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Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems

Author

Listed:
  • Donald P. Ballou

    (Management Science Department, School of Business, State University of New York, Albany, New York 12222)

  • Harold L. Pazer

    (Management Science Department, School of Business, State University of New York, Albany, New York 12222)

Abstract

This paper presents a general model to assess the impact of data and process quality upon the outputs of multi-user information-decision systems. The data flow/data processing quality control model is designed to address several dimensions of data quality at the collection, input, processing and output stages. Starting from a data flow diagram of the type used in structured analysis, the model yields a representation of possible errors in multiple intermediate and final outputs in terms of input and process error functions. The model generates expressions for the possible magnitudes of errors in selected outputs. This is accomplished using a recursive-type algorithm which traces systematically the propagation and alteration of various errors. These error expressions can be used to analyze the impact that alternative quality control procedures would have on the selected outputs. The paper concludes with a discussion of the tractability of the model for various types of information systems as well as an application to a representative scenario.

Suggested Citation

  • Donald P. Ballou & Harold L. Pazer, 1985. "Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems," Management Science, INFORMS, vol. 31(2), pages 150-162, February.
  • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:2:p:150-162
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    File URL: http://dx.doi.org/10.1287/mnsc.31.2.150
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    Citations

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    Cited by:

    1. Prat, Nicolas & Madnick, Stuart E., 2008. "Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage," Working papers 40085, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. repec:eee:proeco:v:193:y:2017:i:c:p:737-747 is not listed on IDEAS
    3. Klein, Barbara D., 2001. "Detecting errors in data: clarification of the impact of base rate expectations and incentives," Omega, Elsevier, vol. 29(5), pages 391-404, October.
    4. Donald Ballou & Richard Wang & Harold Pazer & Giri Kumar Tayi, 1998. "Modeling Information Manufacturing Systems to Determine Information Product Quality," Management Science, INFORMS, vol. 44(4), pages 462-484, April.
    5. Kon, Henry B. & Siegel, Michael D., 2003. "Error browsing and mediation : interoperability regarding data error," Working papers #94-15, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.
    7. Sabrina Sicari & Cinzia Cappiello & Francesco Pellegrini & Daniele Miorandi & Alberto Coen-Porisini, 2016. "A security-and quality-aware system architecture for Internet of Things," Information Systems Frontiers, Springer, vol. 18(4), pages 665-677, August.
    8. Klein, B. D. & Rossin, D. F., 1999. "Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy," Omega, Elsevier, vol. 27(5), pages 569-582, October.
    9. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    10. Benita M. Gullkvist, 2013. "Drivers of change in management accounting practices in an ERP environment," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Eastern Macedonia and Thrace Institute of Technology (EMATTECH), Kavala, Greece, vol. 6(2), pages 149-174, September.
    11. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    12. Risto Silvola & Janne Harkonen & Olli Vilppola & Hanna Kropsu-Vehkapera & Harri Haapasalo, 2016. "Data quality assessment and improvement," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 22(1), pages 62-81.
    13. Michnik, Jerzy & Lo, Mei-Chen, 2009. "The assessment of the information quality with the aid of multiple criteria analysis," European Journal of Operational Research, Elsevier, vol. 195(3), pages 850-856, June.
    14. So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.

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