Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)

Using Agile methods, you can bring far greater innovation, value, and quality to any data warehouse, business intelligence, or analytics project. However, conventional Agile methodologies 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; support enormous and fast-growing data volumes; and more. Collier's techniques offer equal 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 works together towards 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, DBA, 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 Contents 10 Foreword 16 Foreword 18 Preface 20 Acknowledgments 34 About the Author 36 Part I: Agile Analytics: Management Methods 38 Chapter 1 Introducing Agile Analytics 40 Alpine-Style Systems Development 41 What Is Agile Analytics? 44 Data Warehousing Architectures and Skill Sets 50 Why Do We Need Agile Analytics? 53 Introducing FlixBuster Analytics 59 Wrap-Up 60 Chapter 2 Agile Project Management 62 What Is Agile Project Management? 63 Phased-Sequential DW/BI Development 67 Envision → Explore Instead of Plan → Do 69 Changing the Role of Project Management 72 Making Sense of Agile “Flavors” 73 Tenets of Agility 76 Wrap-Up 93 Chapter 3 Community, Customers, and Collaboration 96 What Are Agile Community and Collaboration? 97 The Agile Community 101 A Continuum of Trust 104 The Mechanics of Collaboration 106 Consumer Collaboration 110 Doer Collaboration 114 Planner Collaboration 115 Precursors to Agility 117 Wrap-Up 119 Chapter 4 User Stories for BI Systems 122 What Are User Stories? 123 User Stories versus Requirements 126 From Roles to Use Cases to User Stories 129 Decomposing Epics 136 What’s the Smallest, Simplest Thing? 140 Story Prioritization and Backlog Management 144 Story-Point Estimating 148 Parking Lot Diagrams 154 Wrap-Up 156 Chapter 5 Self-Organizing Teams Boost Performance 158 What Is a Self-Organizing Team? 159 Self-Organization Requires Self-Discipline 164 Self-Organization Requires Shared Responsibility 165 Self-Organization Requires Team Working Agreements 167 Self-Organization Requires Honoring Commitments 169 Self-Organization Requires Glass-House Development 171 Self-Organizing Requires Corporate Alignment 173 Wrap-Up 174 Part II: Agile Analytics: Technical Methods 176 Chapter 6 Evolving Excellent Design 178 What Is Evolutionary Design? 181 How Much Up-Front Design? 185 Agile Modeling 186 Data Model Patterns 189 Managing Technical Debt 191 Refactoring 194 Deploying Warehouse Changes 204 Other Reasons to Take an Evolutionary Approach 208 Case Study: Adaptive Warehouse Architecture 211 Wrap-Up 226 Chapter 7 Test-Driven Data Warehouse Development 230 What Is Agile Analytics Testing? 231 Agile Testing Framework 234 BI Test Automation 238 Sandbox Development 248 Test-First BI Development 252 BI Testing Guidelines 257 Setup Time 258 Functional BI Testing 259 Wrap-Up 260 Chapter 8 Version Control for Data Warehousing 262 What Is Version Control? 263 The Repository 267 Working with Files 270 Organizing the Repository 277 Tagging and Branching 282 Choosing an Effective Tool 289 Wrap-Up 291 Chapter 9 Project Automation 294 What Is Project Automation? 295 Getting Started 298 Build Automation 299 Continuous Integration 311 Push-Button Releases 318 Wrap-Up 325 Chapter 10 Final Words 328 Focus on the Real Problem 328 Being Agile versus Doing Agile 330 Gnarly Problems 333 What about Emerging Technologies? 335 Adoption Strategies 336 Closing Thoughts . . . 343 References and Recommended Reading 346 Index 352 A 352 B 353 C 354 D 355 E 357 F 358 G 358 H 358 I 358 J 359 K 359 L 359 M 359 N 360 O 360 P 360 Q 361 R 361 S 362 T 363 U 364 V 365 W 366 X 366 Y 366 Z 366

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.

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.