Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing
By: Ken W. Collier (author)Paperback
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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.
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.
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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