The Practitioner's Guide to Data Quality Improvement

By: David Lashin

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Paperback | 15 October 2010

At a Glance

Paperback
436 Pages

Dimensions(cm)
23.5 x 19.05 x 2.26

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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.

Industry 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

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