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The Practitioner's Guide to Data Quality Improvement - Practitioner's Guide to Data Quality Improvement
von: David Loshin
Elsevier Reference Monographs, 2010
ISBN: 9780080920344 , 423 Seiten
Format: PDF, ePUB, OL
Kopierschutz: DRM
Preis: 47,95 EUR
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The Practitioner's Guide to Data Quality Improvement - Practitioner's Guide to Data Quality Improvement
The Practitioner’s Guide to Data Quality Improvement
4
Copyright
5
Contents
6
Foreword
12
Preface
14
Acknowledgments
22
About the Author
24
Chapter 1: Business Impacts of Poor Data Quality
26
1.1. Information Value and Data Quality Improvement
28
1.2. Business Expectations and Data Quality
29
1.3. Qualifying Impacts
30
1.4. Some Examples
32
1.5. More on Impact Classification
36
1.6. Business Impact Analysis
38
1.7. Additional Impact Categories
39
1.8. Impact Taxonomies and Iterative Refinement
40
1.9. Summary: Translating Impact into Performance
41
Chapter 2: The Organizational Data Quality Program
42
2.1. The Virtuous Cycle of Data Quality
42
2.2. Data Quality Processes
44
2.3. Stakeholders and Participants
52
2.4. Data Quality Tools
55
2.5. Summary
59
Chapter 3: Data Quality Maturity
60
3.1. The Data Quality Strategy
60
3.2. A Data Quality Framework
63
3.3. A Data Quality Capability/Maturity Model
67
3.4. Mapping Framework Components to the Maturity Model
69
3.5. Summary
74
Chapter 4: Enterprise Initiative Integration
78
4.1. Planning Initiatives
78
4.2. Framework Initiatives
85
4.3. Operational and Application Initiatives
87
4.4. Scoping Issues
89
4.5. Summary
91
Chapter 5: Developing A Business Case and A Data Quality Road Map
92
5.1. Return on the Data Quality Investment
93
5.2. Developing the Business Case
94
5.3. Finding the Business Impacts
94
5.4. Researching Costs
97
5.5. Correlating Impacts and Causes
98
5.6. The Impact Matrix
99
5.7. Problems, Issues, Causes
100
5.8. Mapping Impacts to Data Flaws
100
5.9. Estimating the Value Gap
101
5.10. Prioritizing Actions
104
5.11. The Data Quality Road Map
106
5.12. Practical Steps for Developing the Road Map
109
5.13. Accountability, Responsibility, and Management
109
5.14. The Life Cycle of the Data Quality Program
111
5.15. Summary
115
Chapter 6: Metrics and Performance Improvement
116
Chapter Outline
116
6.1. Performance-Oriented Data Quality
117
6.2. Developing Data Quality Metrics
118
6.3. Measurement and Key Data Quality Performance Indicators
121
6.4. Statistical Process Control
124
6.5. Control Charts
126
6.6. Kinds of Control Charts
130
6.7. Interpreting Control Charts
134
6.8. Finding Special Causes
136
6.9. Maintaining Control
137
6.10. Summary
137
Chapter 7: Data Governance
140
7.1. The Enterprise Data Quality Forum
141
7.2. The Data Quality Charter
141
7.3. Mission and Guiding Principles
142
7.4. Roles and Responsibilities
143
7.5. Operational Structure
147
7.6. Data Stewardship
147
7.7. Data Quality Validation and Certification
150
7.8. Issues and Resolution
152
7.9. Data Governance and Federated Communities
152
7.10. Summary
153
Chapter 8: Dimensions of Data Quality
154
8.1. What Are Dimensions of Data Quality?
155
8.2. Categorization of Dimensions
156
8.3. Describing Data Quality Dimensions
159
8.4. Intrinsic Dimensions
160
8.5. Contextual
163
8.6. Qualitative Dimensions
167
8.7. Finding Your Own Dimensions
171
8.8. Summary
171
Chapter 9: Data Requirements Analysis
172
9.1. Business Uses of Information and Business Analytics
173
9.2. Business Drivers and Data Dependencies
176
9.3. What Is Data Requirements Analysis?
177
9.4. The Data Requirements Analysis Process
179
9.5. Defining Data Quality Rules
185
9.6. Summary
189
Chapter 10: Metadata and Data Standards
192
10.1. Challenges
193
10.2. Data Standards
194
10.3. Metadata Management
196
10.4. Business Metadata
198
10.5. Reference Metadata
201
10.6. Data Elements
204
10.7. Business Metadata
208
10.8. A Process for Data Harmonization
210
10.9. Summary
214
Chapter 11: Data Quality Assessment
216
11.1. Planning
217
11.2. Business Process Evaluation
219
11.3. Preparation and Data Analysis
222
11.4. Data Profiling and Analysis
224
11.5. Synthesis of Analysis Results
227
11.6. Review with Business Client
230
11.7. Summary Rapid Data Assessment - Tangible Results
231
Chapter 12: Remediation and Improvement Planning
232
12.1. Triage
233
12.2. The Information Flow Map
237
12.3. Root Cause Analysis
240
12.4. Remediation
241
12.5. Execution
243
12.6. Summary
243
Chapter 13: Data Quality Service Level Agreements
244
13.1. Business Drivers and Success Criteria
245
13.2. Identifying Data Quality Rules
248
13.3. Establishing Data Quality Control
252
13.4. The Data Quality Service Level Agreement
253
13.5. Inspection and Monitoring
255
13.6. Data Quality Metrics and a Data Quality Scorecard
257
13.7. Data Quality Incident Reporting and Tracking
257
13.8. Automating the Collection of Metrics
259
13.9. Reporting the Scorecard
260
13.10. Taking Action for Remediation
264
13.11. Summary - Managing Using the Data Quality Scorecard
264
Chapter 14: Data Profiling
266
14.1. Application Contexts for Data Profiling
267
14.2. Data Profiling: Algorithmic Techniques
270
14.3. Data Reverse Engineering
273
14.4. Analyzing Anomalies
274
14.5. Data Quality Rule Discovery
276
14.6. Metadata Compliance and Data Model Integrity
279
14.7. Coordinating the Participants
281
14.8. Selecting a Data Set for Analysis
282
14.9. Summary
284
Chapter 15: Parsing and Standardization
286
15.1. Data Error Paradigms
287
15.2. The Role of Metadata
289
15.3. Tokens: Units of Meaning
291
15.4. Parsing
293
15.5. Standardization
295
15.6. Defining Rules and Recommending Transformations
297
15.7. The Proactive versus Reactive Paradox
300
15.8. Integrating Data Transformations into the Application Framework
302
15.9. Summary
302
Chapter 16: Entity Identity Resolution
304
16.1. The Lure of Data Correction
305
16.2. The Dual Challenge of Unique Identity
306
16.3. What Is an Entity?
307
16.4. Identifying Attributes
308
16.5. Similarity Analysis and the Matching Process
310
16.6. Matching Algorithms
311
16.7. False Positives, False Negatives, and Thresholding
314
16.8. Survivorship
316
16.9. Monitoring Linkage and Survivorship
318
16.10. Entity Search and Match and Computational Complexity
318
16.11. Applications of Identity Resolution
319
16.12. Evaluating Business Needs
321
16.13. Summary
321
Chapter 17: Inspection, Monitoring, Auditing, and Tracking
324
17.1. The Data Quality Service Level Agreement Revisited
325
17.2. Instituting Inspection and Monitoring: Technology and Process
325
17.3. Data Quality Business Rules
329
17.4. Automating Inspection and Monitoring
332
17.5. Incident Reporting, Notifications, and Issue Management
334
17.6. Putting It Together
337
Chapter 18: Data Enhancement
338
18.1. The Value of Enhancement
339
18.2. Approaches to Data Enhancement
340
18.3. Examples of Data Enhancement
341
18.4. Enhancement through Standardization
344
18.5. Enhancement through Context
345
18.6. Enhancement through Data Merging
346
18.7. Summary: Qualifying Data Sources for Enhancement
349
Chapter 19: Master Data Management and Data Quality
352
19.1. What Is Master Data?
353
19.2. What Is Master Data Management?
355
19.3. "Golden Record"´ or "Unified View"?
356
19.4. Master Data Management as a Tool
357
19.5. MDM: A High-Level Component Approach
358
19.6. Master Data Usage Scenarios
361
19.7. Master Data Management Architectures
364
19.8. Identifying Master Data
368
19.9. Master Data Services
369
19.10. Summary: Approaching MDM and Data Quality
374
Chapter 20: Bringing it All Together
376
20.1. Organization and Management
376
20.2. Building the Information Quality Program
385
20.3. Techniques and Tools
398
20.4. Summary
408
Index
410
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