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.
General
Imprint: |
Morgan Kaufmann Publishers In
|
Country of origin: |
United States |
Series: |
The Morgan Kaufmann Series on Business Intelligence |
Release date: |
November 2010 |
First published: |
October 2010 |
Authors: |
David Loshin
|
Dimensions: |
233 x 190 x 21mm (L x W x T) |
Format: |
Paperback
|
Pages: |
398 |
ISBN-13: |
978-0-12-373717-5 |
Categories: |
Books >
Computing & IT >
Applications of computing >
Databases >
General
|
LSN: |
0-12-373717-6 |
Barcode: |
9780123737175 |
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