Monetizing Your Data: A Guide to Turning Data into Profit–Driving Strategies and Solutions

Transforming data into revenue generating strategies and actions Organizations are swamped with data collected 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: Monetize your data to drive revenue and cut costs Connect your data to decisions that drive action and deliver value Develop analytic tools to guide managers up and down the ladder to better decisions 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. INDICE: 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

• ISBN: 978-1-119-35624-0
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 368
• Fecha Publicación: 19/04/2017
• Nº Volúmenes: 1
• Idioma: Inglés