Data Quality: The Field Guide

Front Cover
Digital Press, 2001 - Computers - 241 pages
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Can any subject inspire less excitement than "data quality"? Yet a moment's thought reveals the ever-growing importance of quality data. From restated corporate earnings, to incorrect prices on the web, to the bombing of the Chinese Embassy, the media reports the impact of poor data quality on a daily basis. Every business operation creates or consumes huge quantities of data. If the data are wrong, time, money, and reputation are lost. In today's environment, every leader, every decision maker, every operational manager, every consumer, indeed everyone has a vested interest in data quality.

Data Quality: The Field Guide provides the practical guidance needed to start and advance a data quality program. It motivates interest in data quality, describes the most important data quality problems facing the typical organization, and outlines what an organization must do to improve. It consists of 36 short chapters in an easy-to-use field guide format. Each chapter describes a single issue and how to address it.




The book begins with sections that describe why leaders, whether CIOs, CFOs, or CEOs, should be concerned with data quality. It explains the pros and cons of approaches for addressing the issue. It explains what those organizations with the best data do. And it lays bare the social issues that prevent organizations from making headway. "Field tips" at the end of each chapter summarize the most important points.

Allows readers to go directly to the topic of interest
Provides web-based material so readers can cut and paste figures and tables into documents within their organizations
Gives step-by-step instructions for applying most techniques and summarizes what "works"
  

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Contents

Who Cares About Data Quality?
1
Yes Millie Even CEOs Are Interested in Data Quality
3
Internet Users Wonder Are These Prices Correct? and DotComs Better Make Sure They Are
7
Chief Financial Officers and Managers of Ongoing Operations Need to Know Where the Money Is
15
Marketers Need to Know about Their Customers
21
Chief Information Officers Are Stuck in the Middle
27
Just in Case You Didnt See Yourself in Chapters 15
35
The Business Case for Data Quality
37
Statistical Control Establishing a Basis for Prediction
123
Quality Improvement Root Cause Analysis to Uncover the Real Causes of Error
131
Quality Planning Setting Targets for Improvement
137
Quality Planning Designing New Information Chains
141
A Note on Reengineering
147
Middle Management Roles and Responsibilities
151
Data Supplier Management
153
Managing Information Chains
161

Disasters Played Out in Public
39
Poor Data Quality Can Be Insidious
43
Seek Competitive Advantage Through Quality Data
47
The Heart of the Matter
51
A Database Is Like a Lake
53
Likely Outcomes
57
The Organic Nature of Data
61
Crafting the Approach
65
Necessary Background
69
Data and Data Quality Defined
71
SecondGeneration Data Quality Systems
75
The CustomerSupplier Model
95
Blocking and Tackling
99
Understanding Customer Needs After All They Are the Final Arbiters of Quality
101
Better Faster Cheaper
107
Measurement 2 Data Tracking
113
Edit Controls
119
Making Better Decisions
167
Tools
171
Why Senior Management Must Lead and What It Must Do
175
Senior Leadership and Support
177
Crafting a Data Policy
181
Organizing for Data Quality
187
Recognizing Social Issues
189
Advancing the Data Culture
197
Summaries
203
On and Just Over the Horizon
205
Field Tips Reorganized
209
The United States Elections of 2000
217
Glossary
221
References
229
Instructions for Downloading Figures and Tables
231
Index
233
Copyright

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About the author (2001)

Redman is President of the Navesink Consulting Group, based in Rumson, New Jersey. Prior to forming Navesink in 1995, he led data work in AT&T's Chief Information Office, where he was responsible for defining and coordinating AT&T's data program. Prior to beginning to work on data quality in 1987, he worked at Bell Labs and Bell Communications Research on network performance. He holds a Ph.D. in Statistics from Florida State University. He holds one patent.

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