Big Data, Big Analytics

Considered one of the top sales and marketing executives in the business analytics space, MICHAEL MINELLI is Vice President, Information Services, for MasterCard Advisors. The majority of his sixteen years of analytics industry experience was at SAS, where he spent over eleven years helping clients with large-scale analytic projects related to marketing, risk, supply chain, and finance. MICHELE CHAMBERS is currently in the Big Data Analytics startup world and was formerly the General Manager & Vice President of Big Data Analytics at IBM, where her team was responsible for working with customers to fully exploit the IBM Big Data Platform. AMBIGA DHIRAJ is the Head of Client Delivery for Mu Sigma, where she leads their delivery teams to solve high-impact business problems in the areas of marketing, supply chain, and risk analytics for market-leading companies across multiple verticals.

• Foreword xiiiPreface xixAcknowledgments xxiChapter 1 What is Big Data and Why is It Important? 1A Flood of Mythic “Start-Up” Proportions 4Big Data is More Than Merely Big 5Why Now? 6A Convergence of Key Trends 7Relatively Speaking . . . 9A Wider Variety of Data 10The Expanding Universe of Unstructured Data 11Setting the Tone at the Top 15Notes 18Chapter 2 Industry Examples of Big Data 19Digital Marketing and the Non-line World 19Don’t Abdicate Relationships 22Is IT Losing Control of Web Analytics? 23Database Marketers, Pioneers of Big Data 24Big Data and the New School of Marketing 27Consumers Have Changed. So Must Marketers. 28The Right Approach: Cross-Channel Lifecycle Marketing 28Social and Affiliate Marketing 30Empowering Marketing with Social Intelligence 31Fraud and Big Data 34Risk and Big Data 37Credit Risk Management 38Big Data and Algorithmic Trading 40Crunching Through Complex Interrelated Data 41Intraday Risk Analytics, a Constant Flow of Big Data 42Calculating Risk in Marketing 43Other Industries Benefit from Financial Services’ Risk Experience 43Big Data and Advances in Health Care 44“Disruptive Analytics” 46A Holistic Value Proposition 47BI is Not Data Science 49Pioneering New Frontiers in Medicine 50Advertising and Big Data: From Papyrus to Seeing Somebody 51Big Data Feeds the Modern-Day Donald Draper 52Reach, Resonance, and Reaction 53The Need to Act Quickly (Real-Time When Possible) 54Measurement Can Be Tricky 55Content Delivery Matters Too 56Optimization and Marketing Mixed Modeling 56Beard’s Take on the Three Big Data Vs in Advertising 57Using Consumer Products as a Doorway 58Notes 59Chapter 3 Big Data Technology 61The Elephant in the Room: Hadoop’s Parallel World 61Old vs. New Approaches 64Data Discovery: Work the Way People’s Minds Work 65Open-Source Technology for Big Data Analytics 67The Cloud and Big Data 69Predictive Analytics Moves into the Limelight 70Software as a Service BI 72Mobile Business Intelligence is Going Mainstream 73Ease of Mobile Application Deployment 75Crowdsourcing Analytics 76Inter- and Trans-Firewall Analytics 77R&D Approach Helps Adopt New Technology 80Adding Big Data Technology into the Mix 81Big Data Technology Terms 83Data Size 101 86Notes 88Chapter 4 Information Management 89The Big Data Foundation 89Big Data Computing Platforms (or Computing Platforms That Handle the Big Data Analytics Tsunami) 92Big Data Computation 93More on Big Data Storage 96Big Data Computational Limitations 96Big Data Emerging Technologies 97Chapter 5 Business Analytics 99The Last Mile in Data Analysis 101Geospatial Intelligence Will Make Your Life Better 103Listening: Is It Signal or Noise? 106Consumption of Analytics 108From Creation to Consumption 110Visualizing: How to Make It Consumable? 110Organizations are Using Data Visualization as a Way to Take Immediate Action 116Moving from Sampling to Using All the Data 121Thinking Outside the Box 122360° Modeling 122Need for Speed 122Let’s Get Scrappy 123What Technology is Available? 124Moving from Beyond the Tools to Analytic Applications 125Notes 125Chapter 6 The People Part of the Equation 127Rise of the Data Scientist 128Learning over Knowing 130Agility 131Scale and Convergence 131Multidisciplinary Talent 131Innovation 132Cost Effectiveness 132Using Deep Math, Science, and Computer Science 133The 90/10 Rule and Critical Thinking 136Analytic Talent and Executive Buy-in 137Developing Decision Sciences Talent 139Holistic View of Analytics 140Creating Talent for Decision Sciences 142Creating a Culture That Nurtures Decision Sciences Talent 144Setting Up the Right Organizational Structure for Institutionalizing Analytics 146Chapter 7 Data Privacy and Ethics 151The Privacy Landscape 152The Great Data Grab isn’t New 152Preferences, Personalization, and Relationships 153Rights and Responsibility 154Playing in a Global Sandbox 159Conscientious and Conscious Responsibility 161Privacy May Be the Wrong Focus 162Can Data Be Anonymized? 164Balancing for Counterintelligence 165Now What? 165Notes 167Conclusion 169Recommended Resources 175About the Authors 177Index 179