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by Ralph Hughes
Agile Data Warehousing Project Management
Cover image
Title page
Table of Contents
Copyright
List of Figures
List of Tables
Preface
Answering the skeptics
Intended audience
Parts and chapters of the book
Invitation to join the agile warehousing community
Author’s Bio
Part 1: An Introduction to Iterative Development
Chapter 1. What Is Agile Data Warehousing?
A quick peek at an agile method
The “disappointment cycle” of many traditional projects
The waterfall method was, in fact, a mistake
Agile’s iterative and incremental delivery alternative
Agile for data warehousing
Where to be cautious with agile data warehousing
Summary
Chapter 2. Iterative Development in a Nutshell
Starter concepts
Iteration phase 1: story conferences
Iteration phase 2: task planning
Iteration phase 3: development phase
Iteration phase 4: user demo
Iteration phase 5: sprint retrospectives
Close collaboration is essential
Selecting the optimal iteration length
Nonstandard sprints
Where did scrum come from?
Summary
Chapter 3. Streamlining Project Management
Highly transparent task boards
Burndown charts reveal the team aggregate progress
Calculating velocity from burndown charts
Common variations on burndown charts
Managing miditeration scope creep
Diagnosing problems with burndown chart patterns
Should you extend a sprint if running late?
Should teams track actual hours during a sprint?
Managing geographically distributed teams
Summary
Part 2: Defining Data Warehousing Projects for Iterative Development
Chapter 4. Authoring Better User Stories
Traditional requirements gathering and its discontents
Agile’s idea of “user stories”
User story definition fundamentals
Common techniques for writing good user stories
Summary
Chapter 5. Deriving Initial Project Backlogs
Value of the initial backlog
Sketch of the sample project
Fitting initial backlog work into a release cycle
The handoff between enterprise and project architects
User role modeling results
Key persona definitions
Carla in corp strategy
An example of an initial backlog interview
Finance is upstream
Observations regarding initial backlog sessions
Summary
Chapter 6. Developer Stories for Data Integration
Why developer stories are needed
Introducing the “developer story”
Developer stories in the agile requirements management scheme
Agile purists do not like developer stories
Initial developer story workshops
Data warehousing/business intelligence reference data architecture
Forming backlogs with developer stories
Evaluating good developer stories: DILBERT’S test
Secondary techniques when developer stories are still too large
Summary
Chapter 7. Estimating and Segmenting Projects
Failure of traditional estimation techniques
An agile estimation approach
Quick story points via “estimation poker”
Story points and ideal time
Estimation accuracy as an indicator of team performance
Value pointing user stories
Packaging stories into iterations and project plans
Segmenting projects into business-valued releases
Project segmentation technique 1: dividing the star schema
Project segmentation technique 2: dividing the tiered integration model
Project segmentation technique 3: grouping waypoints on the categorized services model
Embracing rework when it pays
Summary
Part 3: Adapting Iterative Development for Data Warehousing Projects
Chapter 8. Adapting Agile for Data Warehousing
The context as development begins
Data warehousing/business intelligence-specific team roles
Avoiding data churn within sprints
Pipeline delivery for a sustainable pace
Continuous and automated integration testing
Evolutionary target schemas—the hard way
Summary
Chapter 9. Starting and Scaling Agile Data Warehousing
Starting a scrum team
Scaling agile
What is agile data warehousing?
Communicating success
Moving to pull-driven systems
Summary
References
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
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List of Tables
List of Figures
Figure 1.1
Structure of Scrum development iteration and duration of its phases.
Figure 1.2
All too often, waterfall methods yield to the project “disappointment cycle.”
Figure 1.3
Complex data integration requires increasing modifications to generic agile methods.
Figure 1.4
An agile perspective on the competing risks for DWBI projects.
Figure 1.5
High-level structure of this book and its companion volume.
Figure 2.1
Typical waterfall method with key project management artifacts indicated.
Figure 2.2
Three cycles of generic Scrum.
Figure 2.3
Estimating story points.
Figure 2.4
Identifying an iteration’s “commit line” during sprint planning.
Figure 2.5
Common project room pattern for accelerated coding—Part 1.
Figure 2.6
Common project room pattern for accelerated coding—Part 2.
Figure 2.7
Handling accepted and rejected user stories.
Figure 2.8
Identifying areas of improvement during the sprint retrospective (Part 1).
Figure 2.9
Identifying areas of improvement during the sprint retrospective (Part 2).
Figure 3.1
Simplified representation of a Scrum task board in midsprint.
Figure 3.2
Typical agile burndown chart with a perfect line.
Figure 3.3
Midsprint burndown chart showing trouble.
Figure 3.4
End-of-sprint burndown chart showing a delivery gap.
Figure 3.5
Calculating labor-hour velocity using a burndown chart.
Figure 3.6
Burndown chart showing tech debt and scope creep.
Figure 3.7
Calculating labor-hour velocity for sprints with scope creep.
Figure 3.8
Common burndown patterns: The shallow glide.
Figure 3.9
Worrisome burndown chart pattern: Persistent inflation.
Figure 3.10
Impact of distributing teammates is measurable.
Figure 3.11
Geographical distribution’s impact on team communications.
Figure 3.12
Good remote-presence tools help distributed teams.
Figure 4.1
Agile’s three levels of requirements management.
Figure 4.2
Sample vision box for a revenue assurance project.
Figure 4.3
Sample product board.
Figure 4.4
Three levels of user goals.
Figure 5.1
Fitting early iterations into a typical PMO release cycle.
Figure 5.2
Starter business target model for sample project.
Figure 5.3
User role models for the sample data mart project.
Figure 5.4
Business target model at end of sample project’s initial backlog interview.
Figure 5.5
General functions of backlog items in data integration projects.
Figure 6.1
Most data integration stories cannot be delivered with only one sprint.
Figure 6.2
Hierarchy for agile project definition objects for data integration work.
Figure 6.3
Defining work units for an agile data warehousing project.
Figure 6.4
Developer stories derived from the sample user story.
Figure 6.5
Deriving DILBERT’S test from the user story INVEST test.
Figure 6.6
Developer stories by sets of rows.
Figure 6.7
Decomposing developer stories by sets of columns.
Figure 6.8
Different column types found in data warehousing tables.
Figure 6.9
Decomposing developer stories by target tables.
Figure 6.10
Consistent story size improves an agile team’s velocity.
Figure 7.1
Empirically, waterfall estimates are difficult to rely upon.
Figure 7.2
Two units of measure increase accuracy of agile estimates.
Figure 7.3
Typical card deck for agile “estimating poker.”
Figure 7.4
Deriving a current estimate from a project backlog.
Figure 7.5
Sample dimensional model and corresponding star schema.
Figure 7.6
Sample tiered data model for an integration layer (drawn schematically).
Figure 7.7
Front-end project categorized service model.
Figure 7.8
Back-end project categorized service model.
Figure 7.9
Project segmentation lines drawn on a star schema.
Figure 7.10
Project segmentation using a tiered integration data model.
Figure 7.11
Project segmentation lines on a back-end categorized service model.
Figure 8.1
Expanded team roles for agile data warehousing.
Figure 8.2
Data churn wastes agile team productivity.
Figure 8.3
Agile mash-ups that do not work.
Figure 8.4
Pipelined delivery technique.
Figure 8.5
Pipelined work stages across project iterations.
Figure 8.6
Disposition of defects depends on severity.
Figure 8.7
Using buffers to manage both ends of the pipeline.
Figure 8.8
Testing warehouse applications properly requires many data sets.
Figure 8.9
Warehouse test engines must iterate through numerous scenarios.
Figure 8.10
Extending back-end testing to cover most of a warehouse application.
Figure 9.1
Automated testing gives team in an incremental point of view.
Figure 9.2
Enterprise business intelligence architecture balancing framework.
Figure 9.3
Data topology diagram for typical warehouse program with extensively interdependent projects.
Figure 9.4
Specialty-based meta scrums coordinate projects across large programs.
Figure 9.5
Using current estimates for milestone planning between agile DWBI projects.
Figure 9.6
Typical earned-value reporting graph for a waterfall project.
Figure 9.7
Balancing work between teams using earned-value analysis.
Figure 9.8
One answer to “what is agile data warehousing?”
Figure 9.9
Tracking defects by iteration.
Figure 9.10
Burn-up chart showing value points delivered by sprint.
Figure 9.11
Single team’s story point distribution chart by work type.
Figure 9.12
Scrum data warehousing task board adapted for Kanban concepts.
Figure 9.13
Cumulative flow diagram and some of the metrics it provides.
Figure 9.14
Cycle time distribution analysis.
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