Agile Data Warehousing Project Management
Business Intelligence Systems Using ScrumBy
- Ralph Hughes, former DW/BI practice manager for a leading global systems integrator, has led numerous BI programs and projects for Fortune 500 companies in aerospace, government, telecom, and pharmaceuticals.
You have to make sense of enormous amounts of data, and while the notion of agile data warehousing might sound tricky, it can yield as much as a 3-to-1 speed advantage while cutting project costs in half. Bring this highly effective technique to your organization with the wisdom of agile data warehousing expert Ralph Hughes.
Agile Data Warehousing Project Management will give you a thorough introduction to the method as you would practice it in the project room to build a serious data mart. Regardless of where you are today, this step-by-step implementation guide will prepare you to join or even lead a team in visualizing, building, and validating a single component to an enterprise data warehouse.
data warehousing professionals including architects, designers, data modelers, testers, database administrators, programmers, developers, scrum masters and project managers as well as IT managers, directors, and VPs
Paperback, 366 Pages
Published: September 2012
Imprint: Morgan Kaufmann
"Hughes first began working with agile data warehousing in 1996 and received skeptical reactions up until at least six years ago. Having stuck with this approach throughout, he is now receiving a more and more favorable reception and here uses his expertise to deliver a thorough implementation guide."-- Reference and Research Book News, December 2012
Part I: A Generic Agile Method
Chapter 1. Why Agile?
The "Disappointment Cycle" of Many Traditional Projects
Agiles Iterative and Incremental Delivery Alternative
Agile as Applied to Data Warehousing
Not A Revolution, Just An Impressive Evolution
Where to Be Cautious with Agile DW/BI
Chapter 2. Agile Development in a Nutshell
Minimal Facilities Required
Product Owners and Scrum Masters
Three Cycles of a Generic Scrum Iteration
Iteration Phase 1: Story Conferences
Iteration Phase 2: Task Planning
Iteration Phase 3: Development Phase
Iteration Phase 4: User Demos
Iteration Phase 5: Sprint Retrospectives
Chapter 3. Project Management LiteChapter 4. User Stories for Business Intelligence Applications
Highly-Transparent Task Boards
Burndown Charts Reveal Progress and Velocity
Dealing with Tech Debt and Scope Creep
Should You Extend a Sprint?
Overcoming Geographical Barriers
Traditional Requirement Management And Its Discontents
Agiles Idea of "User Stories"
User Story Definition Fundamentals
Generic Frameworks for Writing Good User Stories
Part II. Adapting Agile to Data WarehousingChapter 6. Agile Estimation for DW/BI
Chapter 5. Developer Stories for Data Integration Projects
Warehousing Architecture and "Developer Stories"
Better Warehouse User Epic Decomposition
Further Project Partitioning Strategies
Answering the Nay Sayers
The Damage Done By Bad Estimation
Why Waterfall Estimate Poorly
Two Approaches: Story Points Versus Ideal Time
Agile Estimation Techniques
Agile Release and Project Planning
Estimation Quality As The Teams One, True Metric
Chapter 7. Further Adaptations for Agile Data WarehousingChapter 8. Starting and Scaling Agile Warehousing Teams
Additional Roles Required
Scrumban: Pipelined Delivery Yields a Sustainable Pace
Tiered Data Models for Managing Dependencies
Reference Models and BOE Cards
Balancing Agility and Perfection in Design
Demo Data Churn and Its Solutions
The Agile Data Warehousing Manifesto
Six Stages in Nurturing A World-Class Team
Stage 0: Time-Boxed Iterations & Agile Estimation
Stage 1: Release Planning
Stage 2: Pipelined Delivery Squads
Stage 3: Requirements Decomposition
Stage 4: Reference Models & Test-Led Development
Stage 5: Continuous Integration Testing
Managing Frustration with Early Iterations
Managing Adversity within the Larger Organization
Picking velocities for the first sprint
Scaling Scrum Teams
SideBar: Items for Iteration -1 and 0
Part III. Retrospective
Chapter 9. Faster, Better, Cheaper
What is Agile?
What is Agile Data Warehousing?
Where Does Agile Get All Its Speed?
Why does Agile Work So Well?
Answering the What Abouts?