Richard Y. Wang
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- 2 Ballou,D P .and Tayi G k .Methodology for allocating resources for data quality enhancsn ent. Cgmvn. ACM 32, 3 Mar. 1989), 320-329. Google ScholarDigital Library
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- 10 Wang, R .Y., Storey, V C., and Firth, C 2.A framework for analysis of data quality, researrch. IEEE Trans Knowledge and Data Engneering 7, 4 (1995), 623-640. Google ScholarDigital Library
- 11 Wang, R .Y .andStrong, D M .Beyond accuracyy:W hat data quality means to data consumers.J. Manage Info sys. 12, 4 (1996), 5-34. Google ScholarDigital Library
- 12 Wang, R .Y .and Lee, Y W.Integrity Analysis: A Software Tod for Total Data quality Management. Cam bridge Research Group, Cam bridge, M A, 1998.Google Scholar
Index Terms
- A product perspective on total data quality management
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Reviews
Lou Agosta
Wang draws an analogy between total quality management (TQM) of manufactured physical products and total data quality management (TDQM). In a world in which information has gone from supporting business to being the business, this is a timely and powerful analogy. Wang adapts W. E. Deming's method of defining, measuring, analyzing, and improving products [1] to the data management environment. The strength of the author's approach is his rich categorization and taxonomy of attributes of information quality (IQ). He classifies them into intrinsic IQ: accuracy, objectivity, believability, and reputation; accessibility IQ: access and security; contextual IQ: relevancy, value-added, timeliness, completeness, and amount of data; and representational IQ: interpretability, ease of understanding, and concise and consistent representation. Wang distinguishes different roles in the information supply chain—suppliers, producers (here called “manufacturers”), consumers, and managers. He proposes and applies a survey method of measurement, in which people in these different roles are asked to evaluate the IQ attributes of an information product. The sample survey results show the manufacturer of an information product perceiving significantly higher percen tages of data completeness in the product than its consumers do. The audience for this instructive approach to data quality includes database administrators, application developers, bus iness managers, and other savvy practitioners in the information economy.
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