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Agile Data Warehousing Project Management

Business Intelligence Systems Using Scrum

By
  • 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.

Audience
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

ISBN: 978-0-12-396463-2

Reviews

  • "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


Contents

  • Part I: A Generic Agile Method
    Chapter 1. Why Agile?
       The "Disappointment Cycle" of Many Traditional Projects
       Agile’s 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
       Non-Standard Iterations

    Chapter 3. Project Management Lite
       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

    Chapter 4. User Stories for Business Intelligence Applications
       Traditional Requirement Management And Its Discontents
       Agile’s Idea of "User Stories"
       User Story Definition Fundamentals
       Generic Frameworks for Writing Good User Stories

    Part II. Adapting Agile to Data Warehousing
    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

    Chapter 6. Agile Estimation for DW/BI
       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 Team’s One, True Metric

    Chapter 7. Further Adaptations for Agile Data Warehousing
       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
       Communicating Progress
       The Agile Data Warehousing Manifesto

    Chapter 8. Starting and Scaling Agile Warehousing Teams
       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?

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