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Data quality: The Achilles heel of end-user computing

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  • Janson, MA

Abstract

Data quality is an important factor in the success or failure of end-user developed information systems. However, experience shows that in some practical situations data of poor quality are the norm rather than the exception. The evidence in this study demonstrates that exploratory statistical techniques can significantly improve data quality during all phases of data acquisition. More importantly, however, these techniques are especially crucial to quality control and to the validation of data already collected.

Suggested Citation

  • Janson, MA, 1988. "Data quality: The Achilles heel of end-user computing," Omega, Elsevier, vol. 16(5), pages 491-502.
    • Handle: RePEc:eee:jomega:v:16:y:1988:i:5:p:491-502
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    Cited by:

    1. Jarke, M. & Jeusfeld, M.A. & Quix, C. & Vassiliadis, P., 1999. "Architecture and quality in data warehouses - An extended repository approach," Other publications TiSEM 4daa92ac-0bc2-42c2-bd6b-7, Tilburg University, School of Economics and Management.
    2. Jeusfeld, M.A. & Quix, C. & Jarke, M., 1998. "Design and analysis of quality information for data warehouses," Other publications TiSEM fde64335-eb29-4c82-b7c8-5, Tilburg University, School of Economics and Management.
    3. Faraji Kasidi & H. Chaturvedi & Rahul Singh, 2010. "Detecting Data Error and Inaccuracy," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(4), pages 405-425, November.

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