Getting in Front on Data: Who Does What

Getting in Front on Data: Who Does What

by Thomas C. Redman


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Getting in Front on Data: Who Does What by Thomas C. Redman

This book lays out the roles everyone, up and down the organization chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics.

Product Details

ISBN-13: 9781634621267
Publisher: Technics Publications, LLC
Publication date: 09/30/2016
Pages: 192
Sales rank: 675,034
Product dimensions: 7.40(w) x 9.20(h) x 0.50(d)

Table of Contents

Foreword Thomas H. Davenport 1

Acknowledgements 5

Introduction 7

Chapter 1 Data Quality: Why, How, Who, and When? 13

Why? 13

How and Who? 16

When? 22

In Summary 23

Chapter 2 What's in it for me? 25

Do I Have a Data Quality Problem? 26

The Wrong Reaction in the Face of Bad Data 29

Use the Rule of Ten to Estimate Costs 33

Identify "Hard-to-Quantify" Costs of Special Importance 34

Think Longer-Term 35

So What's in It for Me? 36

In Summary 37

Chapter 3 Data Customers Must Speak Up 39

Recognize That You Are a Data Customer 41

Communicate Your Needs 43

Innovate and Encourage Innovation 48

Actively Manage Both Internal and External Suppliers 49

Make Your Hidden Data Factories Explicit and Efficient 51

Build Organizational Capability 54

In Summary 55

Chapter 4 Part I: Must-Dos for All Data Creators 57

Recognize You Are a Data Creator and That Your Work Impacts Others 59

Focus on the Most Important Needs of the Most Important Customers 60

Measure Quality Against Those Needs, in the Eyes of the Customers 62

Find and Eliminate Root Causes of Error 66

Establish Control 68

Innovate, Innovate, Innovate 72

In Summary 72

Chapter 4 Part II: Process Management Advances Data Creation 75

Manage Data Creation as a Process 76

Clarity Managerial Responsibilities 77

Extend the Voice of the Customer 78

Look for Improvement Opportunities on the Interfaces Between Tasks/Steps 79

Build Organizational Capabilities 81

Employ Embedded Data Managers 83

The Fundamental Organization Unit for Data Quality 84

Special Instructions for Creating Common Data Definitions 85

In Summary 89

Chapter 5 Provocateurs Disrupt Organizational Momentum 91

Provocateurs Look to Improve Their Current Work 91

Provocateurs Dig Deeper 93

Provocateurs Achieve a Real Result 94

Provocateurs Are Not Rabble Rousers 96

Provocateurs Have Courage and Judgment 97

In Summary 100

Chapter 6 Building a Data Quality Team 101

Pay Special Attention to Proprietary Data 104

Focus the Effort 107

Engage Senior Management 111

Connect Data Creators and Data Customers 113

Provide Common Functions Where It Makes Sense 114

Own the Data Definition Processes 116

Actively Manage Change 116

Build Small but Powerful Core Data Quality Teams 118

In Summary 121

Chapter 7 Essential Roles of Senior Management in Getting to the Next Level 123

Understand the Business Case 124

Put the Right People and Structure in Place 128

Insist that the Organization Get in Front on Data Quality 134

Engage Visibly 136

In Summary 137

Chapter 8 Great Data Quality Programs Need Great Tech 139

Store, Move, and Deliver Data Safely and Securely 142

Automate Data Translation as Data Moves From System to System 142

Contribute to the DQ Effort 143

Don't Take Overall Responsibility for Data Quality 144

Use Business Data Definitions in Systems Development 145

Reduce Data Translation by Simplifying the Data Architecture 146

Put in Place a Powerful Team to Support Data Quality 148

In Summary 149

Chapter 9 Data Quality in Practice 151

Improving Access Bill Quality at AT&T 151

Data Definitions That Stand the Test of Time at Aera Energy 165

In Summary 168

Chapter 10 Advancing Data Quality 169

Appendix A Special Instructions for Decision Makers and Data Scientists 171

Appendix B Special Instructions for Automated Measurement, Connected Devices, and the internet of Things 175

Glossary 179

Index 181

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