From Big Data to Big Profits SUCCESS WITH DATA AND ANALYTICS. Russell Walker OXFORD UNIVERSITY PRESS

Transcription

1 From Big Data to Big Profits SUCCESS WITH DATA AND ANALYTICS Russell Walker OXFORD UNIVERSITY PRESS

2 Contents Foreword xiii Preface xvii Acknowledgments xix Introduction xxi Definitions of Concepts and Terms Used Widely in the Book xxv Book Overview xxvi PART ONE EXAMINING BIG DATA AND ITS VALUE TO FIRMS i. Whatis "Big Data"? 3 Scale: How Big Is Big? How Big Will It Become? 6 Data Creation: A Measure of How Fast Data Is Generated 7 Data Storage: A Measure of Scale and the Data We Keep 9 Data Processing: A Measure of How Much Data We Use 10 Data Consumption: A Measure of Our Demand for Data 11 Implications of Scale in Big Data 15 Exploratory Data Analysis: Considering All of the Data 16 Data Organization and Metadata 19 Variety: Using More than Numerical Data 2.0 Velocity: Leveraging Data within Its Window of Opportunity 21 Viral Distribution of Data: Social Networks Take Front Stage 2.3 Availability of Data Alters Decisions for the Better 2.6 Where Is Big Data Being Created? 27 Customer Data: Externa! Data 28 Operations: Internal Data 30 Knowledge Sets: Internal Data 31 Mass Markets: External Data 33 vii

3 viii ; Contents 2. Benefits ofscale and Velocity in Big Data: The Movement to Now! 35 Overcoming Complexity through Scale in Big Data 35 Yelp and TripAdvisor: Gase Studies in the Creation of Value through Big Data 36 Scale 37 Organization and Metadata 37 Data Variety 38 Data Velocity 38 Data Availability 38 Value of Information: Risk Reduction 38 Data Velocity Is the New Normal 40 Automated Data Creation: A Necessary Byproduct of Scale and Velocity 42 Human Interactions with the Internet of Things: Wearable Devices 45 Mastering Velocity and Scale: Creating Advantages with Big Data 47 Increasing Data Velocity 47 Increasing Data Scale 48 Merging High Velocity and High Scale in Data 50 Merging High Velocity and High Scale at Amazon 51 Merging High Velocity and High Scale in Advertising 54 Getting to High Return on Big Data 55 Success in a High Velocity and High Precision Data Environment Big Data Expands with Passive Data Capture 61 Active Data Capture 61 Example of Passive Data Capture at Work 6z Passive Data Capture 63 Mobile Platforms Expand Passive Data Capture 65 What Variables Can Be Passively Captured with Smartphones Today? 66 Mobile Apps Perform Passive Data Capture Too 67 Passive Data Capture Will Change the Driving Experience 68 Passive Data Capture Adds Value to Agriculture 68 Valuable Features of Passive Data Capture 69 Passive Data Capture Is in the Home of the Future 70 Passive Data Capture Is Transforming Health Care 71 Trade-offs Are Inevitable When Passive Data Capture Is Collected and Leveraged 71 Passive Data Capture Raises Privacy Concerns 73

4 4. NovelMeasures in Market Activity: Direct vs. Indirect Measurement 75 Direct Measurement by Active Data Capture 76 From Micro to Macro 77 Indirect Measurement by Passive Data Capture 79 Measurement of Assets by Leveraging Big Data and Data Inverting 81 What's a Billboard Worth Exactly? 81 Inverting Data 81 Media Measurement by Third Parties 83 Measurement of Health Care Providers 84 Considerations in the Use of Direct and Indirect Asset Measurements 85 Contents ; ix 5. Precision in Data: New Possibilities for Mass Customization and Location-Based Services 86 New Sensors and Mobile Phone Systems Enable Precision in Location-Based Data Capture 86 Social Networks Enable Measuring the Previously Immeasurable 87 Precision in Measuring Human Performance Is Here Now 88 Precision Agriculture Is Changing Decision-Making in Powerful Ways 89 Precision Medicine and Genomics Enable Personalized Care 90 High Precision in Customer Data Leads to Mass Customization 91 Digital Platforms Enable Increased Precision in Data Capture 92 Precision in Data Is Critical to Unraveling Complexity Data Fusion: CombiningData to ProduceEconomic Value 9$ Data Availability in the Real Estate Industry 96 Zillow: A Real Estate Innovator 97 History of Zillow: Data Opens Opportunities 98 Zillow Focuses on Data Fusion and Data Productization 100 Zillow's Data Product Innovations 101 Make Me Move 102 Mortgage Marketplace 102 Zillow Digs 103 Zillow Data 103 Mobile 104 Success with Data Breeds Competition and Innovation 105 Data Comes in All Forms 107 Lessons from Zillow 108 Mint.com Transforms Personal Finance 110

5 x! Contents Fusing of Data at Mint.com Creates Novel Data Views for Users and Vendors in Lessons from Mint.com 112. PART TWO SUCCESS IN LEVERAGING BIG DATA 7. Strategies for Monetizing Big Data 117 Keep the Data Proprietary 119 Monetization Strategy: Leverage Data for Internal Operations 119 Monetization Strategy: Enter New Business 121 Monetization Strategy: License Data Exclusively 124 Data Strategy: Trade Data to Business Partners for Shared Benefits 127 Monetization Strategy: Trade Data with Downstream Business Partners 127 Data Strategy: Seil the Data Product (to a Host of Possible Clients) 131 Monetization Strategy: Seil Data Products to Asset Owners 133 Monetization Strategy: Seil Data Products to Other Interested Parties 136 Monetization Strategy: Seil Premxum Data Product Access 139 Data Strategy: Make the Data Available (and Even Free) to Many Users 141 Monetization Strategy: Leverage User Base for Advertisement Opportunities 142 Advertisement Strategy for Broad Awareness (Low Precision and Low Velocity Data) 146 Advertisement Strategy for Time-Sensitive Decisions (High Velocity in Data) 146 Advertising Strategy for Products or Services Aligned with Customer (High Precision in Data) 148 Advertising Strategy for Products or Services Aligned with Customer AND are Time-Sensitive (High Precision, High Velocity in Data) 150 Novel Data Creation in Advertisement on Digital Platforms 152 Origins of the Marketplace 153 Overview of Data Strategies and Monetization Strategies 158 Multi-sided Business Models Form to Monetize Data from Digital Platforms 160 Linkedln.com Creates Big Data 160 Lessons from Linkedln on Multi-sided Business Models Monetizing Big Data through Productization and Data Inverting 166 The Origins of Netflix as a Disruptive Innovator 167 Blockbuster: A History 168 Netflix Cultivates Big Data on Customer Preferences 169 Netflix Forms a Data Exchange with Customers 170 Big Data and Analytics Enable Netflix's Success 172

6 Contents! xi Data Supporting the Digital Platform Enables Customer Loyal ty 172 Analytics Enable Long-Tail Capture and Aggregation of Demand 173 Data on Movies Changes Relationship with Movie Houses 174 Employee Management Reflects Data Importance 177 The Future of Netflix: Data Wars Have Begun 178 Lessons from Netflix 182 ff. Impact of Analytics and Big Data on Corporate Culture and Recruitment 184 The Rise of the Data Scientist 185 A Portrait of a Data Scientist 188 Graduate Programs in Data Science are Available 192 Benefits of Functionally Assigned Analytical Teams 194 Challenges of Functionally Assigned Analytical Teams 195 Benefits of Centralized Analytical Teams 196 A New Organizational Model: Chief Data Scientist 197 Maximizing the Impact of Data Scientists StimulatingInnovation through BigData 202 Leveraging and Re-leveraging Data Dynamically 202 New Data Fuels Innovation 205 Digital Platforms Enable Innovation 207 New Data and Digital Platforms Can Change Markets 207 Innovation in Health Care 209 Innovation through Data Requires a Data Laboratory for Data Creation 210 Nest and Building New Digital Platforms for Innovation 213 Digital Platforms and the Internet of Things Fuel Innovation 215 Stimulating Innovation with Big Data Challenges 219 Experimenting with Data at the Enterprise DisruptingBusiness Models with New Datafrom Location-Based Services 222 Big Data Possibilities from Cellular Networks 223 Passive vs. Active Data Capture in Location-Based Services 225 Leveraging Location for Data Monetization 227 Location-Based Services 228 Trends in Location-Based Services 228 Foursquare: Using Customer Location Data to Guide You Where to Go 230 Opportunities Created by Leveraging Location-Based Data 231 Foursquare Example: Gaining Precision in User Location Data 234 San Francisco vs. New York 237

7 xii : Contents Lessons from Foursquare 238 Strategy Implications of Using Location-Based Data ProtectingData Assets 242 Privacy Concerns 242 Tracking and Monitoring 243 Who Owns the Data? Data Ownership Raises Many Questions 244 Data Ownership Differs for Actively Shared and Passively Captured Data 245 Privacy in Aggregate Data Views 247 Operational Risk in Dealing with Big Data 248 Best Practices for Firms Dealing with Sensitive Personal Data Future Trends in BigData 252 Increases in Automation for Data Capture, Creation, and Use 252 Cloud Computing Makes Big Data Possible for Most Firms 254 Flexible Analytical Tools Make Big Data Processing Possible to More Firms 254 Mobile Platforms Drive Location-Based Data and Services to New Levels 254 Analytical Talent Will Be in Short Supply Due to High Demand 255 Aggregation of Digital Platforms Will Become More Common 255 Digital Platforms Will Reduce Market Inefficiencies through New Data 256 Autonomy, not Just Automation, Will Become More Mainstream Getting Started SIGMA Framework for Implementing a Big Data Strategy: From BigData to Big Profits 258 Sources of Data 239 Innovation 259 Growth Mindset 260 Market Opportunities 261 Analytics 262 Big Data to Big Profits Diagnostic: Scoring an Enterprise with the SIGMA Framework for Big Data Readiness 263 Getting Started on the Path from Big Data to Big Profits 266 SELECTED BIBLIOGRAPHY 269 INDEX 271