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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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