Full Contents
1 Introduction 1
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 6
1.3.5 The power of the brand 7
1.3.6 Applications 8
1.3.7 Products 8
1.3.8 Purchases 10
1.3.9 Customer loyalty 11
1.3.10 Platforms 11
1.3.11 Data sources 12
1.3.12 Implementation and rollout 13
1.3.13 Deployment issues and problems 13
1.3.14 Data volumes 14
1.3.15 Performance issues 14
1.3.16 OLAP and the Web 16
1.4 Charting conventions 17
1.5 Means, medians and modes 19
2 The sample 21
2.1 Objectives 21
2.1.1 Large sample 21
2.1.2 Well-distributed 22
2.1.3 Unbiased 23
2.2 Sample size 23
2.3 OLAP buyers compared with non-buyers 24
2.4 Respondents’ perspectives 26
2.5 Geographic distribution 29
2.6 Organization sizes by revenue 32
2.7 Organization sizes by employees 33
2.8 Vertical markets 33
3 Products included 39
3.1 Product list 39
3.2 Product notes 41
3.2.1 Microsoft Analysis Services 41
3.2.2 Applix TM1 41
3.2.3 Cognos Analysis 41
3.2.4 MicroStrategy 42
3.2.5 MIS 42
3.2.6 Essbase 42
3.2.7 SAP BW 43
3.2.8 BusinessObjects 43
3.2.9 OutlookSoft 43
3.2.10 Orenburg Board M.I.T. 44
3.2.11 MIK OLAP 44
3.2.12 Oracle OLAP products 44
3.2.13 Hyperion Intelligence 44
3.2.14 Financial applications 45
3.3 Architecture 45
3.4 Trends 47
3.4.1 Arrivals and Departures 49
3.5 Client tools used with OLAP servers 49
3.5.1 Analysis Services client tools 49
3.5.2 Essbase client tools 52
3.5.3 SAP BW client tools 54
3.6 Comparing the front-end tools markets 57
3.6.1 Excel as front-end 57
4 Age profiles 59
4.1 Product age profiles 59
4.2 Median product ages 61
4.3 Changing product shares 61
5 Customer success 64
5.1 Business goals achieved 64
5.2 Business benefits enjoyed 66
5.2.1 Faster or more accurate reporting 67
5.2.2 Better business decisions through more thorough or timely analysis
67
5.2.3 Improved customer satisfaction through enhanced product and service
quality 68
5.2.4 Saved headcount in business departments 68
5.2.5 Increased revenues through better sales and marketing analysis 69
5.2.6 Saved other non-IT costs (eg, inventory, waste, financing) 69
5.2.7 Reduced external IT costs (hardware, support, consulting, software
licensing) 70
5.2.8 Saved headcount in IS 70
5.2.9 Other benefits 71
5.2.10 The Business Benefits Index 72
5.2.11 Benefits summary 73
5.2.12 Benefits trends 75
6 The purchase cycle 77
6.1 What influences the evaluation list? 77
6.1.1 Influences by organization type 78
6.1.2 Influences by license spend 79
6.2 Which industry analysts are influential? 81
6.3 Does it pay to use industry analyst advice? 84
6.4 The benefits of conducting a formal evaluation 85
6.5 Why organizations choose products 87
6.5.1 Product vs corporate factors in product selection 92
6.6 … and how they should have chosen 93
6.7 Does product choice affect the business benefits? 95
6.7.1 Are new versions better? 100
6.7.2 Success rates among client tools 100
6.8 License fees 102
6.9 Do you get what you pay for? 107
7 Vendor scorecard 110
7.1 Vendor marketing effectiveness 110
7.1.1 Getting on the short list 110
7.1.2 Short-listing trend 113
7.1.3 Avoiding competitive evaluations 114
7.2 Sales success: winners and losers 117
7.2.1 Win rates by organization type 122
7.3 Buyer demographics 123
7.4 Seats purchased 125
7.5 Deployed seats 127
7.6 Prevalence rates 130
7.7 Shelfware 132
7.8 Future buying intentions 137
7.9 Product support 139
7.9.1 Product support methods 139
7.9.2 Overall product support ratings 142
7.9.3 Comparing vendor product support performance 142
7.9.4 Do big customers get better product support? 144
7.10 Customer loyalty 146
7.10.1 Product abandonment 147
7.10.2 Which would they standardize on? 150
7.10.3 Reasons for standardization 152
7.10.4 The loyalty top ten 154
8 Implementation 157
8.1 Implementers 157
8.2 External consulting spend 160
8.2.1 External consulting spend by primary implementation resource
160
8.2.2 External consulting spend by product 161
8.2.3 External consulting spend by input data volume 163
8.2.4 External consulting spend by platform 164
8.2.5 External consulting spend by organization size 164
8.2.6 External consulting spend by organization location 165
8.2.7 External consulting spend by product architecture 166
8.2.8 External consulting spend by selection method 167
8.2.9 External consulting spend by implementation time 168
8.3 Median implementation fees 169
8.4 Implementation fees compared to license fees 171
8.5 Do you get what you pay for? 174
8.6 Which implementer is the most successful? 175
8.7 Implementation resource conclusions 176
8.8 Resources used to run and administer OLAP projects 177
9 Timescales 179
9.1 By organization size 182
9.2 By business language 183
9.3 By implementation leader 184
9.4 By input data volumes 186
9.5 By platform 187
9.6 By product 188
9.7 By product architecture 190
9.8 Median implementation times summary 192
9.9 Installed within three or six months 194
9.10 Implementation times conclusions 195
10 What goes wrong? 196
10.1 Problems encountered 196
10.2 People problems 200
10.3 Data problems 204
10.4 Product-related technical problems 205
10.5 Normalized product-related problem analysis 211
10.6 The problem mix in perspective 215
10.7 Barriers to wider deployment 217
10.7.1 Barriers analyzed by product 220
10.7.2 Barriers analyzed by lead implementer 222
10.7.3 Barriers analyzed by license fees 223
10.7.4 No deterrents to wider deployment rankings 224
11 Applications 226
11.1 Applications by product 228
11.2 Applications by data volumes 229
11.3 Applications by maturity 230
11.4 Applications by organization size and location 230
11.5 Applications by selection method 231
12 OLAP and the Web 232
12.1 Web-deployment trends 232
12.2 Web-deployment rates by product 234
12.3 Web-deployment rates by region 236
12.4 Web-deployment rates by organization size 236
12.5 Web-deployment rates by lead implementer 237
12.6 Web deployment rates by application 238
12.7 Median Web-deployment rates 240
12.8 Effects of Web deployment on business success 242
12.9 Extranet usage 243
12.9.1 Extranet deployment rates 243
12.9.2 Extranet deployment trends 246
12.9.3 Current extranet deployment rate summary 248
12.9.4 Extranet target users 251
12.10 Browsers used for OLAP deployments 252
12.11 Preferred architectures 254
13 Server platforms 259
13.1 Server platform trend 259
13.2 Server platforms by region 262
13.3 Server platforms by organization size 264
13.4 Server platforms by data volume 265
13.5 Server platforms by product 267
13.6 Does the server platform affect business success? 269
13.7 The rise of 64-bit OLAP 270
13.8 Server platform conclusions 273
14 Source databases 274
14.1 Source databases 274
14.1.1 Source database trends 275
14.2 Data source mix by input data volumes 276
14.3 Data source mix by product 277
14.4 Most popular OLAP tools for major databases 279
14.4.1 The Microsoft Top Ten 279
14.4.2 The Oracle Top Ten 280
14.4.3 The IBM Top Ten 281
14.4.4 The Teradata Top Ten 281
14.4.5 The Top Ten OLAP databases for manual data entry 282
15 Data volumes 283
15.1 Overall data volumes 284
15.2 Data volumes by product 284
15.3 Data volumes by platform 289
15.4 Data volumes by architecture 289
15.5 Data volumes by organization type 290
15.6 Median data volumes 291
15.7 License fees by data volumes 295
15.8 Is bigger better? 296
16 Performance at the speed of thought? 299
16.1 Does query performance impact business benefits? 300
16.2 How do you measure performance? 302
16.3 Reported query times 305
16.4 Query times vs input data volume 311
16.5 Query times by data volume bands 312
16.6 Complaints about poor query performance 315
16.7 Query performance complaints trend 316
16.8 Poor performance deterring wider deployment 317
16.9 Is MOLAP always faster than ROLAP? 320
16.10 Is OLAP faster on UNIX or Windows? 322
16.11 Data latency: load, build and pre-calculate times 325
16.12 Does latency impact business benefits? 326
16.13 Build/load time profiles by product 328
16.14 Data latency vs input data volume 330
16.15 Reported build times by data band 331
16.16 MOLAP vs ROLAP build/load times 334
16.17 UNIX vs Windows build/load times 337
16.18 Performance questions answered 340
17 Appendix: Survey questionnaire 343
Tables
Table 1 – Recommended Survey invitation wording 22
Table 2 – Respondents’ decision-making roles 27
Table 3 – Location of respondents’ parent organizations 31
Table 4 – Organization total revenues 33
Table 5 – Organization size by employees 33
Table 6 – Vertical markets by product 35
Table 7 – Vertical markets by data volumes 36
Table 8 – Products included in the sample 40
Table 9 – Client tools and applications used with Analysis Services
51
Table 10 – Third-party Excel add-ins used with Analysis Services
52
Table 11 – Combinations of Microsoft and third-party Excel add-ins
used with Analysis Services 52
Table 12 – Client tools and applications used with Essbase 54
Table 13 – Client tools and applications used with SAP BW 55
Table 14 – Usage levels of SAP BW “Business Content”
55
Table 15 – Overall achievement of business goals 65
Table 16 – Weighting factors used to score benefit achievement levels
67
Table 17 – Achievement of faster or more accurate reporting 67
Table 18 – Achievement of better business decisions through more
thorough or timely analysis 68
Table 19 – Achievement of improved customer satisfaction through
enhanced product quality and/or service levels 68
Table 20 – Achievement of headcount savings in business departments
69
Table 21 – Achievement of increased revenues through better sales
and marketing analysis 69
Table 22 – Achievement of savings of other non-IT costs (eg, inventory,
waste, financing) 70
Table 23 – Achievement of reduced external IT costs (hardware, support,
consulting or software licensing) 70
Table 24 – Achievement of headcount savings in IS 71
Table 25 – Achievement of other benefits 72
Table 26 – Overall BBI calculation 72
Table 27 – Benefits summary 75
Table 28 – How did you compile the list of products to evaluate?
77
Table 29 – Demographic effect on influences 79
Table 30 – Relative influence of industry analysts 81
Table 31 – Product share changes linked to analyst influence 84
Table 32 – Reasons given for choosing OLAP products 89
Table 33 – Reasons given for choosing particular OLAP products 91
Table 34 – Selection reasons by other factors 92
Table 35 – Benefits-driven ranking of selection criteria 94
Table 36 – Detailed benefit and goal scores by product 97
Table 37 – Chances of products being evaluated 112
Table 38 – Win rates by organization demographics 123
Table 39 – Demographics of product buyers 124
Table 40 – Shelfware rates 136
Table 41 – Future buying intentions 139
Table 42 – Overall ratings of product support 142
Table 43 – Users’ ratings of major products’ support
144
Table 44 – Reasons cited for discontinuing use of products 150
Table 45 – The possible effects of standardization trends 151
Table 46 – Reasons cited for potentially standardizing on products
153
Table 47 – The product loyalty league table 156
Table 48 – All the implementation resources used 157
Table 49 – The primary implementation resource 158
Table 50 – Implementation spend 160
Table 51 – Ratio of external consulting spend to license fees 174
Table 52 – Net people involved in running and administering projects
178
Table 53 – Most serious deployment problems 197
Table 54 – Analysis of technical problems part I 209
Table 55 – Analysis of technical problems part II 211
Table 56 – Deterrents to wider deployment, by product 221
Table 57 – Deterrents to wider deployment, by product version 222
Table 58 – Deterrents to wider deployment, by lead implementer 223
Table 59 – Deterrents to wider deployment, by license fees paid
224
Table 60 – Primary purpose of applications 227
Table 61 – Average number of applications by category and product
229
Table 62 – Number of applications by category and data volumes 229
Table 63 – OLAP applications by maturity 230
Table 64 – OLAP applications by organization size and location 231
Table 65 – OLAP applications by selection method 231
Table 66 – Percentage of Web-deployed seats from 2001 to 2006 232
Table 67 – Extranet targets 251
Table 68 – Extranet targets broken down 252
Table 69 – Preferred Web architectures by subgroup 258
Table 70 – Platforms by input data volume band 267
Table 71 – Business benefits and goals analysis by platform 270
Table 72 – Data sources by input data volume bands 277
Table 73 – The top ten OLAP tools for Microsoft databases 280
Table 74 – The top ten OLAP tools for Oracle databases 281
Table 75 – The top ten OLAP tools for IBM databases 281
Table 76 – The top ten OLAP tools for Teradata databases 282
Table 77 – The top ten OLAP products for manual data input 282
Table 78 – Reported input data volumes 284
Figures
Figure 1 – Sample chart, illustrating the charting conventions
18
Figure 2 – Illustrating means, medians and modes 20
Figure 3 – OLAP purchases, by total organization revenue 25
Figure 4 – OLAP purchases, by total employees in the organization
26
Figure 5 – Distribution of respondents’ roles 28
Figure 6 – Geographic makeup of the six editions of The OLAP Survey
30
Figure 7 – Geographic scope of respondents’ organizations
32
Figure 8 – OLAP usage rates by vertical market 38
Figure 9 – Architectural mix of products 46
Figure 10 – The primary product mix in each edition of The OLAP
Survey 48
Figure 11 – SAP ERP data proportion in BW 56
Figure 12 – Client tool markets 57
Figure 13 – Excel add-in penetration 58
Figure 14 – Age profiles by product 60
Figure 15 – Median times in months since software purchase 61
Figure 16 – Comparing product mixes in mature and recent sites 63
Figure 17 – Goal achievement rates 66
Figure 18 – Trends in reported benefits 76
Figure 19 – Differing influences by deal size 80
Figure 20 – Analyst influence trends in North America 82
Figure 21 – BBI by analyst influence 85
Figure 22 – Goals and benefits achieved vs selection method 86
Figure 23 – Product compared to corporate factors in product selection
93
Figure 24 –Business Benefits Index (BBI) by product 95
Figure 25 –Comparing the 2003 to 2006 BBI scores by product 98
Figure 26 – Goal and business benefits achievements scores vs site
maturity 99
Figure 27 –Business Benefits Index by major product versions 100
Figure 28 – BBI scores for different client tool groups 102
Figure 29 – License fee distribution 104
Figure 30 – Median license fees paid 105
Figure 31 – License fee distribution 107
Figure 32 – Goals and benefits vs license fees 109
Figure 33 – Evaluation frequency trend 114
Figure 34 – Likelihood of a formal evaluation 116
Figure 35 – Selection rates in evaluations 119
Figure 36 – Win rate trends in purchases within the last three years
121
Figure 37 – Median named user seats, excluding server licenses 125
Figure 38 – Average licensed named users per site, excluding server
licenses 126
Figure 39 – Percentage of sites with at least 1000 named users 127
Figure 40 – Median number of seats deployed per site 128
Figure 41 – Percentage of sites with over 1000 deployed seats 129
Figure 42 – Mean number of seats deployed per site 130
Figure 43 – Prevalence rates in organizations 132
Figure 44 – Inclination to buy more seats 138
Figure 45 – Primary support method trend 140
Figure 46 – Analysis of primary support methods 141
Figure 47 – Support ratings by method 142
Figure 48 – Support quality ratings by product 143
Figure 49 – Product support rating vs license fees paid 145
Figure 50 – Reported rates of discontinued usage 148
Figure 51 – Preferred products to retain when standardizing 152
Figure 52 – Lead Implementer mix 159
Figure 53 – External consulting spend by lead implementation resource
161
Figure 54 – External consulting spend by product 162
Figure 55 – External consulting spend by input data volumes 163
Figure 56 – External consulting spend by platform 164
Figure 57 – External consulting spend by organization size 165
Figure 58 – External consulting spend by organization location 166
Figure 59 – External consulting spend by product architecture 167
Figure 60 – External consulting spend by selection method 168
Figure 61 – External consulting spend by implementation time 169
Figure 62 – Median consulting fees summary 170
Figure 63 – Banded ratio of consulting spend to license fees 172
Figure 64 – Benefits vs external consulting spend 175
Figure 65 – Benefits vs primary implementation resource 176
Figure 66 – Project success rates by implementation time 180
Figure 67 – Problem rates by implementation time 181
Figure 68 – Implementation times by organization size 183
Figure 69 – Implementation times by organization language 184
Figure 70 – Implementation times by project leader 185
Figure 71 – Implementation times by input data volumes 187
Figure 72 – Implementation times by platform 188
Figure 73 – Implementation times by product 190
Figure 74 – Implementation times by product architecture 192
Figure 75 – Median implementation times in months 193
Figure 76 – Percentage of rollouts within three and six months 194
Figure 77 – Problem trends over time 198
Figure 78 – Problem trends since 2002 199
Figure 79 – Most-serious problem trends in The OLAP Surveys 200
Figure 80 – People problems by rollout times 202
Figure 81 – People problems by organization characteristics 203
Figure 82 – Reported incidence of data problems by product 205
Figure 83 – Average serious technical problems reported per site
207
Figure 84 – Environmental problems 212
Figure 85 – Ratio of technical to environmental problems 214
Figure 86 – Comparing the problem mix by product 215
Figure 87 – Comparing the problem mix by organization type 216
Figure 88 – Comparing the problem mix by lead implementer 217
Figure 89 – Deterrents to wider deployment, if any 219
Figure 90 – No deterrents to wider deployment 225
Figure 91 – Applications trend 228
Figure 92 – Web-deployment trend vs forecasts 234
Figure 93 – Web-deployment rates by product 235
Figure 94 – Reported Web-deployment rates by major countries 236
Figure 95 – Reported Web-deployment rates by organization size 237
Figure 96 – Reported Web-deployment rates by lead implementer 238
Figure 97 – Median Web deployment rates by application 239
Figure 98 – Median Web deployment rates across multiple dimensions
241
Figure 99 – Reported success rates by Web deployment rates 242
Figure 100 – Extranet deployment by product 244
Figure 101 – Extranet deployment by country 245
Figure 102 – Extranet deployment by server platform 246
Figure 103 – Extranet rate trends: perception vs reality 248
Figure 104 – Current extranet deployment rates summary 250
Figure 105 – OLAP browser shares 254
Figure 106 – Preferred Web browser architectures 256
Figure 107 – Server platform shares 260
Figure 108 – Overall server platforms trend 261
Figure 109 – Detailed platform trends, 2001-2006 262
Figure 110 – Detailed platform trends by geographic region 263
Figure 111 – Detailed platform trends by organization size 265
Figure 112 – Server platforms vs input data volumes 266
Figure 113 – Server platforms by product 269
Figure 114 – 64-bit server penetration 272
Figure 115 – Source databases for OLAP applications 275
Figure 116 – Database sources for OLAP data 276
Figure 117 – Database sources by OLAP product 278
Figure 118 – Reported input data volumes by product 286
Figure 119 – Product shares vs input data volumes 288
Figure 120 – Reported input data volumes by platform 289
Figure 121 – Reported input data volumes by architecture 290
Figure 122 – Reported input data volumes by organization characteristic
291
Figure 123 – Median reported input data volumes (in GB) 293
Figure 124 – Median reported input data volumes in GB – log
scale 294
Figure 125 – License fees by data volume 295
Figure 126 – License and implementation fees by data volume 296
Figure 127 – Goals and benefits vs input data volumes 297
Figure 128 – Performance problems compared to other product-related
problems 299
Figure 129 – Goals and business benefits vs query performance 301
Figure 130 – Total reported problems vs query performance 302
Figure 131 – Comparing OLAP query times from 2002 to 2006 307
Figure 132 – Reported OLAP query times 308
Figure 133 – Median query times 309
Figure 134 – Median query time trend 310
Figure 135 – Median query times vs median input data volumes 311
Figure 136 – Reported mean typical query times by size band 313
Figure 137 – Reported median typical query times by size band 314
Figure 138 – Performance complaints 315
Figure 139 – Query times vs volumes trend 316
Figure 140 – Query times vs complaints 317
Figure 141 – Poor query performance as a deterrent to wider deployment
319
Figure 142 – Network bandwidth as a deterrent to wider deployment
320
Figure 143 – Reported mean typical query times by architecture 321
Figure 144 – Reported median typical query times by architecture
322
Figure 145 – Reported mean typical query times by platform 324
Figure 146 – Reported median typical query times by platform 325
Figure 147 – Goals and business benefits vs load/calculate times
327
Figure 148 – Load/pre-calculate times by product 329
Figure 149 – Median load/calculate times 330
Figure 150 – Median load/pre-calc times vs median input data volumes
331
Figure 151 – Reported mean typical load/calculate times by size
band 332
Figure 152 – Reported median typical load/calculate times by size
band 334
Figure 153 – Reported mean typical load/calculate times by architecture
335
Figure 154 – Reported median typical load/calculate times by architecture
336
Figure 155 – Reported mean typical load/calculate times by platform
338
Figure 156 – Architecture split by platform 339
Figure 157 – Reported median typical load/calculate times by platform
340
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