Page 9 - 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

xii Contents

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