The BI Survey 8 - Charts and tables
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Figure 1 | Charting example | 22 |
Figure 2 | Illustrating means and quartiles | 24 |
Figure 3 | Recommended Survey invitation wording | 26 |
Figure 4 | Sample make-up | 28 |
Figure 5 | Sample size trend of The BI/OLAP Surveys | 29 |
Figure 6 | Purchase rates | 30 |
Figure 7 | Respondents' roles overall | 31 |
Figure 8 | Respondents' roles by product and vendor | 32 |
Figure 9 | Respondents' roles by volumes, fees, organization | 33 |
Figure 10 | Respondents' roles by geography | 34 |
Figure 11 | Geographic trend of The BI/OLAP Surveys | 35 |
Figure 12 | Location of respondents' parent organizations | 36 |
Figure 13 | Geographic scope of respondents' organizations | 37 |
Figure 14 | Organization total revenues | 38 |
Figure 15 | Organization size by employees | 38 |
Figure 16 | Median customer revenue by product/vendor | 40 |
Figure 17 | Median customer headcount by product/vendor | 41 |
Figure 18 | Industry sector analysis | 42 |
Figure 19 | Industry sector analysis by product | 43 |
Figure 20 | Products included in the Survey | 46 |
Figure 21 | Augmented samples | 47 |
Figure 22 | Usage levels of SAP BI/BW 'Business Content' | 54 |
Figure 23 | Percentage of data originating from SAP ERP | 55 |
Figure 24 | SAP BI Accelerator usage and plans | 56 |
Figure 25 | Time since purchase | 58 |
Figure 26 | Age profiles in months by user type and product | 60 |
Figure 27 | Age profiles in months by demography | 61 |
Figure 28 | Comparing product shares in mature and recent sites | 62 |
Figure 29 | Benefit weightings | 64 |
Figure 30 | Weighted benefit achievement levels | 64 |
Figure 31 | Overall BBI calculation | 66 |
Figure 32 | Trends in reported benefits | 67 |
Figure 33 | Evaluation list influencer trend | 69 |
Figure 34 | Influences by role | 70 |
Figure 35 | Influences by products evaluated | 71 |
Figure 36 | Influences by suites and vendors | 72 |
Figure 37 | Influences by application characteristics | 73 |
Figure 38 | Influences by organization demographics | 75 |
Figure 39 | Goals and benefits achieved vs selection method | 77 |
Figure 40 | Evaluation trends | 79 |
Figure 41 | Reasons given for choosing BI products | 81 |
Figure 42 | Selection criteria trends | 82 |
Figure 43 | Reasons given for choosing all purchased BI products | 84 |
Figure 44 | Reasons given for choosing nominated BI products | 86 |
Figure 45 | Aggregated reasons for choosing different products | 87 |
Figure 46 | Benefits-driven ranking of selection criteria | 89 |
Figure 47 | Suggested changes in selection criteria rankings | 90 |
Figure 48 | Vendor vs customer perceptions of selection criteria | 91 |
Figure 49 | License fee distribution | 93 |
Figure 50 | License fees by respondent roles | 94 |
Figure 51 | License fees by product | 95 |
Figure 52 | License fees by evaluation method | 96 |
Figure 53 | License fees by breadth of deployment and platform | 97 |
Figure 54 | License fees by deterrents to wider deployment | 98 |
Figure 55 | License fees by implementation characteristics | 99 |
Figure 56 | License fees by data characteristics | 100 |
Figure 57 | License fees by customer demography | 101 |
Figure 58 | Goals and BBI vs license fees | 103 |
Figure 59 | Proportion of employees using BI applications | 105 |
Figure 60 | Proportion of users by respondent type | 106 |
Figure 61 | Vendor vs user perceptions of breadth of BI deployment | 107 |
Figure 62 | Proportion of users by product | 108 |
Figure 63 | Proportion of users by age and selection methods | 110 |
Figure 64 | Proportion of users by license fees and platform | 111 |
Figure 65 | Proportion of users by problems encountered | 112 |
Figure 66 | Proportion of users by implementation factors | 113 |
Figure 67 | Proportion of users by applications and user departments | 115 |
Figure 68 | Proportion of users by organization size and vertical market | 117 |
Figure 69 | Proportion of users by parent organization geography | 118 |
Figure 70 | Functions using BI applications | 119 |
Figure 71 | Departments using BI, by respondents' roles | 119 |
Figure 72 | Departments using BI, by respondents' roles, by product | 121 |
Figure 73 | Proportion of users by parent organization demography | 123 |
Figure 74 | Net people involved in running and administering projects | 124 |
Figure 75 | Deployed users per admin head | 125 |
Figure 76 | Deployed applications per admin head | 126 |
Figure 77 | Overall achievement of business goals | 127 |
Figure 78 | Business goal levels, by respondent roles | 128 |
Figure 79 | Achievement of business goals, analyzed by product | 130 |
Figure 80 | Achievement of goals, analyzed by age and evaluation methods | 131 |
Figure 81 | Goal achievement scores vs age | 132 |
Figure 82 | Achievement of business goals, analyzed by implementation factors | 134 |
Figure 83 | Achievement of business goals, analyzed by organization demographics | 135 |
Figure 84 | Achievement of goal achievement by product, adjusted for age | 136 |
Figure 85 | Benefits analyzed by respondents' roles | 137 |
Figure 86 | BBI analyzed by product | 139 |
Figure 87 | The BBI score trend analyzed by product | 140 |
Figure 88 | BBI analyzed by age | 141 |
Figure 89 | BBI analyzed by evaluation factors | 142 |
Figure 90 | BBI analyzed by license fees, deployment breadth, platform | 143 |
Figure 91 | BBI analyzed by Web deployment rates | 144 |
Figure 92 | BBI analyzed by implementers | 145 |
Figure 93 | BBI analyzed by implementation time | 146 |
Figure 94 | BBI analyzed by support quality | 146 |
Figure 95 | BBI analyzed by customer demography | 148 |
Figure 96 | Cost of Ownership Index | 150 |
Figure 97 | Chances of products being evaluated, by respondent role and org. size | 153 |
Figure 98 | Chances of products being evaluated, by geography | 155 |
Figure 99 | Evaluation frequency trend | 157 |
Figure 100 | Likelihood of a formal evaluation | 160 |
Figure 101 | Vendors' preferred description of their niche | 161 |
Figure 102 | Vendors most often encountered in competitive sales | 163 |
Figure 103 | Win rates, including and excluding non-buyers | 165 |
Figure 104 | Selection rates by evaluation type | 167 |
Figure 105 | Win rate trends in recent and mature projects | 169 |
Figure 106 | Win rates by organization size | 171 |
Figure 107 | Win rates by organization location | 173 |
Figure 108 | Customer demographics | 175 |
Figure 109 | License type analysis by product | 176 |
Figure 110 | Seats sold, excluding unlimited licenses | 177 |
Figure 111 | Average and median deployed seats | 179 |
Figure 112 | Percentages of sites with large numbers of deployed seats | 180 |
Figure 113 | Prevalence rates in organizations | 182 |
Figure 114 | Shelfware rates | 186 |
Figure 115 | Likelihood of deploying all seats within 12 months | 187 |
Figure 116 | Inclination to buy more seats | 188 |
Figure 117 | Inclination to buy more seats | 189 |
Figure 118 | Buying intentions ãPositive gapä trend | 191 |
Figure 119 | Primary support method trend | 192 |
Figure 120 | Analysis of primary support methods by respondent roles | 193 |
Figure 121 | Analysis of primary support methods by product and vendor | 195 |
Figure 122 | Analysis of primary support methods by deployment characteristics | 197 |
Figure 123 | Analysis of primary support methods by demographics | 198 |
Figure 124 | Overall ratings of product support | 199 |
Figure 125 | Overall support score trend | 199 |
Figure 126 | Support quality ratings by method and respondent role | 201 |
Figure 127 | Support quality ratings by product and vendor | 203 |
Figure 128 | Support score trend by product | 205 |
Figure 129 | Support quality ratings by vendor size | 206 |
Figure 130 | Support quality ratings by site age and selection method | 207 |
Figure 131 | Support quality ratings by customer size | 209 |
Figure 132 | Regional analysis of support quality rating | 210 |
Figure 133 | Discontinued usage analysis | 213 |
Figure 134 | The possible effects of standardization preferences | 215 |
Figure 135 | Preferred products to retain when standardizing | 216 |
Figure 136 | Reasons given for standardization | 218 |
Figure 137 | User and vendor perception of standardization reasons | 219 |
Figure 138 | The product loyalty league table | 222 |
Figure 139 | Loyalty scores trend | 223 |
Figure 140 | All the implementation resources used | 224 |
Figure 141 | The primary implementation resource | 225 |
Figure 142 | Implementation resources by respondents' roles | 226 |
Figure 143 | The implementation mix by product and suite | 227 |
Figure 144 | The implementation mix by application characteristics | 228 |
Figure 145 | The implementation mix by organization demographics | 229 |
Figure 146 | Implementation fee distribution | 230 |
Figure 147 | Implementation fees trend | 231 |
Figure 148 | Implementation spend by respondent type | 232 |
Figure 149 | Implementation spend by product and suite | 233 |
Figure 150 | Implementation spend by license fees and implementation factors | 234 |
Figure 151 | Implementation costs compared to license fees | 235 |
Figure 152 | Implementation spend by organization demography | 236 |
Figure 153 | Benefits vs external consulting spend | 238 |
Figure 154 | Benefits vs implementers | 239 |
Figure 155 | Implementation time distribution | 241 |
Figure 156 | Project success rates by implementation time | 242 |
Figure 157 | Problem rates by implementation time | 243 |
Figure 158 | Implementation times by respondent role | 244 |
Figure 159 | Implementation times by product and suite | 245 |
Figure 160 | Implementation times by implementation factors | 247 |
Figure 161 | Implementation times by organization demographics | 248 |
Figure 162 | Implemented within three and six months | 249 |
Figure 163 | Problem categories | 252 |
Figure 164 | Reported problem rates by respondent type | 253 |
Figure 165 | Differing perceptions of problem areas | 254 |
Figure 166 | Problem levels by age of project | 256 |
Figure 167 | Problem trends since 2002 | 257 |
Figure 168 | Most-serious problem trends in The BI/OLAP Surveys | 258 |
Figure 169 | People problems by rollout times | 260 |
Figure 170 | People problems analyzed by organization demographics | 262 |
Figure 171 | Reported incidence of data problems by role and product | 264 |
Figure 172 | Reported incidence of data problems by suite and data volume | 265 |
Figure 173 | Reported incidence of product problems by respondent role | 266 |
Figure 174 | Reported incidence of product problems by product and suite | 268 |
Figure 175 | Reported incidence of product problems by mode and age | 270 |
Figure 176 | Reported incidence of product problems by production selection criteria | 272 |
Figure 177 | Reported incidence of product problems by deal size, platform | 273 |
Figure 178 | Reported incidence of product problems by implementation factors | 275 |
Figure 179 | Reported incidence of product problems by performance, data volumes | 277 |
Figure 180 | Environmental problems | 278 |
Figure 181 | Normalized product-related problems | 280 |
Figure 182 | Analyzing the problem mix by respondent role and product | 282 |
Figure 183 | Analyzing the problem mix by deal characteristics | 284 |
Figure 184 | Analyzing the problem mix by implementation factors | 285 |
Figure 185 | Analyzing the problem mix by data volumes and demographics | 287 |
Figure 186 | Deterrents to wider deployment, by respondent type | 290 |
Figure 187 | Deterrents to wider deployment, by product and suite | 292 |
Figure 188 | Deterrents analyzed by selection methods | 294 |
Figure 189 | Deterrents analyzed by implementation leader and time | 295 |
Figure 190 | No deterrents to wider deployment | 296 |
Figure 191 | Applications categorized | 297 |
Figure 192 | Application analysis by respondents' roles | 298 |
Figure 193 | Application analysis by product and suite | 300 |
Figure 194 | Application analysis by selection methods | 301 |
Figure 195 | Application analysis by license fees, breadth, platform | 303 |
Figure 196 | Application analysis by implementation factors | 305 |
Figure 197 | Application analysis by data volumes | 306 |
Figure 198 | Application analysis by organization demographics | 307 |
Figure 199 | Percentage of Web deployed seats from 2001 to 2008 | 308 |
Figure 200 | Web deployment trend vs forecasts | 310 |
Figure 201 | Web deployment analysis by respondent and organization | 312 |
Figure 202 | Web deployment analysis by product and suite | 313 |
Figure 203 | Web deployment analysis by selection and implementation factors | 315 |
Figure 204 | Web deployment analysis by query time and data volume | 316 |
Figure 205 | Web deployment trends by product | 317 |
Figure 206 | Reported success rates by Web deployment rates | 318 |
Figure 207 | Extranet plans | 320 |
Figure 208 | Extranet rate trends: perception vs reality | 321 |
Figure 209 | Extranet analysis by respondent and organization type | 323 |
Figure 210 | Extranet analysis by product | 325 |
Figure 211 | Extranet analysis by deployment aspects | 326 |
Figure 212 | Extranet target trend | 327 |
Figure 213 | Extranet targets by product | 328 |
Figure 214 | Extranet targets by roles and demographic factors | 329 |
Figure 215 | Internet browsers in use | 331 |
Figure 216 | Browsers by product | 332 |
Figure 217 | Browsers by role and organization demography | 333 |
Figure 218 | Preferred Web browser architectures | 335 |
Figure 219 | Preferred Web architectures by product | 337 |
Figure 220 | Preferred Web architectures by role and organization factors | 339 |
Figure 221 | Server platforms | 341 |
Figure 222 | Overall server platforms trend | 342 |
Figure 223 | Detailed platform trends, 2001-2008 | 343 |
Figure 224 | Platform analysis by respondents' roles and organization demography | 345 |
Figure 225 | Platform analysis by product and vendor | 347 |
Figure 226 | Platform analysis by purchase factor | 349 |
Figure 227 | Platform analysis by implementation factors | 350 |
Figure 228 | Business benefits and goal achievement analysis by platform | 351 |
Figure 229 | 32-bit and 64-bit server distribution analyzed by product and volumes | 353 |
Figure 230 | Analysis Services client tools | 357 |
Figure 231 | Essbase client tools | 358 |
Figure 232 | SAP BI/BW client tools | 360 |
Figure 233 | TM1 client tools | 361 |
Figure 234 | Comparing the OLAP server client tools markets | 361 |
Figure 235 | Data sources accessed by Actuate | 363 |
Figure 236 | Data sources accessed by arcplan | 363 |
Figure 237 | Data sources accessed by Bissantz | 364 |
Figure 238 | Data sources accessed by BusinessObjects and Crystal | 365 |
Figure 239 | Data sources accessed by Cognos front-end tools | 366 |
Figure 240 | Data sources accessed by Cubeware Cockpit | 366 |
Figure 241 | Data sources accessed by WebFOCUS | 367 |
Figure 242 | Data sources accessed by Microsoft Reporting Services and PivotTables | 368 |
Figure 243 | Data sources accessed by Panorama | 368 |
Figure 244 | Average number of data sources accessed by BI client tools | 369 |
Figure 245 | Data sources for BI applications | 371 |
Figure 246 | Database vendor trend for BI data | 372 |
Figure 247 | Data sources by BI input data volumes | 374 |
Figure 248 | Data sources by product and vendor | 376 |
Figure 249 | Data sources by product type and platform | 378 |
Figure 250 | Top dozen purchased BI tools in Microsoft database sites | 380 |
Figure 251 | Top five BI suites in Microsoft database sites | 380 |
Figure 252 | Top dozen purchased BI tools in Oracle database sites | 381 |
Figure 253 | Top five multi-product BI suites in Oracle database sites | 381 |
Figure 254 | Top dozen purchased BI tools in IBM database sites | 382 |
Figure 255 | Top five multi-product BI suites in IBM database sites | 382 |
Figure 256 | Top dozen purchased BI tools in open source database sites | 383 |
Figure 257 | Top five multi-product BI suites in open source database sites | 383 |
Figure 258 | Top dozen purchased BI tools in Teradata sites | 384 |
Figure 259 | Top five multi-product BI suites in Teradata sites | 384 |
Figure 260 | Top dozen purchased BI tools in flat files data sites | 385 |
Figure 261 | Top five multi-product BI suites in flat files data sites | 385 |
Figure 262 | Top dozen purchased BI tools in manual data entry sites | 386 |
Figure 263 | Top five multi-product BI vendors in manual data entry sites | 386 |
Figure 264 | Reported input data volumes | 388 |
Figure 265 | Trend of median input data volumes | 389 |
Figure 266 | Median input data volume trend for three major products | 390 |
Figure 267 | Input data volumes analyzed by respondents' roles | 391 |
Figure 268 | Input data volumes analyzed by product | 393 |
Figure 269 | Proportion of customers with multi-terabyte applications | 394 |
Figure 270 | Input data volumes analyzed by deployment factors | 395 |
Figure 271 | Input data volumes analyzed by platform | 396 |
Figure 272 | Input data volumes analyzed by implementation factors | 397 |
Figure 273 | Input data volumes analyzed by industry | 398 |
Figure 274 | Input data volumes analyzed by industry | 399 |
Figure 275 | Support quality, goals and benefits vs input data volumes | 400 |
Figure 276 | Performance problems compared to other product-related problems | 401 |
Figure 277 | Goals and business benefits vs query performance | 403 |
Figure 278 | Complaints vs query performance | 404 |
Figure 279 | Reported query times trends since 2002 | 408 |
Figure 280 | Quartile analysis of query times by role and data volume | 409 |
Figure 281 | Quartile analysis of query times by product and selection method | 410 |
Figure 282 | Median query time trend for key products | 411 |
Figure 283 | Quartile analysis of query times by platform and Web deployment | 412 |
Figure 284 | Median query times vs input data volumes | 414 |
Figure 285 | Query over/under-performance | 415 |
Figure 286 | Performance complaints | 417 |
Figure 287 | Query times vs volumes trend | 419 |
Figure 288 | Query times vs complaints | 420 |
Figure 289 | Deterrents to wider deployment | 421 |
Figure 290 | Goals and business benefits vs load/calculate times | 423 |
Figure 291 | Data latency by respondent role and input data volume | 425 |
Figure 292 | Data latency by product and suite | 427 |
Figure 293 | Median build/calculate times vs input data volumes | 428 |
Figure 294 | Pre-calculate/build time over/under-performance | 430 |
Figure 295 | Bullet graphs used for displaying KPIs | 431 |
Figure 296 | Root KPI: Business Benefits Index | 433 |
Figure 297 | Root KPI: Goal achievement | 434 |
Figure 298 | Root KPI: Competitive win rate | 435 |
Figure 299 | Root KPI: Selection based on product factors | 436 |
Figure 300 | Root KPI: Prevalence rates in multi-product sites | 437 |
Figure 301 | Root KPI: Standardization preferences | 438 |
Figure 302 | Root KPI: Intention to buy more licenses | 439 |
Figure 303 | Root KPI: Discontinuances | 440 |
Figure 304 | Root KPI: Breadth of deployment | 441 |
Figure 305 | Root KPI: Range of applications deployed | 442 |
Figure 306 | Root KPI: Number of departments served | 443 |
Figure 307 | Root KPI: Deployed seats | 444 |
Figure 308 | Root KPI: Data volumes | 445 |
Figure 309 | Root KPI: Cost of Ownership Index | 446 |
Figure 310 | Root KPI: Administrative effort | 447 |
Figure 311 | Root KPI: Implemented within three months | 448 |
Figure 312 | Root KPI: Product-related problems | 449 |
Figure 313 | Root KPI: Product-related deterrents to wider deployment | 450 |
Figure 314 | Root KPI: Product reliability | 451 |
Figure 315 | Root KPI: Product support | 452 |
Figure 316 | Root KPI: Query performance complaints | 453 |
Figure 317 | Root KPI: Query performance | 454 |
Figure 318 | Root KPI: Data latency | 455 |
Figure 319 | Root KPI: Data latency deterring wider deployment | 456 |
Figure 320 | Root KPI: Web deployment | 457 |
Figure 321 | Root KPI: Extranets | 458 |
Figure 322 | Aggregated KPI: Business achievement KPIs | 459 |
Figure 323 | Aggregated KPI: Costs KPIs | 460 |
Figure 324 | Aggregated KPI: Scalability KPIs | 461 |
Figure 325 | Aggregated KPI: Quality and support KPIs | 462 |
Figure 326 | Aggregated KPI: Performance KPIs | 463 |
Figure 327 | Aggregated KPI: Loyalty KPIs | 464 |
Figure 328 | Aggregated KPI: Web KPIs | 465 |
Figure 329 | Overall KPI | 466 |
Figure 330 | Actuate KPI dashboard | 467 |
Figure 331 | arcplan KPI dashboard | 468 |
Figure 332 | Bissantz KPI dashboard | 469 |
Figure 333 | Board KPI dashboard | 470 |
Figure 334 | BusinessObjects KPI dashboard | 471 |
Figure 335 | Cognos Analysis KPI dashboard | 472 |
Figure 336 | Cognos Reporting KPI dashboard | 473 |
Figure 337 | Cognos TM1 Server KPI dashboard | 474 |
Figure 338 | Crystal Reports KPI dashboard | 475 |
Figure 339 | Hyperion Essbase KPI dashboard | 477 |
Figure 340 | Infor PM OLAP KPI dashboard | 478 |
Figure 341 | Microsoft Analysis Services KPI dashboard | 479 |
Figure 342 | Microsoft Excel PivotTables KPI dashboard | 480 |
Figure 343 | Microsoft Reporting Services KPI dashboard | 481 |
Figure 344 | MicroStrategy KPI dashboard | 482 |
Figure 345 | MIK KPI dashboard | 483 |
Figure 346 | Oracle BIEE/BISEO dashboard | 484 |
Figure 347 | Panorama dashboard | 485 |
Figure 348 | QlikView dashboard | 486 |
Figure 349 | SAP BI/BW dashboard | 487 |
Figure 350 | Targit dashboard | 488 |
Figure 351 | WebFOCUS dashboard | 489 |