Cover image for Data quality for the information age
Title:
Data quality for the information age
Author:
Redman, Thomas C.
Personal Author:
Publication Information:
Boston : Artech House, [1996]

©1996
Physical Description:
xxiv, 303 pages : illustrations ; 24 cm
Language:
English
ISBN:
9780890068830
Format :
Book

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Summary

Summary

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."


Table of Contents

Acknowledgmentsp. xiii
Forewordp. xvii
Prefacep. xxi
Part I

p. 1

Chapter 1 Why Care About Data Quality?p. 3
1.1 Introductionp. 3
1.2 Poor Data Quality Is Pervasivep. 4
1.3 Poor Data Quality Impacts Business Successp. 6
1.3.1 Poor Data Quality Lowers Customer Satisfactionp. 6
1.3.2 Poor Data Quality Leads to High and Unnecessary Costsp. 7
1.3.3 Poor Data Quality Lowers Job Satisfaction and Breeds Organizational Mistrustp. 9
1.3.4 Poor Data Quality Impacts Decision Makingp. 9
1.3.5 Poor Data Quality Impedes Re-engineeringp. 10
1.3.6 Poor Data Quality Hinders Long-Term Business Strategyp. 11
1.3.7 Data Fill the White Space on the Organization Chartp. 11
1.3.8 The Enabling Role of Information Technologyp. 12
1.4 Data Quality Can Be a Unique Source of Competitive Advantagep. 12
1.5 Summaryp. 13
Referencesp. 14
Chapter 2 Strategies for Improving Data Accuracyp. 17
2.1 Introductionp. 17
2.2 Backgroundp. 19
2.2.1 Quality, Data, and Data Qualityp. 19
2.2.2 Choice 1: Error Detection and Correctionp. 22
2.2.3 Process Control and Improvementp. 25
2.2.4 Process Designp. 27
2.3 Which Data to Improve?p. 27
2.4 Improving Data Accuracy for One Databasep. 29
2.5 Improving Data Accuracy for Two Databasesp. 30
2.6 Improving Data Accuracy in the Data Warehousep. 32
2.7 Summaryp. 33
Referencesp. 34
Chapter 3 Data Quality Policyp. 37
3.1 Introductionp. 37
3.2 What Should a Data Policy Cover?p. 38
3.2.1 The Data Asset in a Typical Enterprisep. 38
3.2.2 What a Data Policy Can Coverp. 40
3.3 Needed Background on Datap. 41
3.3.1 Differences Between Data and Other Assetsp. 41
3.3.2 Who Uses the Datap. 44
3.4 A Model Data Policyp. 46
3.4.1 Model Data Policyp. 47
3.5 Deploying the Policyp. 49
3.6 Summaryp. 52
Referencesp. 53
Chapter 4 Starting and Nurturing a Data Quality Programp. 55
4.1 Introductionp. 55
4.2 A Model for Successful Changep. 58
4.2.1 Pressure for Changep. 58
4.2.2 Clear, Shared Visionp. 59
4.2.3 Capacity for Changep. 60
4.2.4 Actionable First Stepsp. 61
4.3 Getting Startedp. 61
4.4 Growth Stagesp. 63
4.5 Becoming Part of the Mainstreamp. 64
4.6 The Role of Senior Managementp. 66
4.7 Summaryp. 67
Referencesp. 67
Chapter 5 Data Quality and Re-engineering at ATandTp. 69
5.1 Introductionp. 69
5.2 Backgroundp. 70
5.3 First Stepsp. 73
5.3.1 Improve Bill Verificationp. 73
5.3.2 Prototype with Cincinnati Bellp. 77
5.4 Re-engineeringp. 77
5.4.1 Business Directionp. 78
5.4.2 Program Administrationp. 79
5.4.3 Management Responsibilitiesp. 80
5.4.4 Operational Plan for Improvementp. 81
5.5 Summaryp. 83
Referencesp. 84
Chapter 6 Data Quality Across the Corporation: Telstra's Experiencesp. 85
6.1 Introductionp. 85
6.2 Program Definitionp. 87
6.3 First Stepsp. 89
6.4 Full Programp. 90
6.5 Resultsp. 94
6.6 Summaryp. 95
Referencesp. 96
Part II

p. 97

Chapter 7 Managing Information Chainsp. 99
7.1 Introductionp. 99
7.2 Future Performance of Processesp. 104
7.2.1 Step 1: Establish a Process Owner and Management Teamp. 105
7.2.2 Step 2: Describe the Process and Understand Customer Needsp. 107
7.2.3 Step 3: Establish a Measurement Systemp. 110
7.2.4 Step 4: Establish Statistical Control and Check Conformance to Requirementsp. 111
7.2.5 Step 5: Identify Improvement Opportunitiesp. 112
7.2.6 Step 6: Select Opportunitiesp. 113
7.2.7 Step 7: Make and Sustain Improvementsp. 114
7.3 Summaryp. 117
Referencesp. 118
Chapter 8 Process Representation and the Functions of Information Processing Approachp. 119
8.1 Introductionp. 119
8.2 Basic Ideasp. 120
8.3 The Information Model/The FIP Chartp. 122
8.3.1 The FIP Rowp. 122
8.3.2 The Process Instruction Rowp. 123
8.3.3 The IIPs/OIPs Rowsp. 124
8.3.4 The Physical Devices Rowp. 125
8.3.5 The Person/Organization Rowp. 125
8.3.6 An Example--an Employee Movep. 125
8.4 Enhancements to the Basic Information Modelp. 129
8.4.1 Pictorial Representationp. 130
8.4.2 Exception, Alternative, and Parallel Processesp. 131
8.5 Measurement and Improvement Opportunitiesp. 134
8.5.1 Accuracyp. 134
8.5.2 Timelinessp. 134
8.5.3 Cues for Improvementp. 134
8.6 Summaryp. 136
Referencesp. 137
Chapter 9 Data Quality Requirementsp. 139
9.1 Introductionp. 139
9.2 Quality Function Deploymentp. 140
9.3 Data Quality Requirements for an Existing Information Chainp. 141
9.3.1 Step 1: Understand Customers' Requirementsp. 142
9.3.2 Step 2: Develop a Set of Consistent Customer Requirementsp. 142
9.3.3 Step 3: Translate Customer Requirements into Technical Languagep. 145
9.3.4 Step 4: Map Data Quality Requirements into Individual Performance Requirementsp. 146
9.3.5 Step 5: Establish Performance Specifications for Processesp. 148
9.3.6 Summary Remarksp. 148
9.4 Data Quality Requirements at the Design Stagep. 149
9.4.1 Background and Motivationp. 149
9.4.2 The Complete Job--the Entire Data Life Cyclep. 150
9.4.3 The Methodology Applied at the Design Stagep. 151
9.5 Summaryp. 152
Referencesp. 154
Chapter 10 Statistical Quality Controlp. 155
10.1 Introductionp. 155
10.2 Variationp. 158
10.2.1 Sources of Variationp. 159
10.3 Stable Processesp. 162
10.3.1 Judgment of Stabilityp. 164
10.4 Control Limits: Statistical Theory and Methods of SQCp. 165
10.4.1 The Underlying Theoryp. 165
10.4.2 Formulaep. 167
10.5 Interpreting Control Chartsp. 174
10.6 Conformance to Requirementsp. 181
10.7 Summaryp. 181
10.8 Notes on Referencesp. 182
Referencesp. 182
Chapter 11 Measurement Systems, Data Tracking, and Process Improvementp. 185
11.1 Introductionp. 185
11.2 Measurement Systemsp. 186
11.3 Process Requirementsp. 189
11.4 What to Measurep. 190
11.5 The Measuring Device and Protocol: Data Trackingp. 191
11.5.1 Philosophyp. 191
11.5.2 Step 1: Samplingp. 193
11.5.3 Step 2: Trackingp. 194
11.5.4 Step 3: Identify Errors and Calculate Process Cycle Timesp. 194
11.5.5 Step 4: Summarize Resultsp. 196
11.6 Implementationp. 209
11.7 Summaryp. 211
Referencesp. 212
Part III

p. 213

Chapter 12 Just What Is (or Are) Data?p. 215
12.1 Introductionp. 215
12.2 The Data Life Cyclep. 217
12.2.1 Preliminariesp. 218
12.2.2 Acquisition Cyclep. 219
12.2.3 Usage Cyclep. 222
12.2.4 Checkpoints, Feedback Loops, and Data Destructionp. 224
12.2.5 Discussionp. 225
12.3 Data Definedp. 227
12.3.1 Preliminariesp. 227
12.3.2 Competing Definitionsp. 227
12.3.3 A Set of Factsp. 228
12.3.4 The Result of Measurementp. 228
12.3.5 Raw Material for Informationp. 228
12.3.6 Surrogates for Real-World Objectsp. 229
12.3.7 Representable Triplesp. 229
12.3.8 Discussionp. 230
12.4 Management Properties of Datap. 232
12.4.1 How Data Differ From Other Resourcesp. 233
12.4.2 Implications for Data Qualityp. 235
12.5 A Model of an Enterprise's Data Resourcep. 236
12.6 Informationp. 237
12.7 Summaryp. 239
Referencesp. 240
Chapter 13 Dimensions of Data Qualityp. 245
13.1 Introductionp. 245
13.2 Quality Dimensions of a Conceptual Viewp. 246
13.2.1 Contentp. 248
13.2.2 Scopep. 249
13.2.3 Level of Detailp. 249
13.2.4 Compositionp. 250
13.2.5 View Consistencyp. 252
13.2.6 Reaction to Changep. 252
13.3 Quality Dimensions of Data Valuesp. 254
13.3.1 Accuracyp. 255
13.3.2 Completenessp. 256
13.3.3 Currency and Related Dimensionsp. 258
13.3.4 Value Consistencyp. 259
13.4 Quality Dimensions of Data Representationp. 260
13.4.1 Appropriatenessp. 261
13.4.2 Interpretabilityp. 261
13.4.3 Portabilityp. 262
13.4.4 Format Precisionp. 262
13.4.5 Format Flexibilityp. 262
13.4.6 Ability to Represent Null Valuesp. 262
13.4.7 Efficient Usage of Recording Mediap. 263
13.4.8 Representation Consistencyp. 263
13.5 More on Data Consistencyp. 263
13.6 Summaryp. 266
Referencesp. 267
Part IV

p. 271

Chapter 14 Summary: Roles and Responsibilitiesp. 273
14.1 Introductionp. 273
14.2 Roles for Leadersp. 274
14.3 Roles for Process Ownersp. 277
14.4 Roles for Information Professionalsp. 281
14.4.1 Design Principle: Process Managementp. 283
14.4.2 Design Principle: Measurement Systemsp. 284
14.4.3 Design Principle: Data Architecturep. 284
14.4.4 Design Principle: Cycle Timep. 285
14.4.5 Design Principle: Data Valuesp. 285
14.4.6 Design Principle: Redundancy in Data Storagep. 285
14.4.7 Design Principle: Computerizationp. 286
14.4.8 Design Principle: Data Transformations and Transcriptionp. 286
14.4.9 Design Principle: Value Creationp. 286
14.4.10 Design Principle: Data Destructionp. 287
14.4.11 Design Principle: Editingp. 287
14.4.12 Design Principle: Codingp. 287
14.4.13 Design Principle: Single-Fact Datap. 288
14.4.14 Design Principle: Data Dictionariesp. 288
14.5 Final Remarks--The Three Most Important Pointsp. 288
Glossaryp. 289
About the Authorp. 295
Indexp. 297