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Monetizing Your Data

eBook - A Guide to Turning Data into Profit-Driving Strategies and Solutions

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Bibliografische Daten
ISBN/EAN: 9781119356257
Sprache: Englisch
Umfang: 368 S., 9.88 MB
Auflage: 1. Auflage 2017
E-Book
Format: EPUB
DRM: Adobe DRM

Beschreibung

<b>Transforming data into revenue generating strategies and actions</b><p>Organizations are swamped with datacollected from web traffic, point of sale systems, enterprise resource planning systems, and more<i>,</i> but what to do with it?<i>Monetizing your Data</i> 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.</p><p>This book shows you how to use your data to:</p><ul><li>Monetize your data to drive revenue and cut costs</li><li>Connect your data to decisions that drive action and deliver value</li><li>Develop analytic tools to guide managers up and down the ladder to better decisions</li></ul><p>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;<i>Monetizing your Data</i> walks you through the translation and transformation to help you leverage your data into value creating strategies.</p>

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 xiiiAcknowledgments xviiAbout the Authors xixSECTION I Introduction 1Chapter 1 Introduction 3Decisions 4Analytical Journey 7Solving the Problem 8The Survey Says 9How to Use This Book 12Lets Start 15Chapter 2 Analytical Cycle: Driving Quality Decisions 16Analytical Cycle Overview 17Hierarchy of Information User 28Next Steps 30Chapter 3 Decision Architecture Methodology: Closing the Gap 31Methodology Overview 32Discovery 36Decision Analysis 38Monetization Strategy 40Agile Analytics 41Enablement 46Summary 49SECTION II Decision Analysis 51Chapter 4 Decision Analysis: Architecting Decisions 53Category Tree 54Question Analysis 57Key Decisions 61Data Needs 64Action Levers 67Success Metrics 68Category Tree Revisited 71Summary 74SECTION III Monetization Strategy 77Chapter 5 Monetization Strategy: Making Data Pay 79Business Levers 81Monetization Strategy Framework 84Decision Analysis and Agile Analytics 85Competitive and Market Information 95Summary 97Chapter 6 Monetization Guiding Principles: Making It Solid 98Quality Data 99Be Specific 102Be Holistic 103Actionable 104Decision Matrix 106Grounded in Data Science 107Monetary Value 108Confidence Factor 109Measurable 111Motivation 112Organizational Culture 113Drives Innovation 113Chapter 7 Product Profitability Monetization Strategy: A Case Study 115Background 115Business Levers 117Discovery 117Decide 118Data Science 125Monetization Framework Requirements 125Decision Matrix 128SECTION IV Agile Analytics 131Chapter 8 Decision Theory: Making It Rational 133Decision Matrix 134Probability 136Prospect Theory 139Choice Architecture 140Cognitive Bias 141Chapter 9 Data Science: Making It Smart 145Metrics 146Thresholds 149Trends and Forecasting 150Correlation Analysis 151Segmentation 154Cluster Analysis 156Velocity 160Predictive and Explanatory Models 161Machine Learning 162Chapter 10 Data Development: Making It Organized 164Data Quality 164Dirty Data, Now What? 169Data Types 170Data Organization 172Data Transformation 176Summary 180Chapter 11 Guided Analytics: Making It Relevant 181So, What? 181Guided Analytics 184Summary 196Chapter 12 User Interface (UI): Making It Clear 197Introduction to UI 197The Visual Palette 198Less Is More 199With Just One Look 206Gestalt Principles of Pattern Perception 209Putting It All Together 212Summary 220Chapter 13 User Experience (UX): Making It Work 221Performance Load 221Go with the Flow 225Modularity 228Propositional Density 229Simplicity on the Other Side of Complexity 231Summary 232SECTION V Enablement 233Chapter 14 Agile Approach: Getting Agile 235Agile Development 235Riding the Wave 236Agile Analytics 237Summary 241Chapter 15 Enablement: Gaining Adoption 242Testing 242Adoption 245Summary 250Chapter 16 Analytical Organization: Getting Organized 251Decision Architecture Team 251Decision Architecture Roles 259Subject Matter Experts 261Analytical Organization Mindset 262SECTION VI Case Study 265Case Study Michael Andrews Bespoke 267Discovery 267Decision Analysis Phase 278Monetization Strategy, Part I 286Agile Analytics 287Monetization Strategy, Part II 303Guided Analytics 313Closing 324Bibliography 327Index 331

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