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Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product

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  • Amir Parssian

    (College of Business and Management, University of Illinois at Springfield, Springfield, Illinois 62703)

  • Sumit Sarkar

    (School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Varghese S. Jacob

    (School of Management, University of Texas at Dallas, Richardson, Texas 75080)

Abstract

The cost associated with making decisions based on poor-quality data is quite high. Consequently, the management of data quality and the quality of associated data management processes has become critical for organizations. An important first step in managing data quality is the ability to measure the quality of information products (derived data) based on the quality of the source data and associated processes used to produce the information outputs. We present a methodology to determine two data quality characteristicsÔaccuracy and completenessÔthat are of critical importance to decision makers. We examine how the quality metrics of source data affect the quality for information outputs produced using the relational algebra operations selection, projection, and Cartesian product. Our methodology is general, and can be used to determine how quality characteristics associated with diverse data sources affect the quality of the derived data.

Suggested Citation

  • Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2004. "Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product," Management Science, INFORMS, vol. 50(7), pages 967-982, July.
    • Handle: RePEc:inm:ormnsc:v:50:y:2004:i:7:p:967-982
    DOI: 10.1287/mnsc.1040.0237
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    References listed on IDEAS

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    1. Kon, Henry B. & Madnick, Stuart E. & Siegel, Michael D., 1995. "Good answers from bad data : a data management strategy," Working papers 3868-95., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. 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.
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    Cited by:

    1. Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2009. "Impact of the Union and Difference Operations on the Quality of Information Products," Information Systems Research, INFORMS, vol. 20(1), pages 99-120, March.
    2. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 2018. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 20(2), pages 401-416, April.
    3. 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.
    4. Dominikus Kleindienst, 2017. "The data quality improvement plan: deciding on choice and sequence of data quality improvements," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 387-398, November.
    5. Qi Liu & Gengzhong Feng & Giri Kumar Tayi & Jun Tian, 2021. "Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach," Information Systems Frontiers, Springer, vol. 23(2), pages 375-389, April.
    6. Debabrata Dey & Subodha Kumar, 2013. "Data Quality of Query Results with Generalized Selection Conditions," Operations Research, INFORMS, vol. 61(1), pages 17-31, February.
    7. Debabrata Dey & Subodha Kumar, 2010. "Reassessing Data Quality for Information Products," Management Science, INFORMS, vol. 56(12), pages 2316-2322, December.
    8. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 0. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 0, pages 1-16.

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