The Real Work of Data Science

About the Authors xv Preface xvii About the Companion Website xxi 1 A Higher Calling 1 The Life-Cycle View 2 Problem Elicitation: Understand the Problem 3 Goal Formulation: Clarify the Short-term and Long-term Goals 3 Data Collection: Identify Relevant Data Sources and Collect the Data 3 Data Analysis: Use Descriptive, Explanatory, and Predictive Methods 3 Formulation of Findings: State Results and Recommendations 4 Operationalization of Findings: Suggest Who, What, When, and How 5 Communication of Findings: Communicate Findings, Decisions, and Their Implications to Stakeholders 5 Impact Assessment: Plan and Deploy an Assessment Strategy 5 The Organizational Ecosystem 6 Organizational Structure 6 Organizational Maturity 6 Once Again, Our Goal 6 2 The Difference Between a Good Data Scientist and a Great One 9 Implications 11 3 Learn the Business 13 The Annual Report 13 SWOTs and Strategic Analysis 13 The Balanced Scorecard and Key Performance Indicators 14 The Data Lens 15 Build Your Network 16 Implications 16 4 Understand the Real Problem 17 A Telling Example 17 Understanding the Real Problem 18 Implications 19 5 Get Out There 21 Understand Context and Soft Data 21 Identify Sources of Variability 22 Selective Attention 23 Memory Bias 23 Implications 23 6 Sorry, but You Can't Trust the Data 25 Most Data Is Untrustworthy 25 Dealing with Immediate Issues 27 Getting in Front of Tomorrow's Data Quality Issues 29 Implications 30 7 Make It Easy for People to Understand Your Insights 31 First, Get the Basics Right 31 Presentations Get Passed Around 33 The Best of the Best 34 Implications 34 8 When the Data Leaves Off and Your Intuition Takes Over 35 Modes of Generalization 36 Implications 38 9 Take Accountability for Results 39 Practical Statistical Efficiency 39 Using Data Science to Perform Impact Analysis 41 Implications 42 10 What It Means to Be "Data-driven" 43 Data-driven Companies and People 43 Traits of the Data-driven 44 Traits of the Antis 46 Implications 46 11 Root Out Bias in Decision-making 49 Understand Why It Occurs 50 Take Control on a Personal Level 50 Solid Scientific Footings 51 Problem 1 52 Problem 2 52 Implications 53 12 Teach, Teach, Teach 55 The Rope Exercise 55 The "Roll Your Own" Exercise 56 The Starter Kit of Questions to Ask Data Scientists 59 Implications 60 13 Evaluating Data Science Outputs More Formally 63 Assessing Information Quality 63 A Hands-On Information Quality Workshop 64 Phase I: Individual Work 64 Phase II: Teamwork 65 Phase III: Group Presentation 66 Implications 66 14 Educating Senior Leaders 67 Covering the Waterfront 68 Companies Need a Data and Data Science Strategy 70 Organizations Are "Unfit for Data" 71 Get Started with Data Quality 71 Implications 71 15 Putting Data Science, and Data Scientists, in the Right Spots 73 The Need for Senior Leadership 73 Building a Network of Data Scientists 74 Implications 76 16 Moving Up the Analytics Maturity Ladder 77 Implications 81 17 The Industrial Revolutions and Data Science 83 The First Industrial Revolution: From Craft to Repetitive Activity 84 The Second Industrial Revolution: The Advent of the Factory 84 The Third Industrial Revolution: Enter the Computer 84 The Fourth Industrial Revolution: The Industry 4.0 Transformation 85 Implications 85 18 Epilogue 87 Strong Foundations 87 A Bridge to the Future 88 Appendix A: Skills of a Data Scientist 91 Appendix B: Data Defined 93 Appendix C: Questions to Help Evaluate the Outputs of Data Science 95 Appendix D: Ethical Considerations and Today's Data Scientist 97 Appendix E: Recent Technical Advances in Data Science 99 References 101 A List of Useful Links 107 Index 111