Organizations are swamped with datacollected from web traffic, point of sale systems, enterprise resource planning systems, and more, but what to do with it?Monetizing your Data provides a framework and path for business managers to convert ever-increasing volumes of data into revenue generating actions through three disciplines: decision architecture, data science, and guided analytics. There are large gaps between understanding a business problem and knowing which data is relevant to the problem and how to leverage that data to drive significant financial performance. Using a proven methodology developed in the field through delivering meaningful solutions to Fortune 500 companies, this book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. Beginning with an explanation of the analytical cycle, this book guides you through the process of developing value generating strategies that can translate into big returns. The companion website, www.monetizingyourdata.com, provides templates, checklists, and examples to help you apply the methodology in your environment, and the expert author team provides authoritative guidance every step of the way.

This book shows you how to use your data to:

Turning data into action is key; data can be a valuable competitive advantage, but only if you understand how to organize it, structure it, and uncover the actionable information hidden within it through decision architecture and guided analytics. From multinational corporations to single-owner small businesses, companies of every size and structure stand to benefit from these tools, methods, and techniques;Monetizing your Data walks you through the translation and transformation to help you leverage your data into value creating strategies.

ANDREW ROMAN WELLS is the CEO of Aspirent, a management consulting firm focused on analytics. He has extensive experience building analytical solutions for a wide range of companies, from Fortune 500s to small non-profits. Mr. Wells focuses on helping organizations utilize their data to make impactful decisions that drive revenue through monetization strategies. He has been building analytical solutions for over 25 years and is excited to share these practical methods, tools, and techniques with a wider audience. Mr. Wells earned a Bachelors degree in Business Administration with a focus on Finance and Management Information Systems from the University of Georgia.

KATHY WILLIAMS CHIANG is an established business analytics practitioner with expertise in guided analytics, analytic data mart development, and business planning. Prior to her current position as vice president of business insights at Wunderman Data Management, Ms. Chiang consulted with Aspirent on numerous analytic projects for several multinational clients including IHG and Coca Cola, among others. She has also worked for multinational corporations including Telecommunications Systems of Trinidad and Tobago, Acuity Brands Lighting, BellSouth International, and Portman Overseas. Ms. Chiang is experienced in designing and developing analytic tools and management dashboards that inform, matter, and drive action. She is highly skilled in data exploration, analysis, visualization, and presentation, and has developed solutions in the telecom, hospitality, and consumer products industries covering customer experience, marketing campaigns, revenue management, and web analytics. Ms. Chiang, a native of New Orleans, holds a Bachelor of Science degree in Chemistry, summa cum laude with University honors (4.0), from Louisiana State University, as well as an MBA from Tulane University. She is a member of Phi Beta Kappa and Mensa.

Preface xiii

Acknowledgments xvii

About the Authors xix

Section I Introduction 1

Chapter 1 Introduction 3

Decisions 4

Analytical Journey 7

Solving the Problem 8

The Survey Says 9

How to Use This Book 12

Lets Start 15

Chapter 2 Analytical Cycle: Driving Quality Decisions 16

Analytical Cycle Overview 17

Hierarchy of Information User 28

Next Steps 30

Chapter 3 Decision Architecture Methodology: Closing the Gap 31

Methodology Overview 32

Discovery 36

Decision Analysis 38

Monetization Strategy 40

Agile Analytics 41

Enablement 46

Summary 49

Section II Decision Analysis 51

Chapter 4 Decision Analysis: Architecting Decisions 53

Category Tree 54

Question Analysis 57

Key Decisions 61

Data Needs 64

Action Levers 67

Success Metrics 68

Category Tree Revisited 71

Summary 74

Section III Monetization Strategy 77

Chapter 5 Monetization Strategy: Making Data Pay 79

Business Levers 81

Monetization Strategy Framework 84

Decision Analysis and Agile Analytics 85

Competitive and Market Information 95

Summary 97

Chapter 6 Monetization Guiding Principles: Making It Solid 98

Quality Data 99

Be Specific 102

Be Holistic 103

Actionable 104

Decision Matrix 106

Grounded in Data Science 107

Monetary Value 108

Confidence Factor 109

Measurable 111

Motivation 112

Organizational Culture 113

Drives Innovation 113

Chapter 7 Product Profitability Monetization Strategy: A Case Study 115

Background 115

Business Levers 117

Discovery 117

Decide 118

Data Science 125

Monetization Framework Requirements 125

Decision Matrix 128

Section IV Agile Analytics 131

Chapter 8 Decision Theory: Making It Rational 133

Decision Matrix 134

Probability 136

Prospect Theory 139

Choice Architecture 140

Cognitive Bias 141

Chapter 9 Data Science: Making It Smart 145

Metrics 146

Thresholds 149

Trends and Forecasting 150

Correlation Analysis 151

Segmentation 154

Cluster Analysis 156

Velocity 160

Predictive and Explanatory Models 161

Machine Learning 162

Chapter 10 Data Development: Making It Organized 164

Data Quality 164

Dirty Data, Now What? 169

Data Types 170

Data Organization 172

Data Transformation 176

Summary 180

Chapter 11 Guided Analytics: Making It Relevant 181

So, What? 181

Guided Analytics 184

Summary 196

Chapter 12 User Interface (UI): Making It Clear 197

Introduction to UI 197

The Visual Palette 198

Less Is More 199

With Just One Look 206

Gestalt Principles of Pattern Perception 209

Putting It All Together 212

Summary 220

Chapter 13 User Experience (UX): Making It Work 221

Performance Load 221

Go with the Flow 225

Modularity 228

Propositional Density 229

Simplicity on the Other Side of Complexity 231

Summary 232

Section V Enablement 233

Chapter 14 Agile Approach: Getting Agile 235

Agile Development 235

Riding the Wave 236

Agile Analytics 237

Summary 241

Chapter 15 Enablement: Gaining Adoption 242

Testing 242

Adoption 245

Summary 250

Chapter 16 Analytical Organization: Getting Organized 251

Decision Architecture Team 251

Decision Architecture Roles 259

Subject Matter Experts 261

Analytical Organization Mindset 262

Section VI Case Study 265

Case Study Michael Andrews Bespoke 267

Discovery 267

Decision Analysis Phase 278

Monetization Strategy, Part I 286

Agile Analytics 287

Monetization Strategy, Part II 303

Guided Analytics 313

Closing 324

Bibliography 327

Index 331