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By Mehdi Khosrowpour

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Extra resources for Advanced Topics in Information Resources Management Series, Vol. 1

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The problem is exacerbated by increased company dependence on computer technology for business operations and by strategic requirements necessary to improve company global competitiveness. Further adding to the problem is the trend toward systems integration within and among companies, which has forced IS departments to change focus from developing and operating relatively isolated applications to new ones capable of supporting cross-functional and interorganizational business processes. Faced with such demands, data resources managers have received little guidance from researchers addressing the complete range of data quality dimensions they have to cope with.

Reading, MA: Addison-Wesley. Kanter, R. M. (1983). The Change Masters: Innovation & Entrepreneurship in the American Corporation. New York, NY: Simon & Schuster. Kerr, S. (1995). Creating the boundaryless organization: The radical reconstruction of organization capabilities. Planning Review, September/October, 41-45. Kohn, A. (1995). Punished by Rewards: The Trouble With Gold Stars, Incentive Plans, A’s, Praise, and Other Bribes. Boston, MA: Houghton Mifflin. Landau, M. (1973). On the concept of self-correcting organizations.

No other data quality characteristic matters if the necessary data are defective or not available. , Ballou & Tayi, 1989; Laudon, 1986; Morey, 1982; O’Brien, 1993; Tsichritzis & Fochovski, 1982). Meanwhile, the definition of data quality has been evolving. Originally, data quality engineering was mostly focused on data values maintained by information systems, and data quality research was mostly based on the value triplet component defined earlier. Work by Tufte (1990) and others such as Fox et al.

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