The BI Survey 8 - Table of contents

1

Introduction

1

1.1

Executive summary

1

1.2

Vendor independence

3

1.3

Key findings

4

1.3.1

The market

4

1.3.2

The selection process

5

1.3.3

Achievement of business goals

6

1.3.4

Realizing business benefits

7

1.3.5

The power of the mega vendors

8

1.3.6

Applications

9

1.3.7

Products

10

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

15

1.3.14

Implementation and rollout

15

1.3.15

Deployment issues and problems

16

1.3.16

Performance issues

17

1.3.17

Web BI

18

1.3.18

Vendor vs customer and consultant perceptions

19

1.4

Charting conventions

21

1.5

Means, medians, quartiles and modes

22

2

The sample

25

2.1

Objectives

25

2.1.1

Large sample

25

2.1.2

Well-distributed

26

2.1.3

Unbiased

26

2.2

Sample size and make-up

27

2.3

BI buyers compared with non-buyers

29

2.4

Respondents' perspectives

30

2.5

Geographic distribution

34

2.6

Organization sizes by revenue

37

2.7

Organization sizes by headcount

38

2.8

Customer organization sizes

39

2.9

Vertical markets

41

3

Products included

44

3.1

Product list

44

3.2

Augmented samples

46

3.3

Vendor notes

47

3.3.1

Actuate

48

3.3.2

arcplan

48

3.3.3

Board

48

3.3.4

Business Objects (SAP)

49

3.3.5

Cognos (IBM)

49

3.3.6

Cubeware

50

3.3.7

Hyperion (Oracle)

50

3.3.8

Infor

51

3.3.9

Information Builders

51

3.3.10

LogiXML

51

3.3.11

Microsoft

51

3.3.12

MicroStrategy

52

3.3.13

MIK

52

3.3.14

Oracle

52

3.3.15

Open source

53

3.3.16

QlikTech

53

3.3.17

SAP

53

3.3.18

Targit

56

4

Age profiles

57

4.1

Product age profiles

58

4.2

Changing product shares

62

5

The Business Benefits Index

63

5.1

Business benefits enjoyed

63

5.2

The Business Benefits Index

66

5.3

Business benefits trends

66

6

The purchase cycle

68

6.1

What influences the evaluation list?

68

6.1.1

Influences by role

69

6.1.2

Influences by product evaluated

70

6.1.3

Influences by application characteristics

72

6.1.4

Influences by organization demographics

74

6.2

The benefits of conducting a formal evaluation

75

6.3

Why organizations choose products

79

6.3.1

Reasons for product selection, by product ...

83

6.4

... and how they should have chosen

87

6.5

Vendor vs customer perceptions

91

6.6

License fees

91

6.6.1

License fees by respondents' roles

94

6.6.2

License fees by product

94

6.6.3

License fees by evaluation method

95

6.6.4

License fees by breadth of deployment and platform

96

6.6.5

License fees by deterrents to wider deployment

97

6.6.6

License fees by implementation characteristics

98

6.6.7

License fees by data characteristics

99

6.6.8

License fees by organization demography

100

6.7

Do you get what you pay for?

101

7

The BI ownership experience

104

7.1

Proportion of employees regularly using BI

104

7.1.1

Breadth of deployment analyzed by respondent role

105

7.1.2

The vendor vs the user perception of breadth of deployment

106

7.1.3

Breadth of deployment analyzed by product

107

7.1.4

Breadth of deployment analyzed by age and selection methods

109

7.1.5

Breadth of deployment analyzed by license fees and platform

110

7.1.6

Breadth of deployment analyzed by problem areas

111

7.1.7

Breadth of deployment analyzed by implementation factors

112

7.1.8

Breadth of deployment analyzed by applications and user departments

113

7.1.9

Breadth of deployment analyzed by customer size and vertical market

116

7.1.10

Breadth of deployment analyzed by parent organization geography

117

7.2

Departments using BI

118

7.2.1

Departments using BI, analyzed by respondents' roles

119

7.2.2

Departments using BI, analyzed by product

119

7.2.3

Departments using BI, analyzed by customer demographics

121

7.3

Resources used to run and administer BI projects

123

7.4

Business goals achieved

126

7.4.1

Goal achievement levels, analyzed by respondents' roles

127

7.4.2

Business goals achieved, analyzed by product and vendor

128

7.4.3

Business goals achieved, analyzed by age and evaluation methods

131

7.4.4

Business goals achieved, analyzed by implementation factors

133

7.4.5

Business goals achieved, analyzed by organization demographics

134

7.5

Analyzing product goal achievement over time

136

7.6

Business benefits achieved

137

7.6.1

Benefits analyzed by respondents' roles

137

7.6.2

Benefits analyzed by product and vendor

138

7.6.3

Benefits analyzed by application age

140

7.6.4

Benefits analyzed by evaluation methods

141

7.6.5

Benefits analyzed by license fees, breadth of deployment, platform

142

7.6.6

Benefits analyzed by Web deployment rates

143

7.6.7

Benefits analyzed by implementers

144

7.6.8

Benefits analyzed by implementation time

145

7.6.9

Benefits analyzed by support quality

146

7.6.10

Benefits analyzed by customer demography

146

7.7

The Cost of Ownership Index

149

8

Vendor effectiveness

151

8.1

Vendor marketing effectiveness

151

8.1.1

Getting on the evaluation list

151

8.1.2

Short-listing trend

156

8.1.3

Avoiding competitive evaluations

158

8.2

Vendor self-perception

160

8.3

Sales success: winners and losers

164

8.3.1

Win rates by evaluation type

166

8.3.2

Win rate trends

167

8.3.3

Win rates by organization size

169

8.3.4

Win rates by organization location

171

8.4

Buyer demographics

173

8.5

Licenses purchased

175

8.6

Deployed seats

178

8.7

Prevalence rates

180

8.8

Shelfware

182

8.9

Likelihood of using all purchased seats within a year

186

8.10

Future buying intentions

187

8.11

Product support

191

8.11.1

Product support methods

191

8.11.2

Overall support scores and trend

198

8.11.3

Support ratings by resource and role

199

8.11.4

Comparing vendor product support performance

201

8.11.5

Is big beautiful where product support is concerned?

205

8.11.6

Comparing support scores by site age and selection method

206

8.11.7

Do big customers get better product support?

207

8.11.8

Regional analysis of product support quality

209

8.12

Customer loyalty

210

8.12.1

Product abandonment

211

8.12.2

Which would they standardize on?

213

8.12.3

Reasons for standardization

216

8.12.4

The loyalty league table

219

8.12.5

Loyalty trends

222

9

Implementation

224

9.1

Implementers

224

9.1.1

Implementation mix by respondents' roles

225

9.1.2

Implementation mix by product

226

9.1.3

Implementation mix by application characteristics

227

9.1.4

Implementation mix by organization demographics

228

9.2

External consulting spend

229

9.2.1

External fees by respondents' roles

231

9.2.2

External fees by product and suite

232

9.2.3

External consulting fees by application characteristics

233

9.2.4

External fees by organization demography

236

9.3

Do you get what you pay for?

237

9.4

Which implementer is the most successful?

238

10

Timescales

240

10.1

Effect on success rates as rollout times grow

241

10.2

Reported roll-out times by respondent role

244

10.3

Reported rollout times by product and suite

244

10.4

Reported rollout times by implementation factors

245

10.5

Reported rollout times by organization demographics

247

10.6

Rolled out within three or six months

248

10.7

Implementation times conclusions

249

11

What goes wrong?

251

11.1

Problems encountered

251

11.2

People problems

258

11.3

Data problems

262

11.4

Product-related technical problems

265

11.4.1

Product-related problems analyzed by respondent role

265

11.4.2

Product-related problems analyzed by product

266

11.4.3

Product-related problems analyzed by mode and age

269

11.4.4

Product-related problems analyzed by product selection criteria used

270

11.4.5

Product-related problems analyzed by deal size and platform

272

11.4.6

Product-related problems analyzed by implementation factors

273

11.4.7

Product-related problems analyzed by query times and data volumes

276

11.5

Normalized product-related problem analysis

277

11.6

The problem mix in perspective

281

11.7

Deterrents to wider deployment

288

11.7.1

Deterrents analyzed by product

290

11.7.2

Barriers analyzed by selection methods

292

11.7.3

Barriers analyzed by lead implementer and implementation time

294

11.7.4

No deterrents to wider deployment rankings

295

12

Applications

297

12.1

Applications by role and industry analyst

298

12.2

Applications by product

298

12.3

Applications by selection methods

301

12.4

Applications by license fees, breadth and platform

302

12.5

Applications by implementation factors

303

12.6

Applications by data volumes

306

12.7

Applications by organization demographics

306

13

Web BI

308

13.1

Web deployment trends

308

13.2

Web deployment rates by respondent and organization

311

13.3

Web deployment rates by product and suite

312

13.4

Web deployment rates by selection and implementation factors

314

13.5

Web deployment rates by query time and data volume

315

13.6

Web deployment trends by product since 2002

316

13.7

Effects of Web deployment on business success

317

13.8

Extranet usage

319

13.8.1

Extranet deployment rates

319

13.8.2

Extranet deployment trends

320

13.8.3

Extranet deployment by respondent and organization type

322

13.8.4

Extranet deployment by product

323

13.8.5

Extranet deployment by deployment aspects

325

13.8.6

Extranet target users

326

13.9

Browsers used for BI deployments

329

13.10

Preferred BI Web architectures

333

14

Server platforms

340

14.1

Server platform trend

340

14.2

Server platforms by respondent and organization

344

14.3

Server platforms by product and vendor

346

14.4

Server platforms by purchase factors

348

14.5

Server platforms by implementation factor

349

14.6

Does the server platform affect business success?

350

14.7

The rise of 64-bit BI

351

15

Client/server combos

354

15.1

Client tools used with 'open' OLAP servers

354

15.1.1

Analysis Services client tools

355

15.1.2

Essbase client tools

357

15.1.3

SAP BI/BW client tools

358

15.1.4

TM1 client tools

360

15.1.5

Comparing the server tools markets

361

15.2

BI data sources

361

15.2.1

Data sources accessed by Actuate

362

15.2.2

Data sources accessed by arcplan

363

15.2.3

Data sources accessed by Bissantz client tools

363

15.2.4

Data sources accessed by Business Objects (SAP) client tools

364

15.2.5

Data sources accessed by Cognos client tools

365

15.2.6

Data sources accessed by Cubeware Cockpit

366

15.2.7

Data sources accessed by Information Builders WebFOCUS

366

15.2.8

Data sources accessed by Microsoft BI client tools

367

15.2.9

Data sources accessed by Panorama

368

15.2.10

Comparing the number of data sources accessed by BI client tools

368

16

Source databases

370

16.1

Source databases

370

16.1.1

Source database trends

371

16.2

Data source mix by input data volumes

372

16.3

Data source mix by product and vendor

375

16.4

Data source mix analyzed by other factors

377

16.5

Most popular BI tools used with major databases

379

16.5.1

The Microsoft database BI league tables

379

16.5.2

The Oracle database BI league tables

380

16.5.3

The IBM database BI league tables

381

16.5.4

The open source database BI league tables

382

16.5.5

The Teradata BI league tables

383

16.5.6

The flat files BI league tables

384

16.5.7

The BI league table in sites performing manual data entry

385

17

Data volumes

387

17.1

Overall data volumes

388

17.1.1

Data volume trends

389

17.2

Data volumes analyzed

390

17.2.1

By respondents' roles

391

17.2.2

By product and suite

391

17.2.3

By deployment factors

394

17.2.4

By platform

395

17.2.5

By implementation factors

396

17.2.6

By industry

397

17.2.7

By customer demographics

398

17.3

Is bigger better?

399

18

Performance at the speed of thought?

401

18.1

Does query performance impact business benefits?

402

18.2

How do you measure performance?

405

18.3

Reported query times

407

18.3.1

Query times by respondent type and input data volumes

408

18.3.2

Query times by product and selection method

409

18.3.3

Query times by platform and extent of Web deployment

412

18.4

Query times vs input data volume

413

18.5

Complaints about poor query performance

415

18.6

Query performance complaints trend

417

18.7

Poor performance deterring wider deployment

420

18.8

Data latency: load, build and pre-calculate times

422

18.9

Does latency affect business benefits?

422

18.10

Data latency analyzed

423

18.11

Data latency vs input data volume

428

19

The customers' verdict dashboards

431

19.1

Root KPIs

432

19.1.1

Business Benefits Index

433

19.1.2

Goal achievement, adjusted for age

434

19.1.3

Competitive win rate

435

19.1.4

Selection based on product factors

436

19.1.5

Prevalence rates in multi-product sites

437

19.1.6

Standardization preferences

438

19.1.7

Intention to buy more licenses

439

19.1.8

Discontinued usage rates

440

19.1.9

Proportion of employees using product

441

19.1.10

Range of applications deployed

442

19.1.11

Number of departments served

443

19.1.12

Deployed seats

444

19.1.13

Data volumes (log)

445

19.1.14

Cost of Ownership Index

446

19.1.15

Deployed seats per administrator head

447

19.1.16

Implemented within three months

448

19.1.17

Product-related problems

449

19.1.18

Product-related deterrents to wider deployment

450

19.1.19

Product reliability

451

19.1.20

Product support quality

452

19.1.21

Query performance complaints

453

19.1.22

Query performance, adjusted for data volumes

454

19.1.23

Data latency, adjusted for data volumes

455

19.1.24

Data latency as a deterrent to wider deployment

456

19.1.25

Web deployment (>50 percent Web)

457

19.1.26

Extranets deployed

458

19.2

Aggregated KPIs

458

19.2.1

Business achievement KPIs

459

19.2.2

Costs KPIs

460

19.2.3

Scalability KPIs

461

19.2.4

Quality and support KPIs

462

19.2.5

Performance KPIs

463

19.2.6

Loyalty KPIs

464

19.2.7

Web KPIs

465

19.3

Overall KPI score

466

19.4

Product dashboards

466

19.4.1

Actuate Platform KPI dashboard

467

19.4.2

arcplan KPI dashboard

468

19.4.3

Bissantz KPI dashboard

469

19.4.4

Board KPI dashboard

470

19.4.5

BusinessObjects KPI dashboard

471

19.4.6

Cognos Analysis KPI dashboard

472

19.4.7

Cognos Reporting KPI dashboard

473

19.4.8

Cognos TM1 Server KPI dashboard

474

19.4.9

Crystal Reports KPI dashboard

475

19.4.10

Cubeware Cockpit KPI dashboard

476

19.4.11

Hyperion Essbase KPI dashboard

477

19.4.12

Infor PM OLAP KPI dashboard

478

19.4.13

Microsoft Analysis Services KPI dashboard

479

19.4.14

Microsoft Excel PivotTables KPI dashboard

480

19.4.15

Microsoft Reporting Services KPI dashboard

481

19.4.16

MicroStrategy KPI dashboard

482

19.4.17

MIK KPI dashboard

483

19.4.18

Oracle BIEE/BISEO KPI dashboard

484

19.4.19

Panorama KPI dashboard

485

19.4.20

QlikTech QlikView KPI dashboard

486

19.4.21

SAP BI/BW KPI dashboard

487

19.4.22

Targit KPI dashboard

488

19.4.23

WebFOCUS KPI dashboard

489

20

Appendix: Survey questionnaire

490