The practitioner's guide to data quality improvement
There is no question that poor data quality is a problem pervasive across numerous industries and organizations. According to the 2006 Data Warehousing Institute report on enterprise data quality, nearly half of survey respondents claim that the quality of their data is ""worse than everyo...
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Burlington, MA :
Morgan Kaufmann
2010.
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| Edición: | 1st edition |
| Colección: | MK/OMG Press
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628166306719 |
Tabla de Contenidos:
- 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 Quality2.2. 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 Map5.1. 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 ImprovementChapter 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 Stewardship7.7. 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?
