The Practitioner's Guide to Data Quality Improvement

Preface
Chapter 1: Business Impacts of Poor Data Quality
Chapter 2: The Organizational Data Quality Program
Chapter 3: Data Quality Maturity
Chapter 4: Enterprise Initiative Integration
Chapter 5: Developing a Business Case and a Data Quality Roadmap
Chapter 6: Metrics and Performance Improvement
Chapter 7: Data Governance
Chapter 8: Dimensions of Data Quality
Chapter 9: Data Requirement Analysis
Chapter 10: Metadata and Data Standard
Chapter 11: Data Quality Assessment
Chapter 12: Remediation and Improvement Planning
Chapter 13: Data Quality Service Level Agreements
Chapter 14: Data Profiling
Chapter 15: Parsing and Standardization
Chapter 16: Entity Identity Resolution
Chapter 17: Inspection, Monitoring, Auditing, and Tracking
Chapter 18: Data Enhancement
Chapter 19: Master Data Management and Data Quality
Chapter 20: Bringing It All Together

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 socializing, gaining sponsorship for, planning, and establishing a data quality program.

It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.

This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers.

• Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology.
• Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics.
• Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.

Data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers.