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The BI Survey 7 - Table of contents

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1

Introduction

 

1.1

Executive summary

1

1.2

Vendor independence

3

1.3

Key findings

3

1.3.1

The market

3

1.3.2

The selection process

4

1.3.3

Achievement of business goals

6

1.3.4

Realizing business benefits

7

1.3.5

The power of the brand

8

1.3.6

Applications

9

1.3.7

Products

9

1.3.8

Purchases

11

1.3.9

Cost of ownership

12

1.3.10

Customer loyalty

12

1.3.11

Platforms

13

1.3.12

Data sources

14

1.3.13

Data volumes

14

1.3.14

Implementation and rollout

15

1.3.15

Deployment issues and problems

16

1.3.16

Performance issues

17

1.3.17

BI and the Web

18

1.4

Charting conventions

20

1.5

Means, medians, quartiles and modes

21

 

 

 

2

The sample

 

2.1

Objectives

24

2.1.1

Large sample

24

2.1.2

Well-distributed

25

2.1.3

Unbiased

25

2.2

Sample size and make-up

26

2.3

BI buyers compared with non-buyers

27

2.4

Respondents‚ perspectives

27

2.5

Geographic distribution

30

2.6

Organization sizes by revenue

34

2.7

Organization sizes by employees

35

2.8

Vertical markets

35

 

 

 

3

Products included

 

3.1

Product list

39

3.2

Augmented samples

41

3.3

Vendor notes

42

3.3.1

Applix

42

3.3.2

arcplan

43

3.3.3

Board

43

3.3.4

Business Objects

43

3.3.5

Cognos

44

3.3.6

Cubeware

44

3.3.7

Hyperion

44

3.3.8

Infor

45

3.3.9

Information Builders

46

3.3.10

Microsoft

46

3.3.11

MicroStrategy

47

3.3.12

MIK

47

3.3.13

Oracle

47

3.3.14

Pentaho

48

3.3.15

SAP

48

3.4

The architectural mix

51

 

 

 

4

Age profiles

 

4.1

Product age profiles

54

4.2

Changing product shares

56

 

 

 

5

The purchase cycle

 

5.1

What influences the evaluation list?

58

5.1.1

Influences by product evaluated

59

5.1.1

Influences by license fees, platforms and data volumes

61

5.1.2

Influences by organization demographics

62

5.2

Which industry analysts are influential, and where?

63

5.3

Does it pay to use industry analyst advice?

67

5.4

The benefits of conducting a formal evaluation

67

5.5

Why organizations choose products

71

5.6

... and how they should have chosen

79

5.7

License fees

80

5.7.1

License fees by product, vendor and architecture

83

5.7.2

License fees by evaluation method

84

5.7.3

License fees by implementation

86

5.7.4

License fees by organization demography

87

5.8

Do you get what you pay for?

89

 

 

 

6

The BI ownership experience

 

6.1

Proportion of employees regularly using BI

91

6.2

Departments using BI

99

6.3

Resources used to run and administer BI projects

100

6.4

Business goals achieved

101

6.4.1

Business goals achieved, analyzed by product and vendor

102

6.4.1

Business goals achieved, analyzed by lead implementer

105

6.4.2

Business goals achieved, analyzed by time since purchase

105

6.5

Business benefits enjoyed

108

6.6

The Business Benefits Index

110

6.6.1

Benefits trends

111

6.6.1

Benefits analyzed by product and vendor

112

6.6.2

Benefits analyzed by architecture, selection method, age and distribution

116

6.6.3

Benefits analyzed by Web deployment rate and license fees

118

6.6.4

Benefits analyzed by Web implementation factors

119

6.6.5

Benefits analyzed by customer demography

121

6.7

The Cost of Ownership Index

122

 

 

 

7

Vendor effectiveness

 

7.1

Vendor marketing effectiveness

123

7.1.1

Getting on the short list

123

7.1.2

Short listing trend

126

7.1.3

Avoiding competitive evaluations

128

7.2

Vendor self perception

129

7.3

Sales success: winners and losers

131

7.3.1

Win rates by evaluation type

133

7.3.1

Win rate trends

134

7.3.2

Win rates by organization size

137

7.3.3

Win rates by organization location

139

7.4

Buyer demographics

140

7.5

Licenses purchased

143

7.6

Deployed seats

144

7.7

Prevalence rates

146

7.8

Shelfware

149

7.9

Likelihood of using all purchased seats within a year

152

7.10

Future buying intentions

153

7.11

Product support

155

7.11.1

Product support methods

155

7.11.2

Overall product support ratings

159

7.11.3

Comparing vendor product support performance

160

7.11.4

Who provides better product support — large or small vendors?

164

7.11.5

Comparing support scores by respondent type

165

7.11.1

Do big customers get better product support?

166

7.12

Customer loyalty

167

7.12.1

Product abandonment

168

7.12.2

Which would they standardize on?

170

7.12.3

Reasons for standardization

172

7.12.4

The loyalty league table

174

7.12.5

Loyalty trends

176

 

 

 

8

Implementation

 

8.1

Implementers

178

8.2

External consulting spend

183

8.2.1

External fees by respondent type and most influential analyst firm

184

8.2.1

External fees by products, architecture and data volumes

185

8.2.2

External fees by license fees, implementation times and lead implementer

186

8.2.3

External fees by organization demography

188

8.3

Do you get what you pay for?

189

8.4

Which implementer is the most successful?

191

 

 

 

9

Timescales

 

9.1

By product and vendor

197

9.2

By architecture, platform, implementer and data volume

198

9.3

By organization demography

199

9.4

Installed within three or six months

200

9.5

Implementation times conclusions

201

 

 

 

10

What goes wrong?

 

10.1

Problems encountered

203

10.2

People problems

209

10.3

Data problems

213

10.4

Product-related technical problems

214

10.5

Normalized product-related problem analysis

220

10.6

The problem mix in perspective

223

10.7

Deterrents to wider deployment

227

10.7.1

Barriers analyzed by product

229

10.7.1

Barriers analyzed by architecture, fees and platform

231

10.7.2

Barriers analyzed by lead implementer and implementation time

233

10.7.3

No deterrents to wider deployment rankings

234

 

 

 

11

Applications

 

11.1

Applications by role and industry analyst

237

11.2

Applications by product and vendor

238

11.3

Applications by architecture, deal characteristics

240

11.4

Applications by platform, volumes and implementer

241

11.5

Applications by organization demographics

243

 

 

 

12

Web BI

 

12.1

Web deployment trends

245

12.2

Web deployment rates by respondent and organization

248

12.3

Web deployment rates by product and vendor

249

12.4

Web deployment rates by selection and implementation

251

12.5

Web deployment rates by application

253

12.6

Web deployment trends by product since 2002

254

12.7

Effects of Web deployment on business success

255

12.8

Extranet usage

257

12.8.1

Extranet deployment rates

257

12.8.1

Extranet deployment trends

258

12.8.2

Extranet deployment by product, vendor and architecture

260

12.8.3

Extranet deployment by platform, implementation and demographics

262

12.8.4

Extranet target users

263

12.9

Browsers used for BI deployments

267

12.10

Preferred BI Web architectures

271

 

 

 

13

Server platforms

 

13.1

Server platform trend

276

13.2

Server platforms by product, vendor and architecture

279

13.3

Server platforms by input data volumes

282

13.4

Server platforms by source, license fees and mode

282

13.5

Server platforms by organization factors

283

13.6

Does the server platform affect business success?

284

13.7

The rise of 64-bit BI

285

 

 

 

14

Client/server combos

 

14.1

Client tools used with 'open' OLAP servers

289

14.1.1

Analysis Services client tools

290

14.1.1

Essbase client tools

292

14.1.2

SAP BI/BW client tools

294

14.1.3

TM1 client tools

295

14.1.4

Comparing the server tools markets

296

14.2

BI data sources

296

14.2.1

Data sources accessed by arcplan

297

14.2.2

Data sources accessed by Business Objects client tools

298

14.2.3

Data sources accessed by Cognos client tools

299

14.2.4

Data sources accessed by Cubeware Cockpit

300

14.2.5

Data sources accessed by Information Builders WebFOCUS

300

14.2.6

Data sources accessed by Microsoft BI client tools

301

14.2.1

Comparing the number of data sources accessed by BI client tools

302

 

 

 

15

Source databases

 

15.1

Source databases

304

15.1.1

Source database trends

305

15.2

Data source mix by input data volumes

306

15.3

Data source mix by product and vendor

307

15.4

Data source mix by product type and platform

308

15.5

Data source mix by organization demography

309

15.6

Most popular BI tools used with major databases

310

15.6.1

The Microsoft database BI league tables

311

15.6.2

The Oracle database BI league tables

312

15.6.3

The IBM database BI league tables

313

15.6.4

The Sybase database BI league tables

314

15.6.5

The Teradata database BI league tables

315

15.6.6

The BI league table in sites performing manual data entry

316

 

 

 

16

Data volumes

 

16.1

Overall data volumes

318

16.2

Data volumes by product

320

16.3

Data volumes by platform

324

16.4

Data volumes by 32-bit vs 64-bit

324

16.5

Data volumes by architecture

325

16.6

Data volumes by lead implementer

326

16.7

Data volumes by industry sector

326

16.8

Data volumes by customer demographic

327

16.9

License fees by data volumes

328

16.10

Is bigger better?

329

 

 

 

17

Performance at the speed of thought?

 

17.1

Does query performance impact business benefits?

332

17.2

How do you measure performance?

335

17.3

Reported query times

337

17.4

Query times vs input data volume

343

17.5

Complaints about poor query performance

345

17.6

Query performance complaints trend

346

17.7

Poor performance deterring wider deployment

348

17.8

Is MOLAP always faster than ROLAP?

354

17.9

Is BI faster on UNIX or Windows?

356

17.10

Data latency: load, build and pre-calculate times

357

17.11

Does latency impact business benefits?

358

17.12

Data latency by product and vendor

359

17.13

Data latency vs input data volume

362

17.14

Data latency vs architecture

364

17.15

Performance questions answered

366

 

 

 

18

Appendix: Survey questionnaire