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By Mehdi Khosrowpour

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

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Hibbard, J. (1997). Ernst & Young deploys app for knowledge management. Information Week, Jul 28, 28. Hildebrand, C. (1999). Does KM=IT? CIO Enterprise, Sept. 15. html . Kalakota, R. and Robinson, M. (1999). e-Business: Roadmap for Success. 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.

Quality Process, 61-64. Loebl, A. (1990). Accuracy and relevance and the quality of data. In Liepins, G. E. and Uppuluri, V. R. R. ), Data Quality Control: Theory and Practices, 105-143. New York. Morey, R. (1982). Estimating and improving the quality of information in a MIS. Communications of the ACM, 25(5), 337-342. Nesdore, P. (2001). The quest for mobility. MSI Oak Brook, 19(1), 53-60. O’Brien, J. (1993). Management Information Systems: A Managerial End User Perspective. Englewood Cliffs New Jersey, Irwin.

From another perspective, Figure 7 illustrates how these four dimensions of data quality engineering correspond to the various data view perspectives. The quality of a Using a Metadata Framework To Improve Data Resources Quality 29 Figure 4: Extended model of data quality dimensions Quality of Data Value Practice related Quality of Data Representation Data Quality Quality of Data Models Structure related Quality of Data Architecture Figure 5: Hierarchical relationship of the four data quality dimensions (closer to the user) (closer to the architect) Data Representation Quality Data Value Quality Data Model Quality Data Architecture Quality as presented to the user as maintained in the system as understood by developers as an organizational asset Figure 6: Metamodel quality dimensions related to data model life cycle phases Dimension Data Architecture Quality Data Model Quality Data Value Quality Data Representation Quality Focus/Phase: Data architecture quality is the focus of metadata creation & refinement efforts.

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