The Real Work of Data Science: Turning Data Into Information, Better Decisions, and Stronger Organizations

ISBN-13: 9781119570707 / Angielski / Miękka / 2019 / 144 str.

Ron S. Kenett; Thomas C. Redman

Spis treści

About the Authors xvPreface xviiAbout the Companion Website xxi1 A Higher Calling 1The Life-Cycle View 2Problem Elicitation: Understand the Problem 3Goal Formulation: Clarify the Short-term and Long-term Goals 3Data Collection: Identify Relevant Data Sources and Collect the Data 3Data Analysis: Use Descriptive, Explanatory, and Predictive Methods 3Formulation of Findings: State Results and Recommendations 4Operationalization of Findings: Suggest Who, What, When, and How 5Communication of Findings: Communicate Findings, Decisions, and Their Implications to Stakeholders 5Impact Assessment: Plan and Deploy an Assessment Strategy 5The Organizational Ecosystem 6Organizational Structure 6Organizational Maturity 6Once Again, Our Goal 62 The Difference Between a Good Data Scientist and a Great One 9Implications 113 Learn the Business 13The Annual Report 13SWOTs and Strategic Analysis 13The Balanced Scorecard and Key Performance Indicators 14The Data Lens 15Build Your Network 16Implications 164 Understand the Real Problem 17A Telling Example 17Understanding the Real Problem 18Implications 195 Get Out There 21Understand Context and Soft Data 21Identify Sources of Variability 22Selective Attention 23Memory Bias 23Implications 236 Sorry, but You Can't Trust the Data 25Most Data Is Untrustworthy 25Dealing with Immediate Issues 27Getting in Front of Tomorrow's Data Quality Issues 29Implications 307 Make It Easy for People to Understand Your Insights 31First, Get the Basics Right 31Presentations Get Passed Around 33The Best of the Best 34Implications 348 When the Data Leaves Off and Your Intuition Takes Over 35Modes of Generalization 36Implications 389 Take Accountability for Results 39Practical Statistical Efficiency 39Using Data Science to Perform Impact Analysis 41Implications 4210 What It Means to Be "Data-driven" 43Data-driven Companies and People 43Traits of the Data-driven 44Traits of the Antis 46Implications 4611 Root Out Bias in Decision-making 49Understand Why It Occurs 50Take Control on a Personal Level 50Solid Scientific Footings 51Problem 1 52Problem 2 52Implications 5312 Teach, Teach, Teach 55The Rope Exercise 55The "Roll Your Own" Exercise 56The Starter Kit of Questions to Ask Data Scientists 59Implications 6013 Evaluating Data Science Outputs More Formally 63Assessing Information Quality 63A Hands-On Information Quality Workshop 64Phase I: Individual Work 64Phase II: Teamwork 65Phase III: Group Presentation 66Implications 6614 Educating Senior Leaders 67Covering the Waterfront 68Companies Need a Data and Data Science Strategy 70Organizations Are "Unfit for Data" 71Get Started with Data Quality 71Implications 7115 Putting Data Science, and Data Scientists, in the Right Spots 73The Need for Senior Leadership 73Building a Network of Data Scientists 74Implications 7616 Moving Up the Analytics Maturity Ladder 77Implications 8117 The Industrial Revolutions and Data Science 83The First Industrial Revolution: From Craft to Repetitive Activity 84The Second Industrial Revolution: The Advent of the Factory 84The Third Industrial Revolution: Enter the Computer 84The Fourth Industrial Revolution: The Industry 4.0 Transformation 85Implications 8518 Epilogue 87Strong Foundations 87A Bridge to the Future 88Appendix A: Skills of a Data Scientist 91Appendix B: Data Defined 93Appendix C: Questions to Help Evaluate the Outputs of Data Science 95Appendix D: Ethical Considerations and Today's Data Scientist 97Appendix E: Recent Technical Advances in Data Science 99References 101A List of Useful Links 107Index 111

O autorze

RON S. KENETT is Chairman of the KPA Group, Israel, Senior Research Fellow at the Samuel Neaman Institute, Technion, Haifa and, previously, Professor of Operations Management, State University of New York, Binghamton, New York and President of the European Network for Business and Industrial Statistics.THOMAS C. REDMAN, "the Data Doc," is the President of Data Quality Solutions. He helps leaders and companies understand their most important issues and opportunities in the data, chart a course, and build the organizational capabilities they need to execute.