The Practitioner's Guide to Data Quality Improvement

by David Loshin

Paper Book, 2011



Call number

IBMC Library - QA 76.9 D3 L6934 2011


Burlington, MA : Morgan Kaufmann, 1st edition


0123737176 / 9780123737175


There is no question that poor data quality is a problem pervasive across numerous industries and organizations. According to the 2006 Data Warehousing Institute report on enterprise data quality, nearly half of survey respondents claim that the quality of their data is ""worse than everyone thinks."" The number of respondents saying that their organization had suffered losses, problems, or costs due to poor data quality grew from 44% in 2001 to 53% in 2005. Thankfully, in recent years, these same respondents have claimed on new focus on data quality and the need to do something about it. Here

Local notes

Business impacts of poor data quality
The organizational data quality program
Data quality maturity
Enterprise initiative integration
Developing a business case and a data quality road map
Metrics and performance improvement
Data governance
Dimensions of data quality
Data requirement analysis
Metadata and data standards
Data quality assessment
Remediation and improvement planning
Data quality service level agreements
Data profiling
Parsing and standardization
Entity identity resolution
Inspection, monitoring, auditing, and tracking
Data enhancement
Master data management and data quality
Bringing it all together.
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