Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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.
- 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
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
List of Figures
List of Tables
Answering the skeptics
Parts and chapters of the book
Invitation to join the agile warehousing community
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
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
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
What is agile data warehousing?
Moving to pull-driven systems
- No. of pages:
- © Morgan Kaufmann 2012
- 28th September 2012
- Morgan Kaufmann
- eBook ISBN:
- Paperback ISBN:
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.
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
"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
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.