The Practitioner

The Practitioner's Guide to Data Quality Improvement

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.

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

Paperback, 432 Pages

Published: October 2010

Imprint: Morgan Kaufmann

ISBN: 978-0-12-373717-5

Reviews

  • "There is NOTHING like this out there that I am aware of, and certainly nothing from anyone with same stature as David Loshin."--David Plotkin, Wells Fargo Bank

    "The book provides a comprehensive look at data quality from both a business and IT perspective. It does not just cover technology issues, but discusses people, process, and technology. And that is important, because this is the mix that is needed in order to initiate any type of quality improvement regimen."--Data Technology Today Blog


Contents

  • 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

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