| 1 | Your Project Name | |
| 2 | Step 1: Business Case Assessment | |
| 3 | Determine the business need | |
| 4 | Identify the business need | |
| 5 | Determine current financial consequences of the business need | |
| 6 | Assess the current decision-support system (DSS) solutions | 3 |
| 7 | Assess current usage of the existing DSS | |
| 8 | Determine the shortcomings of the existing DSS | |
| 9 | Perform gap analysis | |
| 10 | Assess the operational sources and procedures | 3 |
| 11 | Assess the data quality of operational systems | |
| 12 | Review file structures and databases | |
| 13 | Review content (domain) of source data elements | |
| 14 | Assess the current data movement | |
| 15 | Review data entry practices | |
| 16 | Review data extraction practices | |
| 17 | Review data manipulation practices | |
| 18 | Review data duplication practices | |
| 19 | Assess current operational procedures | |
| 20 | Identify poor data entry practices | |
| 21 | Identify lack of edit checks | |
| 22 | Identify defective program code | |
| 23 | Identify lack of training | |
| 24 | Assess the competitors ' BI decision-support initiatives | 3 |
| 25 | Determine the competitors' successes and failures | |
| 26 | Determine whether competitors gained market advantages | |
| 27 | Determine the BI application objectives | 6, 10, 24 |
| 28 | Identify the strategic business goals of the organization | |
| 29 | Define the overall BI decision-support objectives | |
| 30 | Define the project-specific BI application objectives | |
| 31 | Match the overall BI decision-support objectives to the strategic business goals | |
| 32 | Match the project-specific BI application objectives to the strategic business goals | |
| 33 | Propose a BI solution | 27 |
| 34 | Review current DSS solutions | |
| 35 | Review DSS gap analysis | |
| 36 | Determine how the BI application will lessen the business pain | |
| 37 | Create a high-level architecture for the proposed BI solution | |
| 38 | Consolidate and prioritize unfulfilled requirements from previous BI projects | |
| 39 | Create a high-level (conceptual) logical data model | |
| 40 | Perform a cost-benefit analysis | 27 |
| 41 | Determine costs | |
| 42 | Determine benefits ( tangible and intangible) | |
| 43 | Identify short- term benefits to the organization | |
| 44 | Identify long-term benefits to the organization | |
| 45 | Calculate the projected return on investment (ROI) | 41, 42 |
| 46 | Perform a risk assessment | 27 |
| 47 | Create a risk assessment matrix | |
| 48 | List the technology risks | |
| 49 | List the complexity risks | |
| 50 | List the integration risks | |
| 51 | List the organization risks | |
| 52 | List the project team risks | |
| 53 | List the financial investment risks | |
| 54 | Assign weights to the risks | 47 |
| 55 | Rank the risks: low, medium, or high | 54 |
| 56 | Determine the risks ( ramifications ) of not implementing a BI solution | 54 |
| 57 | Write the assessment report | 33, 40, 46 |
| 58 | Describe the business need | |
| 59 | Describe lost opportunities | |
| 60 | Describe the proposed BI solution | |
| 61 | State the cost justification and expected ROI | |
| 62 | Include risk assessment results | |
| 63 | Write recommendations (include operational business process improvements) | |
| 64 | Obtain project approval from business sponsor | 57 |
| 65 | Step 2: Enterprise Infrastructure Evaluation | |
| 66 | Section A: Technical Infrastructure Evaluation | |
| 67 | Assess the existing platform | 64 |
| 68 | Review hardware | |
| 69 | Review operating systems | |
| 70 | Review middleware, especially DBMS gateways | |
| 71 | Review custom interfaces | |
| 72 | Review network components and bandwidth | |
| 73 | Review the DBMS | |
| 74 | Review tools (CASE, ETL, OLAP, etc.) | |
| 75 | Review the meta data repository | |
| 76 | Perform gap analysis | |
| 77 | Evaluate and select new products | 64 |
| 78 | Identify the product categories you need to evaluate (hardware, DBMS, tools) | |
| 79 | List all products being considered for each category | |
| 80 | Itemize your requirements for the products | |
| 81 | Weigh each product requirement (scale of 1 to 10) | |
| 82 | Rank each product against the weighted requirements (scale of 0 to 10) | 81 |
| 83 | Determine the total score for each product | 82 |
| 84 | List all vendors of all products | |
| 85 | Itemize your requirements for the vendors | |
| 86 | Weigh each vendor requirement (scale of 1 to 10) | |
| 87 | Rank each vendor against the weighted requirements (scale of 0 to 10) | 86 |
| 88 | Determine the total score for each vendor | 87 |
| 89 | Evaluate the product scores and vendor scores | 83, 88 |
| 90 | Create a short list of products and vendors in each category | 89 |
| 91 | Have the products demonstrated by the vendors | 90 |
| 92 | Choose the final product in each product category | 91 |
| 93 | Obtain business sponsor approval to license the products | 92 |
| 94 | Write the technical infrastructure assessment report | 67, 77 |
| 95 | Itemize findings about servers, operating systems, middleware, etc. | |
| 96 | List the weighted requirements | |
| 97 | List the product scores | |
| 98 | List the vendor scores | |
| 99 | List the product costs | |
| 100 | List the products on the short list | |
| 101 | Explain the rationale for selecting or rejecting products | |
| 102 | Explain the final selection criteria | |
| 103 | Write the executive summary | |
| 104 | Expand the current platform | 94 |
| 105 | Order new products | |
| 106 | Install new products | 105 |
| 107 | Test new products | 106 |
| 108 | Train technical staff on new products | 106 |
| 109 | Section B: Nontechnical Infrastructure Evaluation | |
| 110 | Assess the effectiveness of existing nontechnical infrastructure components | 57 |
| 111 | Review standards for data naming, abbreviations, modeling, etc. | |
| 112 | Review the use of the development methodology | |
| 113 | Review estimating guidelines | |
| 114 | Review change-control procedures | |
| 115 | Review issues management procedures | |
| 116 | Review roles and responsibilities | |
| 117 | Review security processes and guidelines | |
| 118 | Review meta data capture and delivery processes | |
| 119 | Review meta data repository functionality | |
| 120 | Review the process for merging logical data models into the enterprise data model | |
| 121 | Review data quality measures and the cleansing triage process | |
| 122 | Review the service-level agreement (SLA) process | |
| 123 | Review the BI support function | |
| 124 | Review the dispute resolution process | |
| 125 | Review the communication process | |
| 126 | Perform gap analysis | |
| 127 | Write the nontechnical infrastructure assessment report | 110 |
| 128 | Itemize findings about inadequate standards, guidelines, procedures, etc. | |
| 129 | Write recommendations for nontechnical infrastructure changes | |
| 130 | Prioritize nontechnical infrastructure requirements for the BI project | |
| 131 | Prioritize nontechnical infrastructure requirements for outside the BI project | |
| 132 | Write the executive summary | |
| 133 | Improve the nontechnical infrastructure | 127 |
| 134 | Create time estimates for creating or modifying new standards, guidelines, procedures | |
| 135 | Change the guidelines for using the development methodology | |
| 136 | Modify the roles and responsibilities | |
| 137 | Create new processes as needed | |
| 138 | Step 3: Project Planning | |
| 139 | Determine the project requirements | 94, 127 |
| 140 | Define data requirements | |
| 141 | Define functional requirements ( reports , queries, online help function) | |
| 142 | Define infrastructure requirements (technical and nontechnical) | |
| 143 | Expand or create the high-level logical data model | 140 |
| 144 | Validate the requirements with other business people | 140, 141, 142 |
| 145 | Obtain sponsor approval for the requirements | 144 |
| 146 | Determine the condition of the source files and databases | 139 |
| 147 | Review the content of each potential source file and source database (internal and external) | |
| 148 | Assess source data violations | |
| 149 | Review technical data conversion rules | |
| 150 | Review business data domain rules | |
| 151 | Review business data integrity rules | |
| 152 | Determine which data elements are critical, important, insignificant | |
| 153 | Estimate the time needed for cleansing of critical source data | 152 |
| 154 | Estimate the time needed for cleansing of important source data | 152 |
| 155 | Review data-cleansing estimates with the business sponsor and prioritize the cleansing effort | 153, 154 |
| 156 | Determine or revise the cost estimates | 146 |
| 157 | Review the technical infrastructure assessment report | |
| 158 | Review the nontechnical infrastructure assessment report | |
| 159 | Review the project requirements | |
| 160 | Review the project constraints (time, scope, budget, resources, quality) | |
| 161 | Review the need for consulting, contracting, training | |
| 162 | Revise the original cost estimates | 161 |
| 163 | Revise the risk assessment | 146 |
| 164 | Review and revise the original risk assessment matrix | |
| 165 | Determine the likelihood of the risks materializing: low, medium, high | |
| 166 | Determine the impact of every risk: low, medium, high | |
| 167 | Define triggers | |
| 168 | Define a risk mitigation plan | |
| 169 | Define a contingency plan | |
| 170 | Identify your assumptions | |
| 171 | Include assumptions as risks on the contingency plan | |
| 172 | Review the project constraints as they relate to risk | |
| 173 | Identify critical success factors | 156, 163 |
| 174 | Define the success criteria for the BI project | |
| 175 | Determine critical success factors | 174 |
| 176 | Review critical success factors with the business sponsor | 175 |
| 177 | Obtain agreement and cooperation on the critical success factors from the business sponsor | 176 |
| 178 | Prepare the project charter | 173 |
| 179 | State the purpose and reason for the BI project | |
| 180 | State costs and benefits | |
| 181 | Describe infrastructure and business process improvements | |
| 182 | Describe the high-level scope (data and functions) | |
| 183 | List items not in the scope | |
| 184 | List expectations from the business people (preliminary SLA) | |
| 185 | Define team structure, roles, and responsibilities | |
| 186 | List risks, assumptions, and constraints | |
| 187 | List critical success factors | |
| 188 | Create a high-level project plan | 173 |
| 189 | Create a work breakdown structure | |
| 190 | Determine base estimates for all tasks | |
| 191 | Identify task dependencies | |
| 192 | Revise the base estimates for assigned resources | 191 |
| 193 | Address skill level | |
| 194 | Address subject matter expertise | |
| 195 | Address additional administrative activities | |
| 196 | Address non-work- related activities | |
| 197 | Identify resource dependencies (resource leveling) | 192 |
| 198 | Create a critical path method (CPM) or Pert chart | 197 |
| 199 | Create a Gantt chart | 197 |
| 200 | Kick off the project | 178, 188 |
| 201 | Prepare an agenda for the kickoff meeting | |
| 202 | Call a kickoff meeting | 201 |
| 203 | Assign roles and responsibilities to core team members | 202 |
| 204 | Identify extended team members and review their responsibilities | 202 |
| 205 | Discuss the project charter | 202 |
| 206 | Walk through the project plan | 202 |
| 207 | Discuss the concept of self-organizing teams | 202 |
| 208 | Step 4: Project Requirements Definition | |
| 209 | Define the requirements for technical infrastructure enhancements | 200 |
| 210 | Define the requirements for additional hardware | |
| 211 | Define the requirements for additional middleware | |
| 212 | Define the requirements for a new DBMS or upgrades to the existing DBMS | |
| 213 | Define the requirements for the network or upgrades to it | |
| 214 | Determine the security requirements | |
| 215 | Define the requirements for development tools (CASE, ETL) | |
| 216 | Define the requirements for data access and reporting tools (OLAP, report writers) | |
| 217 | Define the requirements for a new data mining tool | |
| 218 | Determine whether to license or build a meta data repository | |
| 219 | Determine how to enhance an existing meta data repository | |
| 220 | Define the requirements for nontechnical infrastructure enhancements | 200 |
| 221 | Define the requirements for governance (prioritizing) standards and procedures | |
| 222 | Define the requirements for the development methodology | |
| 223 | Define the requirements for estimating guidelines | |
| 224 | Define the requirements for the scope management process | |
| 225 | Define the requirements for the issues management process | |
| 226 | Define the requirements for roles and responsibilities | |
| 227 | Define the requirements for the security process | |
| 228 | Define the requirements for the meta data capture and delivery process | |
| 229 | Define the requirements for logical data modeling | |
| 230 | Define the requirements for the data cleansing process | |
| 231 | Define the requirements for the testing procedures | |
| 232 | Define the requirements for the SLA process | |
| 233 | Define the requirements for the BI support function | |
| 234 | Define the requirements for the dispute resolution process | |
| 235 | Define the requirements for the communication process | |
| 236 | Define the reporting requirements | 200 |
| 237 | Collect or create sample report layouts | |
| 238 | Collect or create sample queries | |
| 239 | Define business rules for the reports | |
| 240 | Define aggregation and summarization rules | |
| 241 | Define reporting dimensions | |
| 242 | Define query libraries | |
| 243 | Identify stewards of the libraries | |
| 244 | Get samples of ad hoc queries (if possible) | |
| 245 | Define access interfaces | |
| 246 | Define the requirements for source data | 200 |
| 247 | Define all source data elements | |
| 248 | Classify data elements as critical, important, insignificant | |
| 249 | Define the data domains ( allowable values) | |
| 250 | Define the significant and obvious business rules for the data | |
| 251 | Determine the data-cleansing requirements | |
| 252 | Define the historical data requirements | |
| 253 | Review the project scope | 209, 220, 236, 246 |
| 254 | Compare the detailed project requirements to the high-level scope in the project charter | |
| 255 | Review the project constraints (time, scope, budget, resources, quality) | |
| 256 | Determine whether the scope is realistic under those constraints | |
| 257 | Renegotiate the scope, if necessary | 256 |
| 258 | Create a change-control document | |
| 259 | Create an issues log | |
| 260 | Expand the logical data model | 253 |
| 261 | Add newly discovered entities and relationships | |
| 262 | Refine the logical data model by resolving the many-to-many relationships | 261 |
| 263 | Add unique identifiers to each entity | 262 |
| 264 | Attribute the logical data model with critical data elements | 262 |
| 265 | Define preliminary service-level agreements | 253 |
| 266 | Identify or revise the expectations for availability | |
| 267 | Identify or revise the expectations for security | |
| 268 | Identify or revise the expectations for response time | |
| 269 | Identify or revise the expectations for data cleanliness | |
| 270 | Identify or revise the expectations for ongoing support | |
| 271 | Write the application requirements document | 260, 265 |
| 272 | Describe the technical infrastructure requirements | |
| 273 | Describe the nontechnical infrastructure requirements | |
| 274 | Describe the reporting requirements | |
| 275 | Describe the ad hoc and canned query requirements | |
| 276 | Describe the requirements for source data, including history | |
| 277 | Describe the data-cleansing requirements | |
| 278 | Describe the security requirements | |
| 279 | List the preliminary SLAs | |
| 280 | Step 5: Data Analysis | |
| 281 | Analyze the external data sources | 271 |
| 282 | Identify entities and relationships from each external data source | |
| 283 | Merge the new entities and relationships from the external data sources into the logical data model | |
| 284 | Refine the logical data model | 271, 281 |
| 285 | Fully attribute the logical data model to include all required source data elements | |
| 286 | Create new entities and relationships where needed to store the new attributes | |
| 287 | Analyze the layout of all identified source files and source databases | |
| 288 | Analyze the content of all identified source data elements | |
| 289 | Create the data-specific business meta data components | |
| 290 | Analyze the source data quality | 271, 281 |
| 291 | Apply business data domain rules and business data integrity rules | |
| 292 | Look for default values | |
| 293 | Look for missing values | |
| 294 | Look for cryptic values | |
| 295 | Look for contradicting values | |
| 296 | Look for values that violate the business rules | |
| 297 | Look for missing primary keys | |
| 298 | Look for duplicate primary keys | |
| 299 | Determine the severity of the problem | |
| 300 | Determine the criticality of the problem | |
| 301 | Expand the enterprise logical data model | 284, 290 |
| 302 | Merge the project-specific logical data model into the enterprise logical data model | |
| 303 | Identify data discrepancies and inconsistencies between the logical data models | |
| 304 | Resolve data discrepancies | 290 |
| 305 | Discuss the discrepancies with data owners and other business executives | |
| 306 | Adjust either the project-specific logical data model or the enterprise logical data model | |
| 307 | Notify other affected project teams | |
| 308 | Document the discrepancies as meta data and schedule time for resolutions | |
| 309 | Write the data-cleansing specifications | 304 |
| 310 | Review the classification of data elements: critical, important, insignificant | |
| 311 | Write data-cleansing specifications for all critical data elements | |
| 312 | Write data-cleansing specifications for selected important data elements | |
| 313 | Step 6: Application Prototyping | |
| 314 | Analyze the access requirements | 271 |
| 315 | Review the application requirements document with the subject matter expert and the business representative | |
| 316 | Analyze the report requirements | |
| 317 | Analyze the query requirements | |
| 318 | Analyze the ad hoc requirements | |
| 319 | Analyze the interface requirements | |
| 320 | Communicate all your findings to the database administrator | |
| 321 | Create a skill set matrix for each business person participating in the prototyping activities | |
| 322 | Indicate computer skill level: beginning, advanced, expert | |
| 323 | Indicate application knowledge: beginning, advanced, expert | |
| 324 | Determine the scope of the prototype | 271 |
| 325 | Determine the objective and the primary use of the prototype | |
| 326 | Decide which type of prototype to build (show-and-tell, demo, etc.) | |
| 327 | Select a subset of functions (reports, queries, ETL, interface) | |
| 328 | Select a subset of sample data from source files and source databases | |
| 329 | Create a change-control document for the prototype | |
| 330 | Create an issues log for the prototype | |
| 331 | Determine the number of prototype iterations | |
| 332 | Determine the number of prototype participants | |
| 333 | Determine the time limits for each prototype iteration | 331 |
| 334 | Estimate the cost and benefit for each prototype iteration | 331 |
| 335 | Determine the point of diminishing returns for prototyping | 331 |
| 336 | Select tools for the prototype | 271 |
| 337 | Review existing in-house tools and find out who uses them | |
| 338 | Review the availability of new reporting and querying tools | |
| 339 | Review existing or new graphical tools | |
| 340 | Review existing or new report distribution tools | |
| 341 | Review existing DBMS options for the prototype | |
| 342 | Select the platform on which the prototype will be developed | |
| 343 | Select one of the installed and tested DBMSs | |
| 344 | Select one or more existing or new tools | |
| 345 | Determine training needs for the new tools | |
| 346 | Schedule training sessions | 345 |
| 347 | Prepare the prototype charter | 314, 324, 336 |
| 348 | State the purpose of the prototype | |
| 349 | State what type of prototype you selected | |
| 350 | List who will participate (IT and business people) | |
| 351 | Define what the rules are (scope, time, iterations) | |
| 352 | Define how you will measure the success of the prototype | |
| 353 | Design the reports and queries | 347 |
| 354 | Design the reports | |
| 355 | Design the queries | |
| 356 | Design the interfaces | |
| 357 | Create a physical data model (database design) for the prototype database | |
| 358 | Identify the data to be used for the prototype | |
| 359 | Map sample source data or new test data into the prototype database | 358 |
| 360 | Build the prototype | 347 |
| 361 | Create the physical prototype database | |
| 362 | Create sample test data | |
| 363 | Load the prototype database with sample data | |
| 364 | Write a selected subset of reports | |
| 365 | Write a selected subset of queries | |
| 366 | Write a selected subset of interfaces or other functions | |
| 367 | Test reports, queries, interfaces, or other functions | 364, 365, 366 |
| 368 | Document any problems with the tool | 367 |
| 369 | Document any issues with the reports or queries | 367 |
| 370 | Document any issues with the interfaces or other functions | 367 |
| 371 | Document any issues with dirty source data | 367 |
| 372 | Validate the time and cost estimates for the BI application | 367 |
| 373 | Demonstrate the prototype | 353, 360 |
| 374 | Review reports and queries with the business people | |
| 375 | Review problems and issues with the business sponsor and the business representative | |
| 376 | Review the project requirements with the subject matter expert and the business representative | |
| 377 | Document requested changes in the change-control document | |
| 378 | Analyze the impact of requested changes on other constraints (time, quality, cost, resources) | 377 |
| 379 | Review impact of requested changes with the business sponsor and the business representative | 378 |
| 380 | Revise the application requirements document to include approved changes | 379 |
| 381 | Review lessons learned with the entire project core team and in particular with the ETL step core team | 380 |
| 382 | Use prototype demonstrations to promote the BI application | 380 |
| 383 | Perform the next prototype iteration, if applicable | 373 |
| 384 | Step 7: Meta Data Repository Analysis | |
| 385 | Analyze the meta data repository requirements | 271 |
| 386 | Review the technical infrastructure assessment report | |
| 387 | Perform a cost-benefit analysis for licensing versus building a meta data repository | |
| 388 | Make the decision to license or build a meta data repository | 387 |
| 389 | Review the nontechnical infrastructure assessment report | |
| 390 | Determine the scope of the meta data repository deliverables | |
| 391 | Prioritize the meta data repository deliverables | 390 |
| 392 | Update the application requirements document to reflect any changes | 391 |
| 393 | Analyze the interface requirements for the meta data repository | 271 |
| 394 | Analyze the meta data sources | |
| 395 | Analyze word processing files and spreadsheets | |
| 396 | Analyze DBMS dictionaries | |
| 397 | Analyze CASE, ETL, OLAP tools | |
| 398 | Analyze report writers and query tools | |
| 399 | Analyze the data mining tool | |
| 400 | Determine what import and export features are available in these tools | |
| 401 | Determine what import and export features are available in the meta data repository product | |
| 402 | Analyze the meta data repository access and reporting requirements | 271 |
| 403 | Review the original meta data repository access and reporting requirements | |
| 404 | Review the meta data security requirements | |
| 405 | Identify the access interface media (PDF, HTML) | |
| 406 | Analyze the feasibility of a context-sensitive help function | |
| 407 | Determine what reports should be produced from the meta data repository | |
| 408 | Create the logical meta model | 385, 393, 402 |
| 409 | Create business meta data entities | |
| 410 | Create technical meta data entities | |
| 411 | Determine the relationships between the meta data entities | |
| 412 | Create attributes for business and technical meta data entities | |
| 413 | Draw an entity-relationship diagram | |
| 414 | Create the meta-meta data | |
| 415 | Describe all meta data entities | 408 |
| 416 | Name the meta data entities | |
| 417 | Define all meta data entities | |
| 418 | Define the relationships between all meta data entities | |
| 419 | Define the security for meta data entities | |
| 420 | Define the physical location for meta data entities | |
| 421 | Define timeliness for meta data | |
| 422 | Define volume for meta data entities | |
| 423 | Describe all meta data attributes | 415 |
| 424 | Name the meta data attributes | |
| 425 | Define all meta data attributes | |
| 426 | Define type and length for meta data attributes | |
| 427 | Define the domain for meta data attributes | |
| 428 | Define the security for meta data attributes | |
| 429 | Define ownership for meta data attributes | |
| 430 | Define the business rules for meta data entities, attributes, and relationships | 423 |
| 431 | Step 8: Database Design | |
| 432 | Review the data access requirements | 309, 373 |
| 433 | Review the data-cleansing specifications | |
| 434 | Review the prototyping results | |
| 435 | Review detailed access and analysis requirements | |
| 436 | Review detailed reporting requirements | |
| 437 | Review detailed querying requirements | |
| 438 | Review known ad hoc querying requirements | |
| 439 | Review data security requirements | |
| 440 | Determine projected data volumes and growth factors | |
| 441 | Determine the projected number of concurrent database usages | |
| 442 | Determine the location of business people | |
| 443 | Determine the frequency of report and query executions | |
| 444 | Determine the peak and seasonal reporting periods | |
| 445 | Determine platform limitations | |
| 446 | Determine tool limitations (ETL, OLAP, report writers) | |
| 447 | Determine the aggregation and summarization requirements | 309, 373 |
| 448 | Review measures (facts) used by the prototype | |
| 449 | Review the dimensions used by the prototype | |
| 450 | Review the drill-down and roll-up functions of the prototype | |
| 451 | Review common reporting patterns among existing reports | |
| 452 | Determine the most frequently used reporting dimensions | |
| 453 | Review the logical data model with the data administrator | |
| 454 | Determine the level of detail (granularity) needed | |
| 455 | Determine how the detailed data will be accessed (drill-down or ad hoc) | |
| 456 | Determine how many business relationships (entity relationships) will be needed | |
| 457 | Design the BI target databases | 435, 447 |
| 458 | Determine the appropriate database design schemas (multidimensional or entity-relationship) | |
| 459 | Create the physical data models (database design diagrams) | |
| 460 | Create the technical meta data for the physical data models | 459 |
| 461 | Map the physical data models to the logical data model | 459 |
| 462 | Design the physical database structures | 457 |
| 463 | Determine how to cluster the tables | |
| 464 | Determine the placement of datasets | |
| 465 | Determine how to stripe disks | |
| 466 | Determine how to partition the tables across multiple disks | |
| 467 | Determine how much free space to choose | |
| 468 | Determine how much buffer space to declare | |
| 469 | Determine how large to set the blocksize | |
| 470 | Determine the most appropriate indexing strategy | |
| 471 | Determine whether referential integrity will be enforced by the DBMS | |
| 472 | Build the BI target databases | 462 |
| 473 | Create the data definition language (DDL) | |
| 474 | Define storage groups | |
| 475 | Define databases | |
| 476 | Define partitions | |
| 477 | Define tablespaces | |
| 478 | Define tables | |
| 479 | Define columns | |
| 480 | Define primary keys | |
| 481 | Define foreign keys | 480 |
| 482 | Define indices | 480 |
| 483 | Create the data control language (DCL) | 473 |
| 484 | Define parameters for the security SYSTABLE | |
| 485 | Set up group IDs | |
| 486 | Grant CRUD (create, read, update, delete) authority to group IDs | |
| 487 | Assign developers, business analysts, and programs to the appropriate group IDs | |
| 488 | Run the DDL to create the physical database structures | 473 |
| 489 | Run the DCL to grant authority to the physical database structures | 483 |
| 490 | Build the indices | 488 |
| 491 | Develop database maintenance procedures | 483 |
| 492 | Define database maintenance activities | |
| 493 | Define database backups (full and incremental backups ) | |
| 494 | Define disaster recovery procedures | |
| 495 | Define reorganization procedures for fragmented tables | |
| 496 | Define the frequency of and procedure for performance monitoring activities | |
| 497 | Prepare to monitor and tune the database designs | 491 |
| 498 | Plan to monitor the performance of ETL loads, reports, and queries at runtime | |
| 499 | Plan to use a performance-monitoring utility to diagnose performance degradation | |
| 500 | Plan to refine the database design schemas | |
| 501 | Plan to add additional indices, if necessary | |
| 502 | Prepare to monitor and tune the query designs | 491 |
| 503 | Plan to review and streamline all SQL calls in programs | |
| 504 | Plan to write pass-through queries for OLAP tools, if necessary | |
| 505 | Plan to utilize parallel query execution | |
| 506 | Step 9: Extract/Transform/Load Design | |
| 507 | Create the source-to-target mapping document | 491 |
| 508 | Review the record layouts for the source files | |
| 509 | Review the data description blocks for the source databases | |
| 510 | Review the data-cleansing specifications for source data elements | |
| 511 | Create a matrix for all target tables and target columns | |
| 512 | List all applicable source files and source databases for every target table | |
| 513 | List all relevant source data elements for every target column | |
| 514 | List data type and length for every target column | |
| 515 | List data type and length for every source data element | |
| 516 | Write transformation specifications for populating the columns | 514, 515 |
| 517 | Combine data content from multiple sources (if needed) | |
| 518 | Split data content from one data element across multiple columns (if needed) | |
| 519 | Include aggregation and summarization algorithms | |
| 520 | Include data-cleansing specifications for each column | |
| 521 | Include logic for checking referential integrity (if not performed by the DBMS) | |
| 522 | Include logic for error messages and record rejection counts | |
| 523 | Include logic for reconciliation totals (record counts, domain counts, amount counts) | |
| 524 | Test the ETL tool functions | 491 |
| 525 | Review the transformation specifications in the source-to-target mapping document | 516 |
| 526 | Determine whether the ETL tool functions can perform the required transformation logic | 525 |
| 527 | Determine what supplementary custom code must be written | 526 |
| 528 | Design the ETL process flow | 507, 524 |
| 529 | Determine the most efficient sequence to extract source data | |
| 530 | Determine the most efficient sequence to transform, cleanse , and load the data | |
| 531 | Determine the sort and merge steps in the ETL process | |
| 532 | Identify all temporary and permanent work files and tables | |
| 533 | Determine what components of the ETL process can run in parallel | |
| 534 | Determine what tables can be loaded in parallel | |
| 535 | Draw the process flow diagram | 533, 534 |
| 536 | Show the extracts from source files and source databases | |
| 537 | Indicate temporary and permanent work files and tables | |
| 538 | Show the sort and merge processes | |
| 539 | Show the transformation programs | |
| 540 | Show the error rejection files and error reports | |
| 541 | Show the load files and load utilities | |
| 542 | Design the ETL programs | 528 |
| 543 | Design three sets of ETL programs | |
| 544 | Design the initial load programs | |
| 545 | Design the historical load programs | |
| 546 | Design the incremental load programs | |
| 547 | Modularize the ETL programs | |
| 548 | Translate the transformation specifications into programming specifications | 547 |
| 549 | Set up the ETL staging area | 528 |
| 550 | Determine whether and how to distribute the ETL process | |
| 551 | Set up the ETL server | |
| 552 | Allocate space for temporary and permanent work files and tables | 551 |
| 553 | Create program libraries | 551 |
| 554 | Establish program-versioning procedures | 551 |
| 555 | Step 10: Meta Data Repository Design | |
| 556 | Design the meta data repository database | 414 |
| 557 | Review the logical meta model for the meta data repository | |
| 558 | Design the meta data repository database (entity-relationship or object-oriented) | |
| 559 | Draw the physical meta model diagram (entity-relationship or object-oriented) | |
| 560 | Map the physical meta model to the logical meta model | |
| 561 | Create the DDL for the meta data repository database | |
| 562 | Create the DCL for the meta data repository database | |
| 563 | Design backup and recovery procedures | |
| 564 | Design versioning and archival procedures | |
| 565 | Install and test the meta data repository product | 414 |
| 566 | Compile a list of meta data repository products and vendors | |
| 567 | Compare the meta data repository products to the meta data repository requirements | |
| 568 | Create a scorecard for each evaluated meta data repository product | |
| 569 | Create a scorecard for each evaluated meta data repository vendor | |
| 570 | Narrow the list of meta data repository products and vendors to a short list | 568, 569 |
| 571 | Arrange for meta data repository product demos | |
| 572 | Check the vendors' client references | |
| 573 | License the meta data repository product | 572 |
| 574 | Install and test the meta data repository product | 573 |
| 575 | Design the meta data migration process | 556, 565 |
| 576 | Analyze all sources for extracting business meta data | |
| 577 | Analyze all sources for extracting technical meta data | |
| 578 | Design the tool interface process | |
| 579 | Design the transformations for the extracted meta data | |
| 580 | Design the load programs for the meta data repository | |
| 581 | Write the programming specifications for the meta data migration process | |
| 582 | Write tool interface programming specifications | 578 |
| 583 | Write transformation programming specifications | 579 |
| 584 | Write meta data repository load programming specifications | 580 |
| 585 | Design the meta data application | 556 |
| 586 | Design the meta data repository report programs | |
| 587 | Design the media for displaying meta data ad hoc query results | |
| 588 | Design the access interface process | |
| 589 | Design the context-sensitive online help function | |
| 590 | Write the programming specifications for the meta data application | |
| 591 | Write report programming specifications | 586 |
| 592 | Write query script specifications | 587 |
| 593 | Write access interface programming specifications | 588 |
| 594 | Write online help function programming specifications | 589 |
| 595 | Step 11: Extract/Transform/Load Development | |
| 596 | Build and unit test the ETL process | 542, 549 |
| 597 | Code the ETL programs | |
| 598 | If using an ETL tool, write instructions for the ETL tool modules | |
| 599 | Capture the ETL technical meta data for the meta data repository | |
| 600 | Write code to produce reconciliation totals, quality metrics, and load statistics | |
| 601 | Unit test each individual program module | 597, 600 |
| 602 | If using an ETL tool, unit test each ETL tool module | 598, 600 |
| 603 | Write the scripts to execute the ETL programs and the sort, merge, and load utilities | 601, 602 |
| 604 | Integration or regression test the ETL process | 596 |
| 605 | Create a test plan with test cases for the ETL process | |
| 606 | Create test data for the ETL programs | |
| 607 | Integration or regression test the entire ETL process | 605, 606 |
| 608 | Log the actual test results and document any test issues | 605, 606 |
| 609 | Compare actual test results with expected test results | 605, 606 |
| 610 | Revise the ETL programs (or the instructions for the ETL tool) | 609 |
| 611 | Retest the entire ETL process from beginning to end | 610 |
| 612 | Performance test the ETL process | 604 |
| 613 | Test individual ETL programs and tool modules that read or write to high-volume tables | |
| 614 | Test the parallel execution of ETL programs and tool modules against high-volume tables | |
| 615 | Test the ETL programs and ETL tool modules that perform complicated operations | |
| 616 | Use full-volume data for performance testing | |
| 617 | If using a stress test simulation tool, define test components and run a simulation test | |
| 618 | Quality assurance (QA) test the ETL process | 612 |
| 619 | Move all ETL programs into the QA environment | |
| 620 | QA test the entire ETL process from beginning to end | 619 |
| 621 | Obtain approval from the operations staff to move the ETL process into production | 620 |
| 622 | Acceptance test the ETL process | 612 |
| 623 | Acceptance test the entire ETL process from beginning to end | |
| 624 | Validate all cleansing transformations | |
| 625 | Validate error-handling routines | |
| 626 | Validate reconciliation totals | |
| 627 | Obtain certification for the ETL process from the business representative | 623 |
| 628 | Step 12: Application Development | |
| 629 | Determine the final project requirements | 491 |
| 630 | Review the results of the prototype | |
| 631 | Review the prototyping programs and scripts | |
| 632 | Review the change-control document | |
| 633 | Review the issues log | |
| 634 | Review existing and mock-up report layouts | |
| 635 | Review existing spreadsheets | |
| 636 | Review the latest version of the application requirements document | |
| 637 | Agree on the final project requirements | |
| 638 | Update the application requirements document to reflect any changes | 637 |
| 639 | Design the application programs | 491 |
| 640 | Design the final reports | |
| 641 | Design the final queries | |
| 642 | Design the front-end interface (GUI, Web portal) | |
| 643 | Design the online help function | |
| 644 | Write programming specifications | |
| 645 | Write report programming specifications | 640 |
| 646 | Write query script specifications | 641 |
| 647 | Write front-end interface programming specifications | 642 |
| 648 | Write online help function programming specifications | 643 |
| 649 | Create a test plan with test cases and a test log | 645, 646 |
| 650 | Build and unit test the application programs | 639 |
| 651 | Create sample test data | |
| 652 | Load the development databases with sample test data | |
| 653 | Rewrite or enhance prototyping programs and scripts | |
| 654 | Code the final report programs | |
| 655 | Code the final query scripts | |
| 656 | Code the final front-end interface programs | |
| 657 | Code the online help function programs | |
| 658 | Unit test each individual program module | 654, 655, 656, 657 |
| 659 | Test the application programs | 650 |
| 660 | Integration or regression test all programs and scripts from beginning to end | |
| 661 | Integration or regression test report programs | |
| 662 | Integration or regression test query scripts | |
| 663 | Integration or regression test front-end interface programs | |
| 664 | Integration or regression test online help function programs | |
| 665 | Log the actual test results and document any test issues | |
| 666 | Compare actual test results with expected test results | |
| 667 | Revise the application programs and scripts | 666 |
| 668 | Retest the application programs and scripts from beginning to end | 667 |
| 669 | Performance test complex high-volume programs | 668 |
| 670 | Use full-volume data for performance testing | 668 |
| 671 | If using a stress test simulation tool, define test components and run a simulation test | 668 |
| 672 | Move databases, programs, and scripts into the QA environment | 669 |
| 673 | QA test the entire application from beginning to end | 672 |
| 674 | Obtain approval from the operations staff to move the application programs into production | 673 |
| 675 | Acceptance test the entire application from beginning to end | 669 |
| 676 | Obtain certification for the application from the business representative | 675 |
| 677 | Provide data access and analysis training | 650 |
| 678 | Identify help desk staff to be trained | |
| 679 | Identify "power users" or other business liaison personnel to be trained | |
| 680 | Identify business people to be trained | |
| 681 | Create training materials | |
| 682 | Create presentation slides and instructor notes | |
| 683 | Create student workbooks with exercises | 682 |
| 684 | Create exercise solutions and other pertinent handouts | 683 |
| 685 | Schedule training sessions | 684 |
| 686 | Conduct training sessions | 685 |
| 687 | Measure training effectiveness | 686 |
| 688 | Step 13: Data Mining | |
| 689 | State the business problem | 491 |
| 690 | Define the business problem | |
| 691 | Obtain commitment for a data mining solution | 690 |
| 692 | Set realistic expectations for the data mining tool | 691 |
| 693 | Identify preliminary algorithms relevant to the business problem | 691 |
| 694 | Collect the data | 689 |
| 695 | Identify available data sources (operational as well as BI) | |
| 696 | Extract pertinent data from various internal data sources | |
| 697 | Acquire pertinent data from external data sources | |
| 698 | Consolidate and cleanse the data | 689 |
| 699 | Merge data from various internal data sources | |
| 700 | Match and merge internal data with external data | |
| 701 | Review the structure of the merged data | |
| 702 | Select a sample of data for each analytical data model | |
| 703 | Select related meta data from the meta data repository | |
| 704 | Review the data domains and measure the quality and reasonability of data values | 702 |
| 705 | Validate domain reasonability across active variables | 702 |
| 706 | Prepare the data | 702 |
| 707 | Review the frequency distribution of categorical variables | |
| 708 | Review maximum, minimum, mean, mode, and median for quantitative variables | |
| 709 | Use statistical distribution parameters to filter noise in the data | |
| 710 | Eliminate or replace variables with missing values | |
| 711 | Convert data formats to suit the particular data mining algorithm used | |
| 712 | Derive new variables from original input data | |
| 713 | Consolidate customers by assigning a household number to related customers | |
| 714 | Relate customers with products and services | |
| 715 | Apply data reduction | |
| 716 | Apply data mining transformation techniques | 715 |
| 717 | Apply "discretization" technique | |
| 718 | Apply "one-of-N" technique | |
| 719 | Build the analytical data model | 694, 698, 706 |
| 720 | Create the analytical (informational) data model | |
| 721 | Select data mining operations with the appropriate algorithms | |
| 722 | Test accuracy using confusion matrices and input sensitivity analyses | |
| 723 | Repeat prior steps to train and retrain the model | 722 |
| 724 | Interpret the data mining results | 719 |
| 725 | Review the data mining results | |
| 726 | Look for results that are interesting, valid, and actionable | |
| 727 | Present the new findings using visualization technology | 726 |
| 728 | Formulate ways in which the new information can be exploited | 726 |
| 729 | Perform external validation of the results | 719 |
| 730 | Compare data mining results to published industry statistics | |
| 731 | Validate the selection of your variables and time frame against the variables and time frame of the industry statistics | |
| 732 | Identify the variations between your analysis results and the industry statistics | |
| 733 | Determine the reasons for the variations | |
| 734 | Monitor the analytical data model over time | 724, 729 |
| 735 | Keep validating your analytical data model against industry statistics at regular time intervals | |
| 736 | When industry statistics change, change your analytical data model and retrain it | 735 |
| 737 | Research the data mining capabilities of your competitors | |
| 738 | Monitor your competitors' market share and adjust your model | |
| 739 | Step 14: Meta Data Repository Development | |
| 740 | Build the meta data repository database | 575, 585 |
| 741 | Run the DDL to create the physical meta data repository database structures | |
| 742 | Run the DCL to grant CRUD authority on the meta data repository database structures | |
| 743 | If licensing a meta data repository product, set up CRUD authority on the meta data repository product | |
| 744 | Test all meta data repository product components, especially the meta data repository database | |
| 745 | Build and unit test the meta data migration process | 740 |
| 746 | Code the tool interface programs or use the export facility of the various tools | |
| 747 | Code the meta data transformation programs | |
| 748 | Code the meta data load programs or use the import facility of the meta data repository product or the DBMS load utility | |
| 749 | Code the meta data programs that will run during ETL | |
| 750 | Code the meta data programs to capture load statistics | |
| 751 | Code the meta data programs to capture reconciliation totals | |
| 752 | Code the meta data programs to capture data-cleansing metrics | |
| 753 | Code the meta data programs to capture rejection counts and reasons for rejections | |
| 754 | Unit test the meta data migration programs | |
| 755 | Unit test the tool interface programs | 746 |
| 756 | Unit test the meta data transformation programs | 747 |
| 757 | Unit the test meta data load programs | 748 |
| 758 | Unit test the meta data programs that will run during the ETL process | 749 |
| 759 | Build and unit test the meta data application | 740 |
| 760 | Code the access interface programs | |
| 761 | Code the meta data report programs | |
| 762 | Code the meta data query scripts | |
| 763 | Code the meta data repository online help function programs | |
| 764 | Unit test the meta data application programs (or meta data repository product modules) | |
| 765 | Unit test the access interface programs | 760 |
| 766 | Unit test the meta data report programs | 761 |
| 767 | Unit test the meta data query scripts | 762 |
| 768 | Unit test the meta data repository online help function programs | 763 |
| 769 | Test the meta data repository programs or product functions | 745, 759 |
| 770 | Create a test plan with test cases | |
| 771 | Create test cases for the meta data migration process | |
| 772 | Create test cases for the meta data repository application programs or product modules | |
| 773 | Create test cases for the meta data programs that run during the ETL process | |
| 774 | Create test data for meta data repository testing | |
| 775 | Create test data for the meta data migration process | |
| 776 | Create test data for the meta data repository application or product modules | |
| 777 | Create test data for the meta data programs that run during the ETL process | |
| 778 | Integration or regression test the meta data repository | 770, 774 |
| 779 | Integration or regression test the meta data migration process | |
| 780 | Integration or regression test the meta data repository application or product modules | |
| 781 | Integration or regression test the meta data programs that run during the ETL process | |
| 782 | Log the actual test results and document any test issues | 778 |
| 783 | Compare actual test results with expected test results | 778 |
| 784 | Revise the meta data repository programs | 783 |
| 785 | Retest the meta data repository programs from beginning to end | 784 |
| 786 | Conduct QA testing with operations staff | 785 |
| 787 | Conduct acceptance testing with the subject matter expert and the business representative | 785 |
| 788 | Prepare the meta data repository for production | 769 |
| 789 | Install and test the server platform for the production meta data repository | |
| 790 | Create DDL and DCL for the production meta data repository database | |
| 791 | Write operating procedures for the operations staff for running the meta data repository reports | |
| 792 | Write a reference guide for the help desk staff and the business people | |
| 793 | Develop performance monitoring and tuning procedures for the meta data repository database | |
| 794 | Develop meta data repository usage monitoring procedures | |
| 795 | Provide meta data repository training | 769 |
| 796 | Identify help desk staff to be trained | |
| 797 | Identify "power users" to be trained | |
| 798 | Identify business people to be trained | |
| 799 | Create meta data repository training materials | |
| 800 | Create meta data repository presentation slides and instructor notes | |
| 801 | Create meta data repository student workbooks with exercises | 800 |
| 802 | Create exercise solutions and other pertinent handouts | 801 |
| 803 | Schedule meta data repository training sessions | 802 |
| 804 | Conduct meta data repository training sessions | 803 |
| 805 | Measure meta data repository training effectiveness | 804 |
| 806 | Step 15: Implementation | |
| 807 | Plan the implementation | 618, 622, 659, 719, 788 |
| 808 | Select an implementation (rollout) strategy | |
| 809 | Set the implementation date | |
| 810 | Determine the number of business people for the initial rollout | |
| 811 | Schedule the necessary resources to participate in implementation activities | |
| 812 | Schedule the functions to be rolled out | |
| 813 | Prepare for organizational impact | |
| 814 | Set up the production environment | 807 |
| 815 | Set up the production ETL program library | |
| 816 | Set up the production application program library | |
| 817 | Set up the production meta data repository program library | |
| 818 | Create the production BI target databases | |
| 819 | Create the production meta data repository database | |
| 820 | Grant appropriate authority on the production BI target databases | 818 |
| 821 | Grant appropriate authority on the production meta data repository database | 819 |
| 822 | Grant appropriate authority on all production program libraries | 815, 816, 817 |
| 823 | Write ETL operating procedures for operations staff | |
| 824 | Write application reference guides for help desk staff and the business people | |
| 825 | Implement production security levels for all BI application components | |
| 826 | Install all the BI application components | 814 |
| 827 | Move ETL programs into the production ETL program library | |
| 828 | Move initial load programs | |
| 829 | Move historical load programs | |
| 830 | Move incremental load programs | |
| 831 | Move application programs into the production application program library | |
| 832 | Move report programs | |
| 833 | Move query scripts | |
| 834 | Move front-end interface programs | |
| 835 | Move online help function programs | |
| 836 | Move meta data repository programs into the production meta data repository program library | |
| 837 | Move meta data migration programs | |
| 838 | Move meta data application programs or product modules | |
| 839 | Set up the production schedule | 814 |
| 840 | Set up the ETL process on the job scheduler | |
| 841 | Add to the job scheduler the meta data programs that run during the ETL process | |
| 842 | Set up on the job scheduler the regularly scheduled application report programs | |
| 843 | Set up on the job scheduler the regularly scheduled meta data repository programs | |
| 844 | Set up the meta data migration process | |
| 845 | Set up the meta data repository application | |
| 846 | Load the production databases | 826, 839 |
| 847 | Run the initial load process | |
| 848 | Run the historical load process | |
| 849 | Run the meta data migration process | |
| 850 | Prepare for ongoing support | 846 |
| 851 | Establish a schedule for on-call emergency support | |
| 852 | Schedule database maintenance activities for the production databases | |
| 853 | Schedule database backups | |
| 854 | Schedule disaster recovery testing | |
| 855 | Schedule database reorganizations | |
| 856 | Schedule database monitoring activities for the production databases | |
| 857 | Schedule performance monitoring activities | |
| 858 | Schedule growth monitoring activities | |
| 859 | Schedule usage monitoring activities | |
| 860 | Schedule data quality monitoring activities for the BI target databases | |
| 861 | Schedule activities for reviewing meta data metrics | |
| 862 | Schedule quality spot checks | |
| 863 | Develop or review capacity plans for the BI platform | |
| 864 | Develop capacity plans for processors | |
| 865 | Develop capacity plans for disk storage | |
| 866 | Develop capacity plans for network components (including bandwidth) | |
| 867 | Start production processing (go live) | 850 |
| 868 | Step 16: Release Evaluation | |
| 869 | Prepare for the post-implementation review | 850FS+30 days |
| 870 | Review budget expenditures | |
| 871 | Review the original project plan and final schedule | |
| 872 | Review the estimated and actual task completion times | |
| 873 | Review the issues log (resolved and unresolved issues) | |
| 874 | Review the change-control procedure and scope changes | |
| 875 | Review unfulfilled requirements (dropped from scope) | |
| 876 | Review the effectiveness of the development approach | |
| 877 | Review the effectiveness of the team structure | |
| 878 | Review the effectiveness of the organizational placement | |
| 879 | Review the existing infrastructure (technical and nontechnical) | |
| 880 | Identify missing infrastructure pieces (technical and nontechnical) | 879 |
| 881 | Assess the performance of the BI application | |
| 882 | Review the effectiveness of training | |
| 883 | Review the implementation (rollout) strategy | |
| 884 | Review the effectiveness of the release concept | |
| 885 | Organize the post-implementation review meeting | 850FS+30 days |
| 886 | Create the preliminary post-implementation review agenda | |
| 887 | List date, time, and place | |
| 888 | List invited attendees | |
| 889 | List topics for discussion | |
| 890 | List and assign topics for research | |
| 891 | List questions to be discussed and answered | |
| 892 | Solicit additional topics and questions from attendees | |
| 893 | Send out the preliminary agenda to attendees | |
| 894 | Schedule the meeting at an off-site location | |
| 895 | Arrange facilitation by a third party | |
| 896 | Arrange for a third-party scribe to take notes during the meeting | |
| 897 | Revise and send the final meeting agenda | 893 |
| 898 | Send out documentation to be discussed during the review | 897 |
| 899 | Conduct the post-implementation review meeting | 869, 885 |
| 900 | Introduce the attendees | |
| 901 | Explain the rules for the facilitated session | |
| 902 | Discuss each item on the agenda | |
| 903 | Document discussions, suggestions, resolutions | |
| 904 | Document action items | |
| 905 | Assign action items | |
| 906 | Establish completion or response date for each action item | |
| 907 | Follow up on the post-implementation review | 899 |
| 908 | Document unfulfilled requirements to be bundled with the next BI release | |
| 909 | Write the meeting minutes | |
| 910 | Publish the meeting minutes | |
| 911 | Work on assigned action items | |
| 912 | Monitor the work performed on action items | |
| 913 | Document the action item results | 911 |
| 914 | Publish the action item results | 913 |
| 915 | Implement nontechnical infrastructure improvements | |
| 916 | Improve the development approach | |
| 917 | Improve use of the development methodology | |
| 918 | Improve processes and procedures | |
| 919 | Improve guidelines | |
| 920 | Improve standards | |
