Data quality for the information age:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston
Artech House
1996
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIV, 303 Seiten Illustrationen, Diagramme |
| ISBN: | 0890068836 |
Internformat
MARC
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| 100 | 1 | |a Redman, Thomas C. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Data quality for the information age |c Thomas C. Redman |
| 264 | 1 | |a Boston |b Artech House |c 1996 | |
| 300 | |a XXIV, 303 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 2 | |a Bases de données comme sujet | |
| 650 | 2 | |a Contrôle qualité | |
| 650 | 7 | |a Databanken |2 gtt | |
| 650 | 7 | |a Kwaliteitscontrole |2 gtt | |
| 650 | 4 | |a Business |x Data processing |x Management | |
| 650 | 4 | |a Database design | |
| 650 | 4 | |a Database management |x Quality control | |
| 650 | 0 | 7 | |a Datenbankverwaltung |0 (DE-588)4389357-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Qualitätsmanagement |0 (DE-588)4219057-5 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Qualitätsmanagement |0 (DE-588)4219057-5 |D s |
| 689 | 0 | |5 DE-604 | |
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Datensatz im Suchindex
| _version_ | 1804126046283366400 |
|---|---|
| adam_text | Contents
Acknowledgments xiii
Foreword xvii
Preface xxi
Parti 1
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
vii
viii Data Quality for the Informai
1.4 Data Quality Can Be a
Unique Source of Competitive
Advantage 12
1.5 Summary 13
ileferences 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
noN Age
3.2.2 What a Data Policy
Can Cover 40
3.3 Needed Background on Data 41
3.3.1 Differences Between Data and
OtherAssets 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 II 97
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
Contents ix
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
x Data Quality for the Information
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
n Age
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 III 213
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 Cyck 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 A 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
Contents xi
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
xii Data Quality for the Informatioi
Part IV 271
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
Datastorage 285
v Age
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 Fad 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
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| language | English |
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| spelling | Redman, Thomas C. Verfasser aut Data quality for the information age Thomas C. Redman Boston Artech House 1996 XXIV, 303 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Bases de données comme sujet Contrôle qualité Databanken gtt Kwaliteitscontrole gtt Business Data processing Management Database design Database management Quality control Datenbankverwaltung (DE-588)4389357-0 gnd rswk-swf Qualitätsmanagement (DE-588)4219057-5 gnd rswk-swf Datenbankverwaltung (DE-588)4389357-0 s Qualitätsmanagement (DE-588)4219057-5 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007756348&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Redman, Thomas C. Data quality for the information age Bases de données comme sujet Contrôle qualité Databanken gtt Kwaliteitscontrole gtt Business Data processing Management Database design Database management Quality control Datenbankverwaltung (DE-588)4389357-0 gnd Qualitätsmanagement (DE-588)4219057-5 gnd |
| subject_GND | (DE-588)4389357-0 (DE-588)4219057-5 |
| title | Data quality for the information age |
| title_auth | Data quality for the information age |
| title_exact_search | Data quality for the information age |
| title_full | Data quality for the information age Thomas C. Redman |
| title_fullStr | Data quality for the information age Thomas C. Redman |
| title_full_unstemmed | Data quality for the information age Thomas C. Redman |
| title_short | Data quality for the information age |
| title_sort | data quality for the information age |
| topic | Bases de données comme sujet Contrôle qualité Databanken gtt Kwaliteitscontrole gtt Business Data processing Management Database design Database management Quality control Datenbankverwaltung (DE-588)4389357-0 gnd Qualitätsmanagement (DE-588)4219057-5 gnd |
| topic_facet | Bases de données comme sujet Contrôle qualité Databanken Kwaliteitscontrole Business Data processing Management Database design Database management Quality control Datenbankverwaltung Qualitätsmanagement |
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