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

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