Key Features
* Provides a thorough grounding on the mechanics of Scrum as well as practical advice on keeping your team on track
* Includes strategies for getting accurate and actionable requirements from a team’s business partner
* Revolutionary estimating techniques that make forecasting labor far more understandable and accurate
* Demonstrates a blends of Agile methods to simplify team management and synchronize inputs across IT specialties
* Enables you and your teams to start simple and progress steadily to world-class performance levels
Description
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.
Readership
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
Agile Data Warehousing Project Management, 1st Edition
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?
