The Practitioner's Guide to Data Quality Improvement

The Practitioner's Guide to Data Quality Improvement

3.83 (6 ratings by Goodreads)
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Description

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.show more

Product details

  • Paperback | 432 pages
  • 187.96 x 231.14 x 22.86mm | 725.74g
  • ELSEVIER SCIENCE & TECHNOLOGY
  • Morgan Kaufmann Publishers In
  • San Francisco, United States
  • English
  • 42 illustrations; Illustrations, unspecified
  • 0123737176
  • 9780123737175
  • 633,423

Review quote

"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 Blogshow more

Table of 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 Togethershow more

About David Loshin

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.show more

Review Text

"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 "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 Blogshow more

Rating details

6 ratings
3.83 out of 5 stars
5 17% (1)
4 50% (3)
3 33% (2)
2 0% (0)
1 0% (0)
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