The Practitioner's Guide to Data Quality Improvement

Business problems are directly related to missed data quality expectations. This book 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.

Autorentext
David Loshin is President of Knowledge Integrity, Inc., a company specializing in data management consulting. The author of numerous books on performance computing and data management, including Master Data Management" (2008) and Business Intelligence The Savvy Manager's Guide" (2003), and creator of courses and tutorials on all facets of data management best practices, David is often looked to for thought leadership in the information management industry.

Klappentext

Information is an asset that is generated through numerous processes, with multiple feeds of raw data that are combined, processed, and fed out to multiple customers both inside and outside your organization. Many business problems can be directly tied to a situation in which data quality is below expectations. This can, however, be mitigated by closely controlling the quality of the process, overseeing the activities from beginning to end, and ensuring that any imperfections are identified as early as possible. The Practitioner's Guide to Data Quality Improvement provides readers with the fundamentals for developing an enterprise data quality program, and it serves as a guide for both the practitioner and the manager in establishing a data quality center of excellence. Readers are presented with information about business case templates, characterization of business impacts, correlated metrics, visualization and reporting, and the use of data quality tools. This seminal text offers advice on how to actually get the job done, not shying away from difficult topics or subjects.

Inhalt

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