Data Quality For The Information Age / Edition 1

Data Quality For The Information Age / Edition 1

by Thomas C. Redman
     
 

ISBN-10: 0890068836

ISBN-13: 9780890068830

Pub. Date: 01/01/1997

Publisher: Artech House, Incorporated

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

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)

Table of Contents

Acknowledgmentsxiii
Forewordxvii
Prefacexxi
Part I1
Chapter 1Why Care About Data Quality?3
1.1Introduction3
1.2Poor Data Quality Is Pervasive4
1.3Poor Data Quality Impacts Business Success6
1.3.1Poor Data Quality Lowers Customer Satisfaction6
1.3.2Poor Data Quality Leads to High and Unnecessary Costs7
1.3.3Poor Data Quality Lowers Job Satisfaction and Breeds Organizational Mistrust9
1.3.4Poor Data Quality Impacts Decision Making9
1.3.5Poor Data Quality Impedes Re-engineering10
1.3.6Poor Data Quality Hinders Long-Term Business Strategy11
1.3.7Data Fill the White Space on the Organization Chart11
1.3.8The Enabling Role of Information Technology12
1.4Data Quality Can Be a Unique Source of Competitive Advantage12
1.5Summary13
References14
Chapter 2Strategies for Improving Data Accuracy17
2.1Introduction17
2.2Background19
2.2.1Quality, Data, and Data Quality19
2.2.2Choice 1: Error Detection and Correction22
2.2.3Process Control and Improvement25
2.2.4Process Design27
2.3Which Data to Improve?27
2.4Improving Data Accuracy for One Database29
2.5Improving Data Accuracy for Two Databases30
2.6Improving Data Accuracy in the Data Warehouse32
2.7Summary33
References34
Chapter 3Data Quality Policy37
3.1Introduction37
3.2What Should a Data Policy Cover?38
3.2.1The Data Asset in a Typical Enterprise38
3.2.2What a Data Policy Can Cover40
3.3Needed Background on Data41
3.3.1Differences Between Data and Other Assets41
3.3.2Who Uses the Data44
3.4A Model Data Policy46
3.4.1Model Data Policy47
3.5Deploying the Policy49
3.6Summary52
References53
Chapter 4Starting and Nurturing a Data Quality Program55
4.1Introduction55
4.2A Model for Successful Change58
4.2.1Pressure for Change58
4.2.2Clear, Shared Vision59
4.2.3Capacity for Change60
4.2.4Actionable First Steps61
4.3Getting Started61
4.4Growth Stages63
4.5Becoming Part of the Mainstream64
4.6The Role of Senior Management66
4.7Summary67
References67
Chapter 5Data Quality and Re-engineering at AT&T69
5.1Introduction69
5.2Background70
5.3First Steps73
5.3.1Improve Bill Verification73
5.3.2Prototype with Cincinnati Bell77
5.4Re-engineering77
5.4.1Business Direction78
5.4.2Program Administration79
5.4.3Management Responsibilities80
5.4.4Operational Plan for Improvement81
5.5Summary83
References84
Chapter 6Data Quality Across the Corporation: Telstra's Experiences85
6.1Introduction85
6.2Program Definition87
6.3First Steps89
6.4Full Program90
6.5Results94
6.6Summary95
References96
Part II97
Chapter 7Managing Information Chains99
7.1Introduction99
7.2Future Performance of Processes104
7.2.1Step 1: Establish a Process Owner and Management Team105
7.2.2Step 2: Describe the Process and Understand Customer Needs107
7.2.3Step 3: Establish a Measurement System110
7.2.4Step 4: Establish Statistical Control and Check Conformance to Requirements111
7.2.5Step 5: Identify Improvement Opportunities112
7.2.6Step 6: Select Opportunities113
7.2.7Step 7: Make and Sustain Improvements114
7.3Summary117
References118
Chapter 8Process Representation and the Functions of Information Processing Approach119
8.1Introduction119
8.2Basic Ideas120
8.3The Information Model/The FIP Chart122
8.3.1The FIP Row122
8.3.2The Process Instruction Row123
8.3.3The IIPs/OIPs Rows124
8.3.4The Physical Devices Row125
8.3.5The Person/Organization Row125
8.3.6An Example--an Employee Move125
8.4Enhancements to the Basic Information Model129
8.4.1Pictorial Representation130
8.4.2Exception, Alternative, and Parallel Processes131
8.5Measurement and Improvement Opportunities134
8.5.1Accuracy134
8.5.2Timeliness134
8.5.3Cues for Improvement134
8.6Summary136
References137
Chapter 9Data Quality Requirements139
9.1Introduction139
9.2Quality Function Deployment140
9.3Data Quality Requirements for an Existing Information Chain141
9.3.1Step 1: Understand Customers' Requirements142
9.3.2Step 2: Develop a Set of Consistent Customer Requirements142
9.3.3Step 3: Translate Customer Requirements into Technical Language145
9.3.4Step 4: Map Data Quality Requirements into Individual Performance Requirements146
9.3.5Step 5: Establish Performance Specifications for Processes148
9.3.6Summary Remarks148
9.4Data Quality Requirements at the Design Stage149
9.4.1Background and Motivation149
9.4.2The Complete Job--the Entire Data Life Cycle150
9.4.3The Methodology Applied at the Design Stage151
9.5Summary152
References154
Chapter 10Statistical Quality Control155
10.1Introduction155
10.2Variation158
10.2.1Sources of Variation159
10.3Stable Processes162
10.3.1Judgment of Stability164
10.4Control Limits: Statistical Theory and Methods of SQC165
10.4.1The Underlying Theory165
10.4.2Formulae167
10.5Interpreting Control Charts174
10.6Conformance to Requirements181
10.7Summary181
10.8Notes on References182
References182
Chapter 11Measurement Systems, Data Tracking, and Process Improvement185
11.1Introduction185
11.2Measurement Systems186
11.3Process Requirements189
11.4What to Measure190
11.5The Measuring Device and Protocol: Data Tracking191
11.5.1Philosophy191
11.5.2Step 1: Sampling193
11.5.3Step 2: Tracking194
11.5.4Step 3: Identify Errors and Calculate Process Cycle Times194
11.5.5Step 4: Summarize Results196
11.6Implementation209
11.7Summary211
References212
Part III213
Chapter 12Just What Is (or Are) Data?215
12.1Introduction215
12.2The Data Life Cycle217
12.2.1Preliminaries218
12.2.2Acquisition Cycle219
12.2.3Usage Cycle222
12.2.4Checkpoints, Feedback Loops, and Data Destruction224
12.2.5Discussion225
12.3Data Defined227
12.3.1Preliminaries227
12.3.2Competing Definitions227
12.3.3A Set of Facts228
12.3.4The Result of Measurement228
12.3.5Raw Material for Information228
12.3.6Surrogates for Real-World Objects229
12.3.7Representable Triples229
12.3.8Discussion230
12.4Management Properties of Data232
12.4.1How Data Differ From Other Resources233
12.4.2Implications for Data Quality235
12.5A Model of an Enterprise's Data Resource236
12.6Information237
12.7Summary239
References240
Chapter 13Dimensions of Data Quality245
13.1Introduction245
13.2Quality Dimensions of a Conceptual View246
13.2.1Content248
13.2.2Scope249
13.2.3Level of Detail249
13.2.4Composition250
13.2.5View Consistency252
13.2.6Reaction to Change252
13.3Quality Dimensions of Data Values254
13.3.1Accuracy255
13.3.2Completeness256
13.3.3Currency and Related Dimensions258
13.3.4Value Consistency259
13.4Quality Dimensions of Data Representation260
13.4.1Appropriateness261
13.4.2Interpretability261
13.4.3Portability262
13.4.4Format Precision262
13.4.5Format Flexibility262
13.4.6Ability to Represent Null Values262
13.4.7Efficient Usage of Recording Media263
13.4.8Representation Consistency263
13.5More on Data Consistency263
13.6Summary266
References267
Part IV271
Chapter 14Summary: Roles and Responsibilities273
14.1Introduction273
14.2Roles for Leaders274
14.3Roles for Process Owners277
14.4Roles for Information Professionals281
14.4.1Design Principle: Process Management283
14.4.2Design Principle: Measurement Systems284
14.4.3Design Principle: Data Architecture284
14.4.4Design Principle: Cycle Time285
14.4.5Design Principle: Data Values285
14.4.6Design Principle: Redundancy in Data Storage285
14.4.7Design Principle: Computerization286
14.4.8Design Principle: Data Transformations and Transcription286
14.4.9Design Principle: Value Creation286
14.4.10Design Principle: Data Destruction287
14.4.11Design Principle: Editing287
14.4.12Design Principle: Coding287
14.4.13Design Principle: Single-Fact Data288
14.4.14Design Principle: Data Dictionaries288
14.5Final Remarks--The Three Most Important Points288
Glossary289
About the Author295
Index297

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