Page 10 - The Real Work Of Data Science Turning Data Into Information, Better Decisions, And Stronger Organizations by Ron S. Kenett, Thomas C. Redman (z-lib.org)_Neat
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Contents xiii
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
