Beschreibung
Transforming data into revenue generating strategies and actionsOrganizations 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:
Monetize your data to drive revenue and cut costsConnect your data to decisions that drive action and deliver valueDevelop 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.
Autorenportrait
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.
Inhalt
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
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