Data quality assessment

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Data quality assessment

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Communications of the ACM archive
Volume 45 , Issue 4 (April 2002) table of contents
Supporting community and building social capital

SPECIAL ISSUE: Virtual extension table of contents

Pages: 211 - 218

Year of Publication: 2002

ISSN:0001-0782

Authors

Leo L. Pipino	University of Massachusetts Lowell
Yang W. Lee	Northeastern University, Boston, MA
Richard Y. Wang	Boston University and MIT Sloan School of Management, Cambridge, MA

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 89, Downloads (12 Months): 565, Citation Count: 39

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abstract references cited by index terms collaborative colleagues

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ABSTRACT

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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