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Monetizing Your Data. A Guide to Turning Data into Profit-Driving Strategies and Solutions

  • ID: 3832457
  • Book
  • 368 Pages
  • John Wiley and Sons Ltd
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Praise for MONETIZING YOUR DATA

"Turning data into dollars isn′t only a matter of putting raw data up for sale. Wells and Chiang show in this book that there are many ways to add value to data, and that many of them involve analytics. This book is for any executive who wants data to benefit the bottom line."
Thomas H. Davenport, Distinguished Professor, Babson College, author of Competing on Analytics and Only Humans Need Apply

"Succeeding with analytics is hard. It takes discipline and attention to detail. Everything from data inputs, to metric computation, to interpretation, to presenting of the results must be thought through, well executed, and documented. This book provides detailed guidance on a wide range of specific steps, decisions, and actions that must be undertaken if you wish to successfully create and deploy analytic processes in your organization."
Bill Franks, Chief Analytics Officer, Teradata and author of the books Taming The Big Data Tidal Wave and The Analytics Revolution

"Effectively using and monetizing data is one of the critical challenges facing organizations today. This book goes to the heart of the challenge by focusing immediately and systematically on decisions and on the ′so what?′ that is essential for a successful data–driven, analytical business."
James Taylor, CEO of Decision Management Solutions and author of Real–World Decision Modeling with DMN

"Andrew and Kathy have laid out a great framework for coming up with analytical solutions to business problems. The real–world examples they have provided using their approach is a must–read for aspiring analysts as well as decision makers. I am sure organizations can benefit from implementing this approach to extract value from their data assets."
Dev Koushik, Vice President, Global Revenue Optimization and Distribution Operations, IHG

"Data is a company′s most important asset and monetizing data should be central to every company′s strategy in the future. Monetizing Your Data provides a practical blueprint to build out the critical capabilities that are required to execute on monetizing data."
Bill Groves, Chief Data & Analytic Officer, Honeywell International Inc.

"Aircraft systems are complex, but pilots receive easy to read and understand, actionable, credible information from their cockpits. Business is extremely complex but has lacked a ′cockpit′ where data is credible and actionable. In Monetizing Your Data, Kathy and Andrew have now developed a blueprint for a cockpit, that for the first time can reap true rewards from data."
Roberto Peon, former Chief Marketing Officer and President, Brazil for BellSouth International

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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

Let s 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

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Andrew Roman Wells
Kathy Williams Chiang
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