Table of Contents Introduction Overview of the Book and Technology How This Book Is Organized Who Should Read This Book Tools You Will Need What's on the Website What This Means for You Part I: Business Potential of Big Data Chapter 1: The Big Data Business Mandate Big Data MBA Introduction Focus Big Data on Driving Competitive Differentiation Critical Importance of “Thinking Differently” Summary Homework Assignment Notes Chapter 2: Big Data Business Model Maturity Index Introducing the Big Data Business Model Maturity Index Big Data Business Model Maturity Index Lessons Learned Summary Homework Assignment Chapter 3: The Big Data Strategy Document Establishing Common Business Terminology Introducing the Big Data Strategy Document Introducing the Prioritization Matrix Using the Big Data Strategy Document to Win the World Series Summary Homework Assignment Notes Chapter 4: The Importance of the User Experience The Unintelligent User Experience Consumer Case Study: Improve Customer Engagement Business Case Study: Enable Frontline Employees B2B Case Study: Make the Channel More Effective Summary Homework Assignment Part II: Data Science Chapter 5: Differences Between Business Intelligence and Data Science What Is Data Science? The Analyst Characteristics Are Different The Analytic Approaches Are Different The Data Models Are Different The View of the Business Is Different Summary Homework Assignment Notes Chapter 6: Data Science 101 Data Science Case Study Setup Fundamental Exploratory Analytics Analytic Algorithms and Models Summary Homework Assignment Notes Chapter 7: The Data Lake Introduction to the Data Lake Characteristics of a Business-Ready Data Lake Using the Data Lake to Cross the Analytics Chasm Modernize Your Data and Analytics Environment Analytics Hub and Spoke Analytics Architecture Early Learnings What Does the Future Hold? Summary Homework Assignment Notes Part III: Data Science for Business Stakeholders Chapter 8: Thinking Like a Data Scientist The Process of Thinking Like a Data Scientist Summary Homework Assignment Notes Chapter 9: “By” Analysis Technique “By” Analysis Introduction “By” Analysis Exercise Foot Locker Use Case “By” Analysis Summary Homework Assignment Notes Chapter 10: Score Development Technique Definition of a Score FICO Score Example Other Industry Score Examples LeBron James Exercise Continued Foot Locker Example Continued Summary Homework Assignment Notes Chapter 11: Monetization Exercise Fitness Tracker Monetization Example Summary Homework Assignment Notes Chapter 12: Metamorphosis Exercise Business Metamorphosis Review Business Metamorphosis Exercise Business Metamorphosis in Health Care Summary Homework Assignment Notes Part IV: Building Cross-Organizational Support Chapter 13: Power of Envisioning Envisioning: Fueling Creative Thinking The Prioritization Matrix Summary Homework Assignment Notes Chapter 14: Organizational Ramifications Chief Data Monetization Officer Privacy, Trust, and Decision Governance Unleashing Organizational Creativity Summary Homework Assignment Notes Chapter 15: Stories Customer and Employee Analytics Product and Device Analytics Network and Operational Analytics Characteristics of a Good Business Story Summary Homework Assignment Notes End User License Agreement End User License Agreement List of Illustrations Chapter 1: The Big Data Business Mandate Figure 1.1 Big Data Business Model Maturity Index Figure 1.2 Modern data/analytics environment Chapter 2: Big Data Business Model Maturity Index Figure 2.1 Big Data Business Model Maturity Index Figure 2.2 Crossing the analytics chasm Figure 2.3 Packaging and selling audience insights Figure 2.4 Optimize internal processes Figure 2.5 Create new monetization opportunities Chapter 3: The Big Data Strategy Document Figure 3.1 Big data strategy decomposition process Figure 3.2 Big data strategy document Figure 3.3 Chipotle's 2012 letter to the shareholders Figure 3.4 Chipotle's “increase same store sales” business initiative Figure 3.5 Chipotle key business entities and decisions Figure 3.6 Completed Chipotle big data strategy document Figure 3.7 Business value of potential Chipotle data sources Figure 3.8 Implementation feasibility of potential Chipotle data sources Figure 3.9 Chipotle prioritization of use cases Figure 3.10 San Francisco Giants big data strategy document Figure 3.11 Chipotle's same store sales results Chapter 4: The Importance of the User Experience Figure 4.1 Original subscriber e-mail Figure 4.2 Improved subscriber e-mail Figure 4.3 Actionable subscriber e-mail Figure 4.4 App recommendations Figure 4.5 Traditional Business Intelligence dashboard Figure 4.6 Actionable store manager dashboard Figure 4.7 Store manager accept/reject recommendations Figure 4.8 Competitive analysis use case Figure 4.9 Local events use case Figure 4.10 Local weather use case Figure 4.11 Financial advisor dashboard Figure 4.12 Client personal information Figure 4.13 Client financial information Figure 4.14 Client financial goals Figure 4.15 Financial contributions recommendations Figure 4.16 Spend analysis and recommendations Figure 4.17 Asset allocation recommendations Figure 4.18 Other investment recommendations Chapter 5: Differences Between Business Intelligence and Data Science Figure 5.1 Schmarzo TDWI keynote, August 2008 Figure 5.2 Oakland A's versus New York Yankees cost per win Figure 5.3 Business Intelligence versus data science Figure 5.4 CRISP: Cross Industry Standard Process for Data Mining Figure 5.5 Business Intelligence engagement process Figure 5.6 Typical BI tool graphic options Figure 5.7 Data scientist engagement process Figure 5.8 Measuring goodness of fit Figure 5.9 Dimensional model (star schema) Figure 5.10 Using flat files to eliminate or reduce joins on Hadoop Figure 5.11 Sample customer analytic profile Figure 5.12 Improve customer retention example Chapter 6: Data Science 101 Figure 6.1 Basic trend analysis Figure 6.2 Compound trend analysis Figure 6.3 Trend line analysis Figure 6.4 Boxplot analysis Figure 6.5 Geographical (spatial) trend analysis Figure 6.6 Pairs plot analysis Figure 6.7 Time series decomposition analysis Figure 6.8 Cluster analysis Figure 6.9 Normal curve equivalent analysis Figure 6.10 Normal curve equivalent seller pricing analysis example Figure 6.11 Association analysis Figure 6.12 Converting association rules into segments Figure 6.13 Graph analysis Figure 6.14 Text mining analysis Figure 6.15 Sentiment analysis Figure 6.16 Traverse pattern analysis Figure 6.17 Decision tree classifier analysis Figure 6.18 Cohorts analysis Chapter 7: The Data Lake Figure 7.1 Characteristics of a data lake Figure 7.2 The analytics dilemma Figure 7.3 The data lake line of demarcation Figure 7.4 Create a Hadoop-based data lake Figure 7.5 Create an analytic sandbox Figure 7.6 Move ETL to the data lake Figure 7.7 Hub and Spoke analytics architecture Figure 7.8 Data science engagement process Figure 7.9 What does the future hold? Figure 7.10 EMC Federation Business Data Lake Chapter 8: Thinking Like a Data Scientist Figure 8.1 Foot Locker's key business initiatives Figure 8.2 Examples of Foot Locker's in-store merchandising Figure 8.3 Foot Locker's store manager persona Figure 8.4 Foot Locker's strategic nouns or key business entities Figure 8.5 Thinking like a data scientist decomposition process Figure 8.6 Recommendations worksheet template Figure 8.7 Foot Locker's recommendations worksheet Figure 8.8 Foot Locker's store manager actionable dashboard Figure 8.9 Thinking like a data scientist decomposition process Chapter 9: “By” Analysis Technique Figure 9.1 Identifying metrics that may be better predictors of performance Figure 9.2 NBA shooting effectiveness Figure 9.3 LeBron James's shooting effectiveness Chapter 10: Score Development Technique Figure 10.1 FICO score considerations Figure 10.2 FICO score decision range Figure 10.3 Recommendations worksheet Figure 10.4 Updated recommendations worksheet Figure 10.5 Completed recommendations worksheet Figure 10.6 Potential Foot Locker customer scores Figure 10.7 Foot Locker recommendations worksheet Figure 10.8 CLTV based on sales Figure 10.9 More predictive CLTV score Chapter 11: Monetization Exercise Figure 11.1 “A day in the life” customer persona Figure 11.2 Fitness tracker prioritization Figure 11.3 Monetization road map Chapter 12: Metamorphosis Exercise Figure 12.1 Big Data Business Model Maturity Index Figure 12.2 Patient actionable analytic profile Chapter 13: Power of Envisioning Figure 13.1 Big Data Vision Workshop process and timeline Figure 13.2 Big Data Vision Workshop illustrative analytics Figure 13.3 Big Data Vision Workshop user experience mock-up Figure 13.4 Prioritize Healthcare Systems's use cases Figure 13.5 Prioritization matrix template Figure 13.6 Prioritization matrix process Chapter 14: Organizational Ramifications Figure 14.1 CDMO organizational structure