event Processing (CEP) X

Custom CSS X X X



Data mining and advanced algorithms X X X









Pre-packaged vertical/functional analytical applications X X X

Production reporting X X X X X


Social media analysis (Social BI) X X X


Text analytics X

User/content governance X X X X




45

Mobile Platform Support Vendors (B – I)

Feature Birst Dimensional

Insight Dundas IBM Infor Information

Builders

Ability to write to transactional applications X X X X





Collaborative support X X X


Custom CSS X X


Data integration / data quality tools / ETL X





End-user data "blending" or "mashups" X X





Pre-packaged vertical/functional analytical applications X X X X X


Search-based interface X X X X X



Text analytics X





46

Mobile Platform Support Vendors (K – O)

Feature Klipfolio Logi

Analytics Looker MicroStrategy OpenText Oracle







Complex Event Processing (CEP) X X X X

Custom CSS X X X X


Data integration / data quality tools / ETL X X X












Search-based interface X X X

Social media analysis (Social BI) X X X X X


Text analytics X X X X





47

Mobile Platform Support Vendors (P – Y)

Feature Pentaho Pyramid Analytics Qlik SAP Sisense Tableau Yellowfin



Advanced visualization X X X X X X X

Big data support X X X X X X X

Cognitive computing

Collaborative support X X X X X X

Complex Event Processing (CEP) X X X

Custom CSS X X X X

Data storytelling X X X X X



Data visualization X X X X X X X


End-user "self-service" X X X X X X X

End user data "blending" or "mashups" X X X X X X


Interactive analysis X X X X X X X


Personalized dashboards X X X X X X X



Search-based interface X X X X X

Social media analysis (Social BI) X X X X X X



User/content governance X X X X X X X




48

Traditional Platform Support Vendors (D – I)

Feature Dimensional

Insight Dundas IBM Infor Information

Builders








Custom CSS X X X X

















Text analytics X X





49

Traditional Platform Support Vendors (L – O)

Feature Logi

Analytics Looker Microsoft MicroStrategy OpenText Oracle



Advanced visualization X X X X X X

Big data support X X X X X X

Cognitive computing X X


Complex Event Processing (CEP) X X X X X X

Custom CSS X X X X X X

Data storytelling X X X X X









Location intelligence/analytics X X X X X X


Pre-packaged vertical/functional analytical applications X X X X X



Social media analysis (Social BI) X X X X X X

Streaming data analysis X X X X X

Text analytics X X X X X

User/content governance X X X X X X




50

Traditional Platform Support Vendors (P – R)

Feature Pentaho Pyramid Analytics Qlik RapidMiner


Ad-hoc query X X X X

Advanced visualization X X X X



Collaborative support X X X


Custom CSS X X X

Data storytelling X X



Data visualization X X X X

Embedded BI X X X X

End-user "self-service" X X X X

End-user data "blending" or "mashups" X X X X

In-memory support X X X X

Interactive analysis X X X

Location intelligence/analytics X X X

Personalized dashboards X X X X

Pre-packaged vertical/functional analytical applications X


Search-based interface X


Streaming data analysis X X






51

Traditional Platform Support Vendors (S – Y)

Feature SAP Sisense Tableau TIBCO Yellowfin

Ability to write to transactional applications X X X




Cognitive computing


Complex Event Processing (CEP) X X X

Custom CSS X X X X



Data mining and advanced algorithms X X X









Pre-packaged vertical/functional analytical applications X X X




Streaming data analysis X X

Text analytics X X





52

Other Dresner Advisory Services Research Reports

- “Flagship” Wisdom of Crowds Business Intelligence Market Study

- Advanced and Predictive Analytics

- Big Data Analytics

- Business Intelligence Competency Center

- Cloud Computing and Business Intelligence

- Collective InsightsTM

- Embedded Business Intelligence

- End User Data Preparation

- “Flagship” Wisdom of Crowds Enterprise Planning market Study

- Internet of Things and Business Intelligence

- Location Intelligence

- Mobile Computing and Business Intelligence


http://www.biwisdom.com/

http://advancedanalyticsreport.com/

http://www.bigdataanalyticsreport.com/

http://www.biccreport.com/





http://embeddedbireport.com/



http://www.enterpriseplanningreport.com/

http://www.iotindustryreport.com/

http://www.locationintelligence.report/

http://mobilecomputingreport.com/



53

Appendix - The 2016 Wisdom of Crowds® Business Intelligence Market

Survey Instrument

Please enter your contact information below

First Name*: _________________________________________________

Last Name*: _________________________________________________

Title: _________________________________________________

Company Name*: _________________________________________________

Street Address: _________________________________________________

City: _________________________________________________

State: _________________________________________________

Zip: _________________________________________________

Country: _________________________________________________

Email Address*: _________________________________________________

Phone Number: _________________________________________________

URL: _________________________________________________

May we contact you to discuss your responses and for additional information?

( ) Yes

( ) No

What major geography do you reside in?*

( ) North America

( ) Europe, Middle East and Africa

( ) Latin America

( ) Asia Pacific




54

Please identify your primary industry*

( ) Advertising

( ) Aerospace

( ) Agriculture

( ) Apparel & accessories

( ) Automotive

( ) Aviation

( ) Biotechnology

( ) Broadcasting

( ) Business services

( ) Chemical

( ) Construction

( ) Consulting

( ) Consumer products

( ) Defense

( ) Distribution & logistics

( ) Education (Higher Ed)

( ) Education (K-12)

( ) Energy

( ) Entertainment and leisure

( ) Executive search

( ) Federal government

( ) Financial services

( ) Food, beverage and tobacco




55

( ) Healthcare

( ) Hospitality

( ) Insurance

( ) Legal

( ) Manufacturing

( ) Mining

( ) Motion picture and video

( ) Not for profit

( ) Pharmaceuticals

( ) Publishing

( ) Real estate

( ) Retail & wholesale

( ) Sports

( ) State and local government

( ) Technology

( ) Telecommunications

( ) Transportation

( ) Utilities

( ) Other - Write In: _________________________________________________




56

How many employees does your company employ worldwide?

( ) 1 - 100

( ) 101 - 1,000

( ) 1,001 - 2,000

( ) 2,001 - 5,000

( ) 5,001 - 10,000

( ) More than 10,000

What function do you report into?*

( ) Business Intelligence Competency Center

( ) Executive management

( ) Faculty (Education)

( ) Finance

( ) Human resources

( ) Information Technology (IT)

( ) Manufacturing

( ) Marketing

( ) Medical staff (Healthcare)

( ) Operations

( ) Research and development (R&D)

( ) Sales

( ) Strategic planning function

( ) Supply chain

( ) Other - Write In: _________________________________________________




57

Please respond to the following statement: "My organization considers our Business

Intelligence initiatives a success.

( ) Completely agree

( ) Agree somewhat

( ) Disagree somewhat

( ) Disagree

Why has your organization been successful or unsuccessful?

____________________________________________

____________________________________________

____________________________________________

____________________________________________

Which function drives your Business Intelligence initiatives?

Always Often Sometimes Rarely Never

Operations ( ) ( ) ( ) ( ) ( )

Faculty

(education)

( ) ( ) ( ) ( ) ( )

Business

Intelligence

Competency

Center

( ) ( ) ( ) ( ) ( )

Sales ( ) ( ) ( ) ( ) ( )

Finance ( ) ( ) ( ) ( ) ( )

Research and ( ) ( ) ( ) ( ) ( )




58

development

(R&D)

Information

Technology

(IT)

( ) ( ) ( ) ( ) ( )

Clinical

(Healthcare)

( ) ( ) ( ) ( ) ( )

Human

resources

( ) ( ) ( ) ( ) ( )

Supply chain ( ) ( ) ( ) ( ) ( )

Executive

management

( ) ( ) ( ) ( ) ( )

Marketing ( ) ( ) ( ) ( ) ( )

Manufacturing ( ) ( ) ( ) ( ) ( )

Strategic

planning

function

( ) ( ) ( ) ( ) ( )

What does your organization expect to achieve with Business Intelligence?

Critical

Very

important Important

Somewhat

important Unimportant

Better

decision-

making

( ) ( ) ( ) ( ) ( )

Growth in

revenues

( ) ( ) ( ) ( ) ( )

Improved

operational

( ) ( ) ( ) ( ) ( )




59

efficiency

Enhanced

customer

service

( ) ( ) ( ) ( ) ( )

Increased

competitive

advantage

( ) ( ) ( ) ( ) ( )

Who are the targeted consumers of Business Intelligence within your organization?

Primary Secondary

Not

targeted

Executives ( ) ( ) ( )

Middle

managers

( ) ( ) ( )

Line

managers

( ) ( ) ( )

Individual

contributors

&

professionals

( ) ( ) ( )

Customers ( ) ( ) ( )

Suppliers ( ) ( ) ( )

What percentage of all employees have access to Business Intelligence solutions?

Under

10%

11 -

20%

21 -

40%

41 -

60%

61 -

80%

81%

or

more




60

Today ( ) ( ) ( ) ( ) ( ) ( )

In 12

months

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

In 24

months

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

In 36

months

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

Please choose one of the following to describe the state of data governance in your

organization.

( ) Data as "truth" - A common view of enterprise data is available with common

application of data, filters, rules, and semantics.

( ) A common view of enterprise data is available. However, parochial views and

semantics are used to support specific positions

( ) Consistent data is available at a departmental level. Conflicting, functional views of

data causes confusion and disagreement

( ) We have multiple, inconsistent data sources with conflicting semantics and data.

Information is generally unreliable and distrusted

How do people in your organization take advantage of insights learned from Business

Intelligence solutions?

( ) “Closed loop” - Information is shared, teams work to process and act in a timely

fashion. No formal boundaries

( ) Ad hoc (informal) action on insights across functions

( ) Uncoordinated/ parochial action (sometimes at the expense of others)

( ) Insights are rarely leveraged




61

Please indicate the importance of the following technologies to your Business

Intelligence strategy and plans.

Critical

Very

important Important

Somewhat

important

Not

important

Ability to write to

transactional

applications

( ) ( ) ( ) ( ) ( )

Advanced

visualization

( ) ( ) ( ) ( ) ( )

Big Data (e.g.,

Hadoop)

( ) ( ) ( ) ( ) ( )

Cognitive BI (e.g.,

Artificial

Intelligence-based

BI)

( ) ( ) ( ) ( ) ( )

Collaborative

support for group-

based analysis

( ) ( ) ( ) ( ) ( )

Complex event

processing (CEP)

( ) ( ) ( ) ( ) ( )

Dashboards ( ) ( ) ( ) ( ) ( )

Data discovery ( ) ( ) ( ) ( ) ( )

Data mining,

advanced

algorithms,

predictive

( ) ( ) ( ) ( ) ( )

Data story telling ( ) ( ) ( ) ( ) ( )

Data warehousing ( ) ( ) ( ) ( ) ( )

Edge computing ( ) ( ) ( ) ( ) ( )




62

Embedded BI

(contained within an

application, portal,

etc.)

( ) ( ) ( ) ( ) ( )

End user "self

service"

( ) ( ) ( ) ( ) ( )

End user data

preparation and

blending

( ) ( ) ( ) ( ) ( )

Enterprise

planning/budgeting

( ) ( ) ( ) ( ) ( )

Governance ( ) ( ) ( ) ( ) ( )

In-memory analysis ( ) ( ) ( ) ( ) ( )

Integration with

operational

processes

( ) ( ) ( ) ( ) ( )

Internet of things

(IoT)

( ) ( ) ( ) ( ) ( )

Location

intelligence/analytics

( ) ( ) ( ) ( ) ( )

Mobile device

support

( ) ( ) ( ) ( ) ( )

Open source

software

( ) ( ) ( ) ( ) ( )

Pre-packaged

vertical/functional

analytical

applications

( ) ( ) ( ) ( ) ( )

Reporting ( ) ( ) ( ) ( ) ( )




63

Search-based

interface

( ) ( ) ( ) ( ) ( )

Social media

analysis (Social BI)

( ) ( ) ( ) ( ) ( )

Software-as-a-

service and cloud

computing

( ) ( ) ( ) ( ) ( )

Streaming data

analysis

( ) ( ) ( ) ( ) ( )

Text analytics ( ) ( ) ( ) ( ) ( )

How many Business Intelligence products are currently being used in your organization

today?

( ) Don't know

( ) 1

( ) 2

( ) 3

( ) 4

( ) 5

( ) 6

( ) 7

( ) 8

( ) 9

( ) 10 or more




64

Business Intelligence Vendor Ratings

Please select one vendor to rate

( ) 1010 Data

( ) Actuate (OpenText)

( ) Adaptive Insights

( ) Advizor Solutions

( ) Alpine Data Labs

( ) Alteryx

( ) Altosoft

( ) Arcplan (Longview)

( ) Bime (Zendesk)

( ) Birst

( ) Bitam

( ) Board

( ) ClearStory

( ) Cubeware

( ) Datameer

( ) Datawatch (inc. Panopticon)

( ) Decisyon

( ) Dell Statistica

( ) Dimensional Insight

( ) Domo

( ) Dundas

( ) Entrinsik




65

( ) Exago

( ) Good Data

( ) IBM/Cognos/SPSS

( ) iDashboards

( ) Inetsoft

( ) Infor/Lawson

( ) Information Builders (IBI)

( ) IntuitiveBI

( ) Izenda

( ) Jedox

( ) Jinfonet/JReport

( ) Klipfolio

( ) Knime

( ) Lavastorm

( ) LogiAnalytics

( ) Looker

( ) Microsoft

( ) MicroStrategy

( ) Neudesic

( ) NeutrinoBI

( ) Oracle

( ) Panorama

( ) Pentaho

( ) Phocas




66

( ) Platfora

( ) Predixion

( ) Prognoz

( ) Pyramid Analytics

( ) Qlik

( ) RapidMiner

( ) Roambi (MeLLmo)

( ) Salesforce.com

( ) Salient

( ) SAP/Business Objects

( ) SAS Institute

( ) SiSense

( ) Tableau

( ) Targit

( ) TIBCO (Spotfire, Jaspersoft)

( ) Thoughtspot

( ) Yellowfin

( ) Other - Write In: _________________________________________________




67

Please specify the product name and version for the selected vendor

How long has this product been in use?

( ) Less than 1 year

( ) 1 - 2 years

( ) 3 - 5 years

( ) 6 - 10 years

( ) More than 10 years

How many users currently use this product?

( ) 1-10

( ) 11-50

( ) 51-100

( ) 101-200

( ) 201-500

( ) More than 500

How would you characterize the sales/acquisition experience with this vendor?

Excellent

Very

good Adequate Poor

Very

poor

Don't

know

Professionalism ( ) ( ) ( ) ( ) ( ) ( )

Product knowledge ( ) ( ) ( ) ( ) ( ) ( )




68

Understanding our

business/needs

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

Responsiveness ( ) ( ) ( ) ( ) ( ) ( )

Flexibility/accommodation ( ) ( ) ( ) ( ) ( ) ( )

Business practices ( ) ( ) ( ) ( ) ( ) ( )

Contractual terms and

conditions

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

Follow up after the sale ( ) ( ) ( ) ( ) ( ) ( )

How would you characterize the value for the price paid?

( ) Great value (Well exceeded expectations)

( ) Good value (Somewhat exceeded expectations)

( ) Average value (Met expectations)

( ) Poor value (Fell short of expectations)

( ) Very poor value (Fell far short of expectations)

How would you characterize the quality and usefulness of the product?

Excellent

Very

good Adequate Poor

Very

poor

Don't

know

Robustness/sophistication

of technology

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

Completeness of

functionality

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

Reliability of technology ( ) ( ) ( ) ( ) ( ) ( )




69

Scalability ( ) ( ) ( ) ( ) ( ) ( )

Integration of

components within

product

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

Integration with 3rd party

technologies

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

Overall Usability ( ) ( ) ( ) ( ) ( ) ( )

Ease of installation ( ) ( ) ( ) ( ) ( ) ( )

Ease of administration ( ) ( ) ( ) ( ) ( ) ( )

Customization and

Extensibility

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

Ease of

upgrade/migration to new

versions

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

Online training, forums

and documentation

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

How would you characterize the vendor's technical support?

Excellent

Very

good Adequate Poor

Very

poor

Don't

know


Product

knowledge

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

Responsiveness ( ) ( ) ( ) ( ) ( ) ( )

Continuity of

personnel

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




70

Time to resolve

problems

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

How would you characterize the vendor's consulting services?

Excellent

Very

good Adequate Poor

Very

poor

Don't

know


Product

knowledge

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

Responsiveness ( ) ( ) ( ) ( ) ( ) ( )

Continuity of

personnel

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

Time to resolve

problems

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

How would you rate the "integrity" (i.e., truthfulness, honesty) of this BI vendor?

( ) Excellent

( ) Very good

( ) Adequate

( ) Poor

( ) Very poor

( ) Don't know




71

Did this vendor's overall performance improve, remains the same or decline from last

year?

( ) Improved

( ) Stayed the same

( ) Declined

Would you recommend this vendor/product?

( ) I would recommend this vendor/product

( ) I would NOT recommend this vendor/product

Please enter any additional comments regarding this vendor and/or its products

____________________________________________


Documents

Small and Mid-Sized Enterprise Business Intelligence ...marketo.tibco.com/rs/221-BCQ-142/images/2016-sme-bi-market-study... · September 15, 2016 Dresner Advisory Services, LLC 2016