The Practitioner's Guide to Data Quality Improvement

The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socia...

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Bibliographic Details
Main Author Loshin, David
Format eBook
LanguageEnglish
Published Chantilly Elsevier Science & Technology 2010
Morgan Kaufmann
OMG Press
Edition1
Subjects
Database management
Information management
Management
Quality control
Online AccessGet full text
ISBN9780123737175
0123737176
DOI10.1016/C2009-0-17212-4

Cover

Table of Contents:
  • 16.7. False Positives, False Negatives, and Thresholding -- 16.8. Survivorship -- 16.9. Monitoring Linkage and Survivorship -- 16.10. Entity Search and Match and Computational Complexity -- 16.11. Applications of Identity Resolution -- 16.12. Evaluating Business Needs -- 16.13. Summary -- Chapter 17: Inspection, Monitoring, Auditing, and Tracking -- 17.1. The Data Quality Service Level Agreement Revisited -- 17.2. Instituting Inspection and Monitoring: Technology and Process -- 17.3. Data Quality Business Rules -- 17.4. Automating Inspection and Monitoring -- 17.5. Incident Reporting, Notifications, and Issue Management -- 17.6. Putting It Together -- Chapter 18: Data Enhancement -- 18.1. The Value of Enhancement -- 18.2. Approaches to Data Enhancement -- 18.3. Examples of Data Enhancement -- 18.4. Enhancement through Standardization -- 18.5. Enhancement through Context -- 18.6. Enhancement through Data Merging -- 18.7. Summary: Qualifying Data Sources for Enhancement -- Chapter 19: Master Data Management and Data Quality -- 19.1. What Is Master Data? -- 19.2. What Is Master Data Management? -- 19.3. "Golden Record"´ or "Unified View"? -- 19.4. Master Data Management as a Tool -- 19.5. MDM: A High-Level Component Approach -- 19.6. Master Data Usage Scenarios -- 19.7. Master Data Management Architectures -- 19.8. Identifying Master Data -- 19.9. Master Data Services -- 19.10. Summary: Approaching MDM and Data Quality -- Chapter 20: Bringing it All Together -- 20.1. Organization and Management -- 20.2. Building the Information Quality Program -- 20.3. Techniques and Tools -- 20.4. Summary -- Index
  • Intro -- The Practitioner's Guide to Data Quality Improvement -- Copyright -- Contents -- Foreword -- Preface -- Acknowledgments -- About the Author -- Chapter 1: Business Impacts of Poor Data Quality -- 1.1. Information Value and Data Quality Improvement -- 1.2. Business Expectations and Data Quality -- 1.3. Qualifying Impacts -- 1.4. Some Examples -- 1.5. More on Impact Classification -- 1.6. Business Impact Analysis -- 1.7. Additional Impact Categories -- 1.8. Impact Taxonomies and Iterative Refinement -- 1.9. Summary: Translating Impact into Performance -- Chapter 2: The Organizational Data Quality Program -- 2.1. The Virtuous Cycle of Data Quality -- 2.2. Data Quality Processes -- 2.3. Stakeholders and Participants -- 2.4. Data Quality Tools -- 2.5. Summary -- Chapter 3: Data Quality Maturity -- 3.1. The Data Quality Strategy -- 3.2. A Data Quality Framework -- 3.3. A Data Quality Capability/Maturity Model -- 3.4. Mapping Framework Components to the Maturity Model -- 3.5. Summary -- Chapter 4: Enterprise Initiative Integration -- 4.1. Planning Initiatives -- 4.2. Framework Initiatives -- 4.3. Operational and Application Initiatives -- 4.4. Scoping Issues -- 4.5. Summary -- Chapter 5: Developing A Business Case and A Data Quality Road Map -- 5.1. Return on the Data Quality Investment -- 5.2. Developing the Business Case -- 5.3. Finding the Business Impacts -- 5.4. Researching Costs -- 5.5. Correlating Impacts and Causes -- 5.6. The Impact Matrix -- 5.7. Problems, Issues, Causes -- 5.8. Mapping Impacts to Data Flaws -- 5.9. Estimating the Value Gap -- 5.10. Prioritizing Actions -- 5.11. The Data Quality Road Map -- 5.12. Practical Steps for Developing the Road Map -- 5.13. Accountability, Responsibility, and Management -- 5.14. The Life Cycle of the Data Quality Program -- 5.15. Summary -- Chapter 6: Metrics and Performance Improvement
  • Chapter Outline -- 6.1. Performance-Oriented Data Quality -- 6.2. Developing Data Quality Metrics -- 6.3. Measurement and Key Data Quality Performance Indicators -- 6.4. Statistical Process Control -- 6.5. Control Charts -- 6.6. Kinds of Control Charts -- 6.7. Interpreting Control Charts -- 6.8. Finding Special Causes -- 6.9. Maintaining Control -- 6.10. Summary -- Chapter 7: Data Governance -- 7.1. The Enterprise Data Quality Forum -- 7.2. The Data Quality Charter -- 7.3. Mission and Guiding Principles -- 7.4. Roles and Responsibilities -- 7.5. Operational Structure -- 7.6. Data Stewardship -- 7.7. Data Quality Validation and Certification -- 7.8. Issues and Resolution -- 7.9. Data Governance and Federated Communities -- 7.10. Summary -- Chapter 8: Dimensions of Data Quality -- 8.1. What Are Dimensions of Data Quality? -- 8.2. Categorization of Dimensions -- 8.3. Describing Data Quality Dimensions -- 8.4. Intrinsic Dimensions -- 8.5. Contextual -- 8.6. Qualitative Dimensions -- 8.7. Finding Your Own Dimensions -- 8.8. Summary -- Chapter 9: Data Requirements Analysis -- 9.1. Business Uses of Information and Business Analytics -- 9.2. Business Drivers and Data Dependencies -- 9.3. What Is Data Requirements Analysis? -- 9.4. The Data Requirements Analysis Process -- 9.5. Defining Data Quality Rules -- 9.6. Summary -- Chapter 10: Metadata and Data Standards -- 10.1. Challenges -- 10.2. Data Standards -- 10.3. Metadata Management -- 10.4. Business Metadata -- 10.5. Reference Metadata -- 10.6. Data Elements -- 10.7. Business Metadata -- 10.8. A Process for Data Harmonization -- 10.9. Summary -- Chapter 11: Data Quality Assessment -- 11.1. Planning -- 11.2. Business Process Evaluation -- 11.3. Preparation and Data Analysis -- 11.4. Data Profiling and Analysis -- 11.5. Synthesis of Analysis Results -- 11.6. Review with Business Client
  • 11.7. Summary Rapid Data Assessment - Tangible Results -- Chapter 12: Remediation and Improvement Planning -- 12.1. Triage -- 12.2. The Information Flow Map -- 12.3. Root Cause Analysis -- 12.4. Remediation -- 12.5. Execution -- 12.6. Summary -- Chapter 13: Data Quality Service Level Agreements -- 13.1. Business Drivers and Success Criteria -- 13.2. Identifying Data Quality Rules -- 13.3. Establishing Data Quality Control -- 13.4. The Data Quality Service Level Agreement -- 13.5. Inspection and Monitoring -- 13.6. Data Quality Metrics and a Data Quality Scorecard -- 13.7. Data Quality Incident Reporting and Tracking -- 13.8. Automating the Collection of Metrics -- 13.9. Reporting the Scorecard -- 13.10. Taking Action for Remediation -- 13.11. Summary - Managing Using the Data Quality Scorecard -- Chapter 14: Data Profiling -- 14.1. Application Contexts for Data Profiling -- 14.2. Data Profiling: Algorithmic Techniques -- 14.3. Data Reverse Engineering -- 14.4. Analyzing Anomalies -- 14.5. Data Quality Rule Discovery -- 14.6. Metadata Compliance and Data Model Integrity -- 14.7. Coordinating the Participants -- 14.8. Selecting a Data Set for Analysis -- 14.9. Summary -- Chapter 15: Parsing and Standardization -- 15.1. Data Error Paradigms -- 15.2. The Role of Metadata -- 15.3. Tokens: Units of Meaning -- 15.4. Parsing -- 15.5. Standardization -- 15.6. Defining Rules and Recommending Transformations -- 15.7. The Proactive versus Reactive Paradox -- 15.8. Integrating Data Transformations into the Application Framework -- 15.9. Summary -- Chapter 16: Entity Identity Resolution -- 16.1. The Lure of Data Correction -- 16.2. The Dual Challenge of Unique Identity -- 16.3. What Is an Entity? -- 16.4. Identifying Attributes -- 16.5. Similarity Analysis and the Matching Process -- 16.6. Matching Algorithms