The BI Survey 7 - Charts and tables
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Figure 1 | Charting example |
Figure 2 | Illustrating means and quartiles |
Figure 3 | Recommended Survey invitation wording |
Figure 4 | Sample make-up |
Figure 5 | Purchase rates |
Figure 6 | Respondents‚ roles overall |
Figure 7 | Respondents‚ roles by product, vendor, architecture |
Figure 8 | Respondents‚ roles by fees, volumes, organization |
Figure 9 | Geographic trend of The BI/OLAP Surveys |
Figure 10 | Location of respondents‚ parent organizations |
Figure 11 | Geographic scope of respondents‚ organizations |
Figure 12 | Organization total revenues |
Figure 13 | Organization size by employees |
Figure 14 | Industry sector analysis |
Figure 15 | Industry sector analysis by product |
Figure 16 | Industry sector analysis by multi-product vendor |
Figure 17 | Products included in the sample |
Figure 18 | Augmented samples |
Figure 19 | Usage levels of SAP BI/BW ŚBusiness Content‚ |
Figure 20 | Percentage of data originating from SAP ERP |
Figure 21 | SAP BI Accelerator usage and plans |
Figure 22 | The architectural mix |
Figure 23 | Time since purchase |
Figure 24 | Age profiles |
Figure 25 | Comparing product mixes in mature and recent sites |
Figure 26 | How did you compile a list of BI products to evaluate? |
Figure 27 | Influences by products evaluated |
Figure 28 | Influences by license fees, platforms and data volumes |
Figure 29 | Influences by organization demographics |
Figure 30 | Relative influence of industry analysts |
Figure 31 | Product shares by analyst influence |
Figure 32 | BBI by analyst influence |
Figure 33 | Goals and benefits achieved vs selection method |
Figure 34 | Evaluation trends |
Figure 35 | Reasons given for choosing BI products |
Figure 36 | Reasons for choosing products by type of respondent |
Figure 37 | Reasons for choosing different products |
Figure 38 | Aggregated reasons for choosing different products |
Figure 39 | Reasons for product selection by organization demography |
Figure 40 | Aggregated reasons for product selection by organization demography |
Figure 41 | Benefits-driven ranking of selection criteria |
Figure 42 | License fee distribution |
Figure 43 | License fees by respondent type |
Figure 44 | License fees by product, vendor and architecture |
Figure 45 | License fees by evaluation method, deployment and platform |
Figure 46 | License fees by implementation |
Figure 47 | License fees by demography |
Figure 48 | Goals and benefits vs license fees |
Figure 49 | Proportion of employees using BI applications |
Figure 50 | Proportion of users by respondent type |
Figure 51 | Proportion of users by product, vendor and architecture |
Figure 52 | Proportion of users by timing, license, implementer, volumes |
Figure 53 | Proportion of users by organization demography |
Figure 54 | Departments using BI by product |
Figure 55 | Departments using BI by organization demography |
Figure 56 | Net people involved in running and administering projects |
Figure 57 | Overall achievement of business goals |
Figure 58 | Achievement of business goals, analyzed by product and vendor |
Figure 59 | Achievement of business goals, analyzed by product and vendor |
Figure 60 | Goal achievement scores, analyzed by age |
Figure 61 | Product goal achievement scores vs age |
Figure 62 | Benefit weightings |
Figure 63 | Weighted benefit achievement levels |
Figure 64 | Overall BBI calculation |
Figure 65 | Trends in reported benefits |
Figure 66 | BBI analyzed by product and vendor |
Figure 67 | The BBI score trend analyzed by product |
Figure 68 | BBI analyzed by architecture, selection, age, distribution |
Figure 69 | BBI analyzed by Web deployment rates and license fees |
Figure 70 | BBI analyzed by implementation factors |
Figure 71 | BBI analyzed by customer demography |
Figure 72 | Cost of Ownership Index |
Figure 73 | Analysis of chances of products being evaluated |
Figure 74 | Evaluation frequency trend |
Figure 75 | Likelihood of a formal evaluation |
Figure 76 | Vendors most often encountered in competitive sales |
Figure 77 | Win rates, including and excluding non-buyers |
Figure 78 | Selection rates by evaluation type |
Figure 79 | Win rate trends in recent and mature projects |
Figure 80 | Win rates by organization size |
Figure 81 | Win rates by organization location |
Figure 82 | Customer demographics |
Figure 83 | Unlimited license analysis by product |
Figure 84 | Seats sold, excluding unlimited licenses |
Figure 85 | Average and median deployed seats |
Figure 86 | Percentages of sites with large numbers of deployed seats |
Figure 87 | Prevalence rates in organizations |
Figure 88 | Shelfware rates |
Figure 89 | Likelihood of deploying all seats within 12 months |
Figure 90 | Inclination to buy more seats |
Figure 91 | Buying intentions "Positive gap" trend |
Figure 92 | Primary support method trend |
Figure 93 | Analysis of primary support methods |
Figure 94 | Overall ratings of product support |
Figure 95 | Support ratings by method |
Figure 96 | Support quality ratings by product |
Figure 97 | Product support scores trend |
Figure 98 | Support quality ratings by vendor size |
Figure 99 | Support quality ratings by type of respondent |
Figure 100 | Product support rating vs license fees paid and evaluation method |
Figure 101 | Discontinued usage analysis |
Figure 102 | The possible effects of standardization preferences |
Figure 103 | Preferred products to retain when standardizing |
Figure 104 | Reasons given for standardization |
Figure 105 | The product loyalty league table |
Figure 106 | Loyalty scores trend |
Figure 107 | All the implementation resources used |
Figure 108 | The primary implementation resource |
Figure 109 | The implementation mix by product and vendor |
Figure 110 | The implementation mix by architecture, volumes and demographics |
Figure 111 | Implementation fee distribution |
Figure 112 | Implementation fees trend |
Figure 113 | Implementation spend by respondent type and analyst |
Figure 114 | Implementation spend by product, architecture and volumes |
Figure 115 | Implementation spend by license fees, and implementation factors |
Figure 116 | Implementation costs compared to license fees |
Figure 117 | Implementation spend by organization demography |
Figure 118 | Benefits vs external consulting spend |
Figure 119 | Benefits vs primary implementation resource |
Figure 120 | Implementation time distribution |
Figure 121 | Project success rates by implementation time |
Figure 122 | Problem rates by implementation time |
Figure 123 | Implementation times by product and vendor |
Figure 124 | Implementation times by environment |
Figure 125 | Implementation times by organization demographics |
Figure 126 | Implemented within six months |
Figure 127 | Problem categories |
Figure 128 | Reported problem rates by respondent type |
Figure 129 | Differing perceptions of problem areas |
Figure 130 | Problem levels by age of project |
Figure 131 | Problem trends since 2002 |
Figure 132 | Most-serious problem trends in The BI/OLAP Surveys |
Figure 133 | People problems by rollout times |
Figure 134 | People problems analyzed by organization demographics |
Figure 135 | Reported incidence of data problems by product and data volume |
Figure 136 | Product-related problems analysis |
Figure 137 | Product-related problems by version |
Figure 138 | Product-related problems by input data volume |
Figure 139 | Product-related problems by lead implementer |
Figure 140 | Environmental problems |
Figure 141 | Normalized product-related problems |
Figure 142 | Analyzing the problem mix by product and architecture |
Figure 143 | Analyzing the problem mix by respondent and implementers |
Figure 144 | Analyzing the problem mix by data volumes and demographics |
Figure 145 | Deterrents to wider deployment, by respondent type |
Figure 146 | Deterrents to wider deployment, by product and vendor |
Figure 147 | Deterrents analyzed by architecture, license fees and platform |
Figure 148 | Deterrents analyzed by implementation leader and time |
Figure 149 | No deterrents to wider deployment |
Figure 150 | Applications categorized |
Figure 151 | Application analysis by role and industry analyst |
Figure 152 | Application analysis by product and vendor |
Figure 153 | Application analysis by architecture and purchase characteristics |
Figure 154 | Application analysis by platform, volumes and implementer |
Figure 155 | Application analysis by organization demographics |
Figure 156 | Percentage of Web deployed seats from 2001 to 2007 |
Figure 157 | Web deployment trend vs forecasts |
Figure 158 | Web deployment analysis by respondent and organization |
Figure 159 | Web deployment analysis by product and vendor |
Figure 160 | Web deployment analysis by selection and implementation |
Figure 161 | Web deployment analysis by application |
Figure 162 | Web deployment trends by product |
Figure 163 | Reported success rates by Web deployment rates |
Figure 164 | Extranet plans |
Figure 165 | Extranet rate trends: perception vs reality |
Figure 166 | Extranet analysis by product, vendor and architecture |
Figure 167 | Extranet analysis by platform, implementation and demographics |
Figure 168 | Extranet target trend |
Figure 169 | Extranet targets by product factors |
Figure 170 | Extranet targets by demographic factors |
Figure 171 | Internet browsers in use |
Figure 172 | Browsers by product, vendor, source and platform |
Figure 173 | Browsers by organization demography |
Figure 174 | Preferred Web browser architectures |
Figure 175 | Preferred Web architectures by product factors |
Figure 176 | Preferred Web architectures by organization factors |
Figure 177 | Server platforms |
Figure 178 | Overall server platforms trend |
Figure 179 | Detailed platform trends, 2001-2007 |
Figure 180 | Platform analysis by product, vendor and architecture |
Figure 181 | Platform analysis by input data volumes |
Figure 182 | Platform analysis by organization factors |
Figure 183 | Platform analysis by organization factors |
Figure 184 | Business benefits and goal achievement analysis by platform |
Figure 185 | 32-bit and 64-bit server distribution analyzed by product factors |
Figure 186 | 32-bit and 64-bit server distribution by organization demography |
Figure 187 | Analysis Services client tools |
Figure 188 | Essbase client tools |
Figure 189 | SAP BI/BW client tools |
Figure 190 | TM1 client tools |
Figure 191 | Comparing the OLAP server client tools markets |
Figure 192 | Data sources accessed by arcplan |
Figure 193 | Data sources accessed by BusinessObjects and Crystal |
Figure 194 | Data sources accessed by Cognos front-end tools |
Figure 195 | Data sources accessed by Cubeware Cockpit |
Figure 196 | Data sources accessed by WebFOCUS |
Figure 197 | Data sources accessed by Microsoft PivotTables and Reporting Services |
Figure 198 | Average number of data sources accessed by BI client tools |
Figure 199 | Source databases for BI applications |
Figure 200 | Database sources for BI data |
Figure 201 | Data sources by input data volume bands |
Figure 202 | Data sources by product and vendor |
Figure 203 | Data sources by product type and platform |
Figure 204 | Data sources by organization demography |
Figure 205 | Top ten purchased BI tools in Microsoft database sites |
Figure 206 | Top five multi-product BI vendors in Microsoft database sites |
Figure 207 | Top ten purchased BI tools in Oracle database sites |
Figure 208 | Top five multi-product BI vendors in Oracle database sites |
Figure 209 | Top ten purchased BI tools in IBM database sites |
Figure 210 | Top five multi-product BI vendors in IBM database sites |
Figure 211 | Top ten purchased BI tools in Sybase database sites |
Figure 212 | Top five multi-product BI vendors in Sybase database sites |
Figure 213 | Top ten purchased BI tools in Teradata sites |
Figure 214 | Top five multi-product BI vendors in Teradata sites |
Figure 215 | Top ten purchased BI tools in sites performing manual data entry |
Figure 216 | Top five multi-product BI vendors in manual data entry sites |
Figure 217 | Reported input data volumes |
Figure 218 | Trend of median input data volumes |
Figure 219 | Median input data volume trend for three major products |
Figure 220 | Reported mean input data volumes by product |
Figure 221 | Median and quartile analysis of data volumes by product |
Figure 222 | Changes in relative market shares with data volumes |
Figure 223 | Mean, median and quartile analysis of data volumes by platform |
Figure 224 | Mean, median and quartile analysis of data volumes by 32- vs 64-bit |
Figure 225 | Reported input data volumes by architecture |
Figure 226 | Reported input data volumes by lead implementer |
Figure 227 | Reported input data volumes by industry sector |
Figure 228 | Reported input data volumes by customer demographic |
Figure 229 | License fees by input data volume band |
Figure 230 | Support quality, goals and benefits vs input data volumes |
Figure 231 | Performance problems compared to other product-related problems |
Figure 232 | Goals and business benefits vs query performance |
Figure 233 | Complaints vs query performance |
Figure 234 | Comparing reported query times since 2002 |
Figure 235 | Quartile analysis of query times |
Figure 236 | Median query time trend |
Figure 237 | Median query times vs median input data volumes |
Figure 238 | Performance complaints |
Figure 239 | Query times vs volumes trend |
Figure 240 | Query times vs complaints |
Figure 241 | Deterrents to wider deployment |
Figure 242 | Poor query performance as a deterrent to wider deployment |
Figure 243 | Network bandwidth as a deterrent to wider deployment |
Figure 244 | Reported median typical query times by architecture |
Figure 245 | Reported median typical query times by platform |
Figure 246 | Goals and business benefits vs load/calculate times |
Figure 247 | Data latency by product and vendor |
Figure 248 | Data latency by architecture and data volumes |
Figure 249 | Median latency times vs median input data volumes |
Figure 250 | Reported median latency times by architecture |