Agile Data Warehousing Project Management

Agile Data Warehousing Project Management, 1st Edition

Business Intelligence Systems Using Scrum

Agile Data Warehousing Project Management, 1st Edition,Ralph Hughes,ISBN9780123964632


Morgan Kaufmann




235 X 191

The only step-by-step guide to visualizing, building, and validating an agile enterprise data warehouse

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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


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

Ralph Hughes

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. A certified Scrum Master and a PMI Project Management Professional, he began developing an agile method for data warehouse 15 years ago, and was the first to publish books on the iterative solutions for business intelligence projects. He is a veteran trainer with the world's leading data warehouse institute and has instructed or coached over 1,000 BI professionals worldwide in the discipline of incremental delivery of large data management systems. A frequent keynote speaker at business intelligence and data management events, he serves as a judge on emerging technologies award panels and program advisory committees of advanced technology conferences. He holds BA and MA degrees from Stanford University where he studied computer modeling and econometric forecasting. A co-inventor of Zuzena, the automated testing engine for data warehouses, he serves as Chief Systems Architect for Ceregenics and consults on agile projects internationally.

Affiliations and Expertise

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

Agile Data Warehousing Project Management, 1st Edition

List of Figures

List of Tables


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


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?


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


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


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


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


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


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


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



Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9


Quotes and reviews

"Anyone who has worked on a data warehousing project knows that it can be a monumental undertaking. Agile Data Warehouse (sic) Project Management…offers up an approach that can minimize challenges and improve the chance of successful delivery." --Data and Technology Today blog, April 2013

"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

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