Data Quality For The Information Age / Edition 1

by Thomas C Redman, A Blanton Godfrey

Overview

This informative book goes beyond the technical aspects of data management to provide detailed analyses of quality problems and their impacts, potential solutions and how they are combined to form an overall data quality program, senior management's role, methods used to make improvements, and the life-cycle of data quality. It concludes with case studies, summaries of main points, roles and responsibilities for each individual, and a helpful listing of "dos and don'ts".

Product Details

ISBN-13:	9780890068830
Publisher:	Artech House, Incorporated
Publication date:	01/01/1997
Pages:	332
Product dimensions:	6.00(w) x 9.00(h) x 0.88(d)

About the Author

Dr. Thomas C. Redman is president of the Navesink Consulting Group. He led data quality programs at AT&T and AT&T Bell Labs. He holds a Ph.D. and M.S. in statistics from Florida State University. He is a member of the American Statistical Association, holder of a patent, and is published extensively.

Table of Contents

	Acknowledgments	xiii
	Foreword	xvii
	Preface	xxi
Part I1
Chapter 1	Why Care About Data Quality?	3
1.1	Introduction	3
1.2	Poor Data Quality Is Pervasive	4
1.3	Poor Data Quality Impacts Business Success	6
1.3.1	Poor Data Quality Lowers Customer Satisfaction	6
1.3.2	Poor Data Quality Leads to High and Unnecessary Costs	7
1.3.3	Poor Data Quality Lowers Job Satisfaction and Breeds Organizational Mistrust	9
1.3.4	Poor Data Quality Impacts Decision Making	9
1.3.5	Poor Data Quality Impedes Re-engineering	10
1.3.6	Poor Data Quality Hinders Long-Term Business Strategy	11
1.3.7	Data Fill the White Space on the Organization Chart	11
1.3.8	The Enabling Role of Information Technology	12
1.4	Data Quality Can Be a Unique Source of Competitive Advantage	12
1.5	Summary	13
	References	14
Chapter 2	Strategies for Improving Data Accuracy	17
2.1	Introduction	17
2.2	Background	19
2.2.1	Quality, Data, and Data Quality	19
2.2.2	Choice 1: Error Detection and Correction	22
2.2.3	Process Control and Improvement	25
2.2.4	Process Design	27
2.3	Which Data to Improve?	27
2.4	Improving Data Accuracy for One Database	29
2.5	Improving Data Accuracy for Two Databases	30
2.6	Improving Data Accuracy in the Data Warehouse	32
2.7	Summary	33
	References	34
Chapter 3	Data Quality Policy	37
3.1	Introduction	37
3.2	What Should a Data Policy Cover?	38
3.2.1	The Data Asset in a Typical Enterprise	38
3.2.2	What a Data Policy Can Cover	40
3.3	Needed Background on Data	41
3.3.1	Differences Between Data and Other Assets	41
3.3.2	Who Uses the Data	44
3.4	A Model Data Policy	46
3.4.1	Model Data Policy	47
3.5	Deploying the Policy	49
3.6	Summary	52
	References	53
Chapter 4	Starting and Nurturing a Data Quality Program	55
4.1	Introduction	55
4.2	A Model for Successful Change	58
4.2.1	Pressure for Change	58
4.2.2	Clear, Shared Vision	59
4.2.3	Capacity for Change	60
4.2.4	Actionable First Steps	61
4.3	Getting Started	61
4.4	Growth Stages	63
4.5	Becoming Part of the Mainstream	64
4.6	The Role of Senior Management	66
4.7	Summary	67
	References	67
Chapter 5	Data Quality and Re-engineering at AT&T	69
5.1	Introduction	69
5.2	Background	70
5.3	First Steps	73
5.3.1	Improve Bill Verification	73
5.3.2	Prototype with Cincinnati Bell	77
5.4	Re-engineering	77
5.4.1	Business Direction	78
5.4.2	Program Administration	79
5.4.3	Management Responsibilities	80
5.4.4	Operational Plan for Improvement	81
5.5	Summary	83
	References	84
Chapter 6	Data Quality Across the Corporation: Telstra's Experiences	85
6.1	Introduction	85
6.2	Program Definition	87
6.3	First Steps	89
6.4	Full Program	90
6.5	Results	94
6.6	Summary	95
	References	96
Part II97
Chapter 7	Managing Information Chains	99
7.1	Introduction	99
7.2	Future Performance of Processes	104
7.2.1	Step 1: Establish a Process Owner and Management Team	105
7.2.2	Step 2: Describe the Process and Understand Customer Needs	107
7.2.3	Step 3: Establish a Measurement System	110
7.2.4	Step 4: Establish Statistical Control and Check Conformance to Requirements	111
7.2.5	Step 5: Identify Improvement Opportunities	112
7.2.6	Step 6: Select Opportunities	113
7.2.7	Step 7: Make and Sustain Improvements	114
7.3	Summary	117
	References	118
Chapter 8	Process Representation and the Functions of Information Processing Approach	119
8.1	Introduction	119
8.2	Basic Ideas	120
8.3	The Information Model/The FIP Chart	122
8.3.1	The FIP Row	122
8.3.2	The Process Instruction Row	123
8.3.3	The IIPs/OIPs Rows	124
8.3.4	The Physical Devices Row	125
8.3.5	The Person/Organization Row	125
8.3.6	An Example--an Employee Move	125
8.4	Enhancements to the Basic Information Model	129
8.4.1	Pictorial Representation	130
8.4.2	Exception, Alternative, and Parallel Processes	131
8.5	Measurement and Improvement Opportunities	134
8.5.1	Accuracy	134
8.5.2	Timeliness	134
8.5.3	Cues for Improvement	134
8.6	Summary	136
	References	137
Chapter 9	Data Quality Requirements	139
9.1	Introduction	139
9.2	Quality Function Deployment	140
9.3	Data Quality Requirements for an Existing Information Chain	141
9.3.1	Step 1: Understand Customers' Requirements	142
9.3.2	Step 2: Develop a Set of Consistent Customer Requirements	142
9.3.3	Step 3: Translate Customer Requirements into Technical Language	145
9.3.4	Step 4: Map Data Quality Requirements into Individual Performance Requirements	146
9.3.5	Step 5: Establish Performance Specifications for Processes	148
9.3.6	Summary Remarks	148
9.4	Data Quality Requirements at the Design Stage	149
9.4.1	Background and Motivation	149
9.4.2	The Complete Job--the Entire Data Life Cycle	150
9.4.3	The Methodology Applied at the Design Stage	151
9.5	Summary	152
	References	154
Chapter 10	Statistical Quality Control	155
10.1	Introduction	155
10.2	Variation	158
10.2.1	Sources of Variation	159
10.3	Stable Processes	162
10.3.1	Judgment of Stability	164
10.4	Control Limits: Statistical Theory and Methods of SQC	165
10.4.1	The Underlying Theory	165
10.4.2	Formulae	167
10.5	Interpreting Control Charts	174
10.6	Conformance to Requirements	181
10.7	Summary	181
10.8	Notes on References	182
	References	182
Chapter 11	Measurement Systems, Data Tracking, and Process Improvement	185
11.1	Introduction	185
11.2	Measurement Systems	186
11.3	Process Requirements	189
11.4	What to Measure	190
11.5	The Measuring Device and Protocol: Data Tracking	191
11.5.1	Philosophy	191
11.5.2	Step 1: Sampling	193
11.5.3	Step 2: Tracking	194
11.5.4	Step 3: Identify Errors and Calculate Process Cycle Times	194
11.5.5	Step 4: Summarize Results	196
11.6	Implementation	209
11.7	Summary	211
	References	212
Part III213
Chapter 12	Just What Is (or Are) Data?	215
12.1	Introduction	215
12.2	The Data Life Cycle	217
12.2.1	Preliminaries	218
12.2.2	Acquisition Cycle	219
12.2.3	Usage Cycle	222
12.2.4	Checkpoints, Feedback Loops, and Data Destruction	224
12.2.5	Discussion	225
12.3	Data Defined	227
12.3.1	Preliminaries	227
12.3.2	Competing Definitions	227
12.3.3	A Set of Facts	228
12.3.4	The Result of Measurement	228
12.3.5	Raw Material for Information	228
12.3.6	Surrogates for Real-World Objects	229
12.3.7	Representable Triples	229
12.3.8	Discussion	230
12.4	Management Properties of Data	232
12.4.1	How Data Differ From Other Resources	233
12.4.2	Implications for Data Quality	235
12.5	A Model of an Enterprise's Data Resource	236
12.6	Information	237
12.7	Summary	239
	References	240
Chapter 13	Dimensions of Data Quality	245
13.1	Introduction	245
13.2	Quality Dimensions of a Conceptual View	246
13.2.1	Content	248
13.2.2	Scope	249
13.2.3	Level of Detail	249
13.2.4	Composition	250
13.2.5	View Consistency	252
13.2.6	Reaction to Change	252
13.3	Quality Dimensions of Data Values	254
13.3.1	Accuracy	255
13.3.2	Completeness	256
13.3.3	Currency and Related Dimensions	258
13.3.4	Value Consistency	259
13.4	Quality Dimensions of Data Representation	260
13.4.1	Appropriateness	261
13.4.2	Interpretability	261
13.4.3	Portability	262
13.4.4	Format Precision	262
13.4.5	Format Flexibility	262
13.4.6	Ability to Represent Null Values	262
13.4.7	Efficient Usage of Recording Media	263
13.4.8	Representation Consistency	263
13.5	More on Data Consistency	263
13.6	Summary	266
	References	267
Part IV271
Chapter 14	Summary: Roles and Responsibilities	273
14.1	Introduction	273
14.2	Roles for Leaders	274
14.3	Roles for Process Owners	277
14.4	Roles for Information Professionals	281
14.4.1	Design Principle: Process Management	283
14.4.2	Design Principle: Measurement Systems	284
14.4.3	Design Principle: Data Architecture	284
14.4.4	Design Principle: Cycle Time	285
14.4.5	Design Principle: Data Values	285
14.4.6	Design Principle: Redundancy in Data Storage	285
14.4.7	Design Principle: Computerization	286
14.4.8	Design Principle: Data Transformations and Transcription	286
14.4.9	Design Principle: Value Creation	286
14.4.10	Design Principle: Data Destruction	287
14.4.11	Design Principle: Editing	287
14.4.12	Design Principle: Coding	287
14.4.13	Design Principle: Single-Fact Data	288
14.4.14	Design Principle: Data Dictionaries	288
14.5	Final Remarks--The Three Most Important Points	288
	Glossary	289
	About the Author	295
	Index	297

Editorial Reviews

Provides business leaders, process owners, and information professionals with background and information for setting up a data quality program, making and sustaining order-of-magnitude improvements, and creating a sneaky business advantage. Discusses problems of data quality and their impact, potential solutions and how they dovetail into an overall program, the role of senior management, and life cycles and dimensions of data quality. Includes a glossary without pronunciation. Annotation c. by Book News, Inc., Portland, Or.

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