Data Quality of Query Results with Generalized Selection Conditions
Operations Research
Data quality assessment: The Hybrid Approach
Information and Management
A risk based model for quantifying the impact of information quality
Computers in Industry
| Hi-index | 0.00 |
Business problems are directly related to missed data quality expectations. Flawed information production processes introduce risks preventing the successful achievement of critical business objectives. However, these flaws are mitigated through data quality management and control: controlling the quality of the information production process from beginning to end to ensure that any imperfections are identified early, prioritized, and remediated before material impacts can be incurred. The Practitioner's Guide to Data Quality Improvement 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. This book shares templates and processes for business impact analysis, defining data quality metrics, inspection and monitoring, remediation, and using data quality tools. Never shying away from the difficult topics or subjects, this is the seminal book that offers advice on how to actually get the job done.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.Table of ContentsPreface 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