Agile Data Warehousing Project Management. Business Intelligence Systems Using Scrum

Description

Chapter-1-What-Is-Agile-Data-Warehousing-_2013_Agile-Data-Warehousing-Project-Management.pdf......Page 0 List of Figures......Page 4 List of Tables......Page 6 Anchor 2......Page 7 Preface......Page 8 Intended audience......Page 9 Parts and chapters of the book......Page 10 Invitation to join the agile warehousing community......Page 11 Starting a scrum team......Page 305 Stage 1: time box and story points......Page 307 Stage 3: developer stories and current estimates......Page 308 Stage 5: automatic and continuous integration testing......Page 309 Scaling agile......Page 311 Application complexity......Page 312 Compliance requirements......Page 313 Organizational culture......Page 314 Organizational distribution......Page 315 Coordinating multiple scrum teams......Page 316 Coordinating through scrum of scrums......Page 317 Matching milestones......Page 320 Balancing work between teams with earned-value reporting......Page 321 What is agile data warehousing?......Page 327 Communicating success......Page 330 Handoff quality......Page 331 Defects by iteration......Page 332 Burn-up charts......Page 333 Cross-method comparison projects......Page 335 Cycle times and story point distribution......Page 336 A glimpse at a pull-based approach......Page 337 Kanban advantages......Page 342 2 Can we really define workable units without keeping our estimating skills sharp?......Page 343 6 Aren’t there other reasons for having iterations besides estimating?......Page 344 Stages of scrumban......Page 345 Summary......Page 346 8 Adapting Agile for Data Warehousing......Page 253 The context as development begins......Page 254 Data warehousing/business intelligence-specific team roles......Page 257 Project architect......Page 258 Data architect......Page 264 Systems analyst......Page 266 Systems tester......Page 267 The leadership subteam......Page 268 Resident and visiting “resources”......Page 269 New agile characteristics required......Page 270 Avoiding data churn within sprints......Page 271 Pipeline delivery for a sustainable pace......Page 275 New meaning for Iteration 0 and Iteration −1......Page 278 Pipeline requires two-step user demos......Page 280 Keeping pipelines from delaying defect correction......Page 281 Resolving pipelining’s task board issues......Page 282 Pipelining as a buffer-based process......Page 285 Pipelining is controversial......Page 286 Continuous and automated integration testing......Page 287 High quality is a necessity......Page 289 Agile warehousing testing requirements......Page 290 Nominal data testing......Page 291 Missing data......Page 292 Multiple time points......Page 293 The need for automation......Page 294 Requirements for a warehouse test engine......Page 295 Automated testing for front-end applications......Page 296 Evolutionary target schemas—the hard way......Page 299 Summary......Page 304 7 Estimating and Segmenting Projects......Page 211 Failure of traditional estimation techniques......Page 212 Traditional estimating strategies......Page 213 Insufficient feedback......Page 215 Few reality checks......Page 216 Criteria for a better estimating approach......Page 217 Estimating within the iteration......Page 219 Estimating the overall project......Page 222 Quick story points via “estimation poker”......Page 223 Story points and ideal time......Page 227 Ideal time defined......Page 228 The advantage of story points......Page 229 Estimation accuracy as an indicator of team performance......Page 231 Value pointing user stories......Page 232 Packaging stories into iterations and project plans......Page 233 Criteria for better story prioritization......Page 235 Segmenting projects into business-valued releases......Page 236 The data architectural process supporting project segmentation......Page 237 Dimensional model......Page 238 Categorized service model......Page 239 Project segmentation technique 1: dividing the star schema......Page 242 Project segmentation technique 2: dividing the tiered integration model......Page 244 Project segmentation technique 3: grouping waypoints on the categorized services model......Page 247 Embracing rework when it pays......Page 250 Summary......Page 251 6 Developer Stories for Data Integration......Page 179 Why developer stories are needed......Page 180 Introducing the “developer story”......Page 182 Format of the developer story......Page 183 Developer stories in the agile requirements management scheme......Page 184 Agile purists do not like developer stories......Page 185 Initial developer story workshops......Page 186 Developers workshop within software engineering cycles......Page 188 Data warehousing/business intelligence reference data architecture......Page 189 Forming backlogs with developer stories......Page 191 Demonstrable......Page 194 Business valued......Page 196 Refinable......Page 198 Secondary techniques when developer stories are still too large......Page 199 Decomposition by rows......Page 200 Decomposition by column sets......Page 202 Decomposition by column type......Page 204 Decomposition by tables......Page 205 Theoretical advantages of “small”......Page 207 Summary......Page 209 5 Deriving Initial Project Backlogs......Page 147 Value of the initial backlog......Page 148 Sketch of the sample project......Page 149 Fitting initial backlog work into a release cycle......Page 150 The handoff between enterprise and project architects......Page 152 Key observations......Page 156 User role modeling results......Page 158 Carla in corp strategy......Page 159 Franklin in finance......Page 160 An example of an initial backlog interview......Page 161 Framing the project......Page 166 Finance is upstream......Page 168 Customer segmentation......Page 169 Sales channel......Page 170 Unit reporting......Page 171 Product usage......Page 172 Sometimes a lengthy process......Page 174 Detecting backlog components......Page 175 Prioritizing stories......Page 177 Summary......Page 178 4 Authoring Better User Stories......Page 122 Traditional requirements gathering and its discontents......Page 123 A step in the right direction......Page 125 Agile’s idea of “user stories”......Page 127 Advantages of user stories......Page 128 Identifying rather than documenting the requirements......Page 129 User story definition fundamentals......Page 130 Quick test for actionable user stories......Page 131 How small is small?......Page 132 Epics, themes, and stories......Page 133 Common techniques for writing good user stories......Page 135 Keep story writing simple......Page 137 Use stories to manage uncertainty......Page 138 Focus on understanding “who”......Page 139 Focus on understanding “what”......Page 140 Focus on understanding “why”......Page 142 Be wary of the remaining w’s......Page 144 Add acceptance criteria to the story-writing conversations......Page 145 Summary......Page 146 3 Streamlining Project Management......Page 89 Highly transparent task boards......Page 90 Task boards amplify project quality......Page 92 Task boards naturally integrate team efforts......Page 93 Scrum masters must monitor the task board......Page 94 Burndown charts reveal the team aggregate progress......Page 95 Detecting trouble with burndown charts......Page 97 Developers are not the burndown chart’s victims......Page 99 Calculating velocity from burndown charts......Page 100 Setting capacity when the team delivers early......Page 102 Managing tech debt......Page 103 Managing miditeration scope creep......Page 104 Diagnosing problems with burndown chart patterns......Page 105 An early hill to climb......Page 106 Shallow glide paths......Page 107 Persistent inflation......Page 108 Extending iterations is generally a bad idea......Page 110 Two instances where a changing time box might help......Page 111 Should teams track actual hours during a sprint?......Page 112 Eliminating hour estimation altogether......Page 113 Managing geographically distributed teams......Page 114 Visualize the problem in terms of communication......Page 116 Invest in a solid esprit de corp......Page 117 Invest in high-quality telepresence equipment......Page 118 Summary......Page 120 2 Iterative Development in a Nutshell......Page 42 Starter concepts......Page 43 Three nested cycles......Page 44 The release cycle......Page 45 Development and daily cycles......Page 48 Shippable code and the definition of done......Page 49 Time-boxed development......Page 50 Product owners and scrum masters......Page 51 Product owner......Page 52 Developers as “generalizing specialists”......Page 53 Improved role for the project manager......Page 54 Might a project manager serve as a scrum master?......Page 55 User stories and backlogs......Page 56 Estimating user stories in story points......Page 57 Iteration phase 1: story conferences......Page 59 Basis of estimate cards to escape repeating hard thinking......Page 61 Task planning doublechecks story planning......Page 63 Iteration phase 3: development phase......Page 64 Self-organization......Page 65 Daily scrums......Page 66 Accelerated programming......Page 68 Test-driven development......Page 71 Architectural compliance and “tech debt”......Page 72 Iteration phase 4: user demo......Page 74 Iteration phase 5: sprint retrospectives......Page 76 Retrospectives are vital......Page 79 Close collaboration is essential......Page 81 Selecting the optimal iteration length......Page 82 Nonstandard sprints......Page 83 Architectural sprints......Page 84 “Hardening” sprints......Page 85 Distant history......Page 86 Scrum emerges......Page 87 Summary......Page 88 1 What Is Agile Data Warehousing?......Page 12 A quick peek at an agile method......Page 13 The “disappointment cycle” of many traditional projects......Page 17 The waterfall method was, in fact, a mistake......Page 21 Agile’s iterative and incremental delivery alternative......Page 23 Business centric......Page 24 80-20 Specifications......Page 25 Fail fast and fix quickly......Page 26 Agile methods provide better results......Page 27 Data warehousing entails a “breadth of complexity”......Page 28 Adapted scrum handles the breadth of data warehousing well......Page 29 Managing data warehousing’s “depth of complexity”......Page 31 Guide to this book and other materials......Page 35 Simplified treatment of data architecture for book 1......Page 37 Companion web site......Page 38 Where to be cautious with agile data warehousing......Page 39 Summary......Page 40