Agile Data Warehousing Project Management Business Intelligence Systems Using Scrum

ISBN-10: 0123964636
ISBN-13: 9780123964632
Edition: 2013
Authors: Ralph Hughes
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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  More...

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Book details

List price: $49.95
Copyright year: 2013
Publisher: Elsevier Science & Technology Books
Publication date: 9/28/2012
Binding: Paperback
Pages: 366
Size: 7.50" wide x 9.25" long x 0.75" tall
Weight: 1.694
Language: English

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

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.

List of Figures
List of Tables
Preface
Author's Bio
An Introduction to Iterative Development
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 as an answer to waterfall's problems
Agile methods provide better results
Agile for data warehousing
Data warehousing entails a "breadth of complexity"
Adapted scrum handles the breadth of data warehousing well
Managing data warehousing's "depth of complexity"
Guide to this book and other materials
Simplified treatment of data architecture for book 1
Companion web site
Where to be cautious with agile data warehousing
Summary
Iterative Development in a Nutshell
Starter concepts
Three nested cycles
The release cycle
Development and daily cycles
Shippable code and the definition of done
Time-boxed development
Caves and commons
Product owners and scrum masters
Improved role for the project manager
Might a project manager serve as a scrum master?
User stories and backlogs
Estimating user stories in story points
Iteration phase 1: story conferences
Iteration phase 2: task planning
Basis of estimate cards to escape repeating hard thinking
Task planning doublechecks story planning
Iteration phase 3: development phase
Self-organization
Daily scrums
Accelerated programming
Test-driven development
Architectural compliance and "tech debt"
Iteration phase 4: user demo
Iteration phase 5: sprint retrospectives
Retrospectives are vital
Close collaboration is essential
Selecting the optimal iteration length
Nonstandard sprints
Sprint 0
Where did scrum come from?
Distant history
Scram emerges
Summary
Streamlining Project Management
Highly transparent task boards
Task boards amplify project quality
Task boards naturally integrate team efforts
Scrum masters must monitor the task board
Burndown charts reveal the team aggregate progress
Detecting trouble with burndown charts
Developers are not the burndown chart's victims
Calculating velocity from burndown charts
Common variations on burndown charts
Setting capacity when the team delivers early
Managing tech debt
Managing miditeration scope creep
Diagnosing problems with burndown chart patterns
An early hill to climb
Shallow glide paths
Persistent inflation
Should you extend a sprint if running late?
Extending iterations is generally a bad idea
Two instances where a changing time box might help
Should teams track actual hours during a sprint?
Eliminating hour estimation altogether
Managing geographically distributed teams
Consider whether fully capable subteams are possible
Visualize the problem in terms of communication
Choose geographical divisions to minimize the challenge
Invest in a solid esprit de corp
Provide repeated booster shots of colocation for individuals
Invest in high-quality telepresence equipment
Provide agile team group ware
Summary
Defining Data Warehousing Projects for Iterative Development
Authoring Better User Stories
Traditional requirements gathering and its discontents
Big, careful requirements not a solution
A step in the right direction
Agile's idea of "user stories"
Advantages of user stories
Identifying rather than documenting the requirements
User story definition fundamentals
Quick test for actionable user stories
How small is small?
Epics, themes, and stories
Common techniques for writing good user stories
Keep story writing simple
Use stories to manage uncertainty
Reverse story components
Focus on understanding "who"
Focus on understanding "what"
Focus on understanding "why"
Be wary of the remaining w's
Add acceptance criteria to the story-writing conversations
Summary
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
Key observations
User role modeling results
Key persona definitions
Carla in carp strategy
Franklin in finance
An example of an initial backlog interview
Framing the project
Finance is upstream
Finance categorizes source data
Customer segmentation
Consolidated product hierarchies
Sales channel
Unit reporting
Geographies
Product usage
Observations regarding initial backlog sessions
Sometimes a lengthy process
Detecting backlog components
Managing user story components on the backlog
Prioritizing stories
Summary
Developer Stories for Data Integration
Why developer stories are needed
Introducing the "developer story"
Format of the developer story
Developer stories in the agile requirements management scheme
Agile purists do not like developer stories
Initial developer story workshops
Developers workshop within software engineering cycles
Data warehousing/business intelligence reference data architecture
Forming backlogs with developer stories
Evaluating good developer stories: DILBERT'S test
Demonstrable
Independent
Layered
Business valued
Estimable
Refinable
Testable
Small
Secondary techniques when developer stories are still too large
Decomposition by rows
Decomposition by column sets
Decomposition by column type
Decomposition by tables
Theoretical advantages of "small"
Summary
Estimating and Segmenting Projects
Failure of traditional estimation techniques
Traditional estimating strategies
Why waterfall teams underestimate
Criteria for a better estimating approach
An agile estimation approach
Estimating within the iteration
Estimating the overall project
Quick story points via "estimation poker"
Story points and ideal time
Story points defined
Ideal time defined
The advantage of story points
Estimation accuracy as an indicator of team performance
Value pointing user stories
Packaging stories into iterations and project plans
Criteria for better story prioritization
Segmenting projects into business-valued releases
The data architectural process supporting project segmentation
Artifacts employed for project segmentation
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
Summary
Adapting Iterative Development for Data Warehousing Projects
Adapting Agile for Data Warehousing
The context as development begins
Data warehousing/business intelligence-specific team roles
Project architect
Data architect
Systems analyst
Systems tester
The leadership subteam
Resident and visiting "resources"
New agile characteristics required
Avoiding data churn within sprints
Pipeline delivery for a sustainable pace
New meaning for iteration 0 and iteration -1
Pipeline requires two-step user demos
Keeping pipelines from delaying defect correction
Resolving pipelining's task board issues
Pipelining as a buffer-based process
Pipelining is controversial
Continuous and automated integration testing
High quality is a necessity
Agile warehousing testing requirements
The need for automation
Requirements for a warehouse test engine
Automated testing for front-end applications
Evolutionary target schemas-the hard way
Summary
Starting and Scaling Agile Data Warehousing
Starting a scrum team
Stage 1: time box and story points
Stage 2: pipelined delivery
Stage 3: developer stories and current estimates
Stage 4: managed development data and test-driven development
Stage 5: automatic and continuous integration testing
Stage 6: pull-based collaboration
Scaling agile
Application complexity
Geographical distribution
Team size
Compliance requirements
Information technology governance
Organizational culture
Organizational distribution
Coordinating multiple scrum teams
Coordinating1 through scrum of scrums
Matching milestones
Balancing work between teams with earned-value reporting
What is agile data warehousing?
Communicating success
Handoff quality
Quality of estimates
Defects by iteration
Burn-up charts
Cross-method comparison projects
Cycle times and story point distribution
Moving to pull-driven systems
A glimpse at a pull-based approach
Kanban advantages
A more cautious view
Stages of scrumban
Summary
References
Index

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