Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing [NOOK Book]

Overview

Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that.

 

Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, ...

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Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing

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Overview

Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that.

 

Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier’s techniques offer optimal value whether your projects involve “back-end” data management, “front-end” business analysis, or both.

  • Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success
  • Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation

Collier brings together proven solutions you can apply right now—whether you’re an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results—and have fun along the way.

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Editorial Reviews

From the Publisher
“This book does a great job of explaining why and how you would implement Agile Analytics in the real world. Ken has many lessons learned from actually implementing and refining this approach. Business Intelligence is definitely an area that can benefit from this type of discipline.”

—Dale Zinkgraf, Sr. Business Intelligence Architect

“One remarkable aspect of Agile Analytics is the breadth of coverage—from product and backlog management to Agile project management techniques, from self-organizing teams to evolutionary design practices, from automated testing to build management and continuous integration. Even if you are not on an analytics project, Ken’s treatment of this broad range of topics related to products with a substantial data-oriented flavor will be useful for and beyond the analytics community.”

—Jim Highsmith, Executive Consultant, ThoughtWorks, Inc., and author of Agile Project Management

“Agile methods have transformed software development, and now it’s time to transform the analytics space. Agile Analytics provides the knowledge needed to make the transformation to Agile methods in delivering your next analytics projects.”

—Pramod Sadalage, coauthor of Refactoring Databases: Evolutionary Database Design

“This book captures the fundamental strategies for successful business intelligence/analytics projects for the coming decade. Ken Collier has raised the bar for analytics practitioners—are you up to the challenge?”

—Scott Ambler, Chief Methodologist for Agile and Lean, IBM Rational Founder, Agile Data Method

“A sweeping presentation of the fundamentals that will empower teams to deliver high-quality, high-value, working business intelligence systems far more quickly and cost effectively than traditional software development methods.”

—Ralph Hughes, author of Agile Data Warehousing

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Product Details

  • ISBN-13: 9780321669544
  • Publisher: Pearson Education
  • Publication date: 8/2/2011
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 368
  • Sales rank: 922,850
  • File size: 5 MB

Meet the Author

Ken Collier has worked with Agile methods since 2003, and pioneered the integration of Agile methods with data warehousing, business intelligence, and analytics to create the Agile Analytics style. He continues to refine these ideas as technical lead and project manager on several Agile DW/BI project teams. Collier frequently trains DW/BI teams in Agile Analytics, and has been a keynote speaker on the subject at HEDW (Higher Education Data Warehouse) 2011 and multiple TDWI (The Data Warehousing Institute) World Conferences. He is founder and president of KWC Technologies, Inc., and a senior consultant in the Cutter Consortium’s Agile Development and Business Intelligence practice areas.
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Table of Contents

Foreword by Jim Highsmith xv

Foreword by Wayne Eckerson xvii

Preface xix

Acknowledgments xxxiii

About the Author xxxv

 

Part I: Agile Analytics: Management Methods 1

 

Chapter 1: Introducing Agile Analytics 3

Alpine-Style Systems Development 4

What Is Agile Analytics? 7

Data Warehousing Architectures and Skill Sets 13

Why Do We Need Agile Analytics? 16

Introducing FlixBuster Analytics 22

Wrap-Up 23

 

Chapter 2: Agile Project Management 25

What Is Agile Project Management? 26

Phased-Sequential DW/BI Development 30

Envision → Explore Instead of Plan → Do 32

Changing the Role of Project Management 35

Making Sense of Agile “Flavors” 36

Tenets of Agility 39

Wrap-Up 56

 

Chapter 3: Community, Customers, and Collaboration 59

What Are Agile Community and Collaboration? 60

The Agile Community 64

A Continuum of Trust 67

The Mechanics of Collaboration 69

Consumer Collaboration 73

Doer Collaboration 77

Planner Collaboration 78

Precursors to Agility 80

Wrap-Up 82

 

Chapter 4: User Stories for BI Systems 85

What Are User Stories? 86

User Stories versus Requirements 89

From Roles to Use Cases to User Stories 92

Decomposing Epics 99

What’s the Smallest, Simplest Thing? 103

Story Prioritization and Backlog Management 107

Story-Point Estimating 111

Parking Lot Diagrams 117

Wrap-Up 119

 

Chapter 5: Self-Organizing Teams Boost Performance 121

What Is a Self-Organizing Team? 122

Self-Organization Requires Self-Discipline 127

Self-Organization Requires Shared Responsibility 128

Self-Organization Requires Team Working Agreements 130

Self-Organization Requires Honoring Commitments 132

Self-Organization Requires Glass-House Development 134

Self-Organizing Requires Corporate Alignment 136

Wrap-Up 137

 

Part II: Agile Analytics: Technical Methods 139

 

Chapter 6: Evolving Excellent Design 141

What Is Evolutionary Design? 144

How Much Up-Front Design? 148

Agile Modeling 149

Data Model Patterns 152

Managing Technical Debt 154

Refactoring 157

What Is Refactoring? 159

Deploying Warehouse Changes 167

Other Reasons to Take an Evolutionary Approach 171

Case Study: Adaptive Warehouse Architecture 174

Wrap-Up 189

 

Chapter 7: Test-Driven Data Warehouse Development 193

What Is Agile Analytics Testing? 194

Agile Testing Framework 197

BI Test Automation 201

Sandbox Development 211

Test-First BI Development 215

BI Testing Guidelines 220

Setup Time 221

Functional BI Testing 222

Wrap-Up 223

 

Chapter 8: Version Control for Data Warehousing 225

What Is Version Control? 226

The Repository 230

Working with Files 233

Organizing the Repository 240

Tagging and Branching 245

Choosing an Effective Tool 252

Wrap-Up 254

 

Chapter 9: Project Automation 257

What Is Project Automation? 258

Getting Started 261

Build Automation 262

Continuous Integration 274

Push-Button Releases 281

Wrap-Up 288

 

Chapter 10: Final Words 291

Focus on the Real Problem 291

Being Agile versus Doing Agile 293

Gnarly Problems 296

What about Emerging Technologies? 298

Adoption Strategies 299

Closing Thoughts . . . 306

 

References and Recommended Reading 309

Index 315

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Sort by: Showing all of 2 Customer Reviews
  • Posted January 19, 2012

    more from this reviewer

    Learn how to "be agile", not just "do agile"

    If your business intelligence team has discussed "going agile", this book can give you practical information to help you get there. It's refreshing to see that business intelligence and analytics professionals can adopt practices typically associated with Java, Ruby, and Objective-C developers. The book is organized into two sections, management methods and technical methods. Most of the technical methods focus on data modeling and data integration (often referred to as Extract, Transform, and Load, or ETL). While these areas are critical to a successful business intelligence system, my role is most often focused on the presentation layer or BI toolset (such as SAP BusinessObjects). So I personally gravitated toward the first half of the book, management methods. Ken says more than once that the whole point of agile is to "be agile", not just to "do agile". Unfortunately, "agile" can be overused as the latest management buzzword to dress up "hacking" or "unrealistic deadlines". I was actually surprised to read that agile may not improve delivery times. In the short term, delivery times may increase. But the payoff for agility is projects that more quickly respond to changing requirements and users that receive smaller functional deliveries instead of the "big bang" of the waterfall project death march. While the book is a well-written and easy to read, I found it necessary to read slowly, chapter by chapter, and reflect on what I had read. The book would easily lend itself to a weekly BI book club, where technicians, users, and management meet weekly to discuss the book one chapter at a time. Recommended reading.

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  • Anonymous

    Posted January 19, 2012

    No text was provided for this review.

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