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The Impact of Data Quality Information on Decision Making: An Exploratory Analysis
November/December 1999 (vol. 11 no. 6)
pp. 853-864

Abstract—This paper describes an experiment that explores the consequences of providing information regarding the quality of data used in decision making. The subjects in the study were given three types of information about the data's quality: none, two-point ordinal, and interval scale. This information was made available to the subjects, along with the actual data. Two decision strategies were explored: conjunctive and weighted linear additive. Two decision environments were used: a simple environment and a relatively complex environment. Various combinations of these factors were employed to explore several issues. These include complacency, consensus, and consistency. The paper provides preliminary insights into which type of data-quality information is most effective and the circumstances in which data-quality information is most effective. Such knowledge would be of value to those responsible for designing databases that support decision-makers. Overall, we find that in a situation where subjects are confronted with clearly differentiated alternatives, the inclusion of data-quality information impacted the selection of the preferred alternative while maintaining group consensus.

[1] D.P. Ballou and H.L. Pazer, “Modeling Data and Process Quality Multi-Input Multi-Output Information Systems,” Management Science, vol. 31, no. 2, pp. 150-162, 1985.
[2] D.P. Ballou and H.L. Pazer, “Framework for the Analysis of Error in Conjunctive, Multi-Criteria, Satisficing Decision Processes,” Decision Sciences, vol. 21, no. 4, pp. 752-770, 1990.
[3] D.M. Grether, A. Schwartz, and L.L. Wilde, “The Irrelevance of Information Overload: An Analysis of Search and Disclosure,” Southern California Law Rev., vol. 59, pp. 277-303, 1986.
[4] S.L. Jarvenpaa, "The Effect of Task Demands and Graphical Format on Information Processing and Strategies," Management Science, vol. 35, no. 3, Mar. 1989, pp. 285-303.
[5] J.R. Johnson, R.A. Leitch, and J. Neter, “Characteristics of Errors in Account Receivables and Inventory Audits,” Accounting Rev., vol. 56, no. 2, pp. 270-293, 1981.
[6] B.D. Klein, D.L Goodhue, and G.B. Davis, “Can Humans Detect Errors in Data? Impact of Base Rates, Incentives, and Goals,” MIS Quarterly, vol. 21, pp. 169-194, June 1997.
[7] J.W. Payne, “Task Complexity and Contingent Processing in Decision Making: An Information Search and Protocol Analysis,” Organizational Behavior and Human Performance, vol. 16, pp. 366-387, 1976.
[8] J.W. Payne, J.R. Bettman, and E.J. Johnson, The Adaptive Decision Maker. Cambridge, Mass.: Cambridge Univ. Press, 1993.
[9] T.C. Redman, Data Quality for the Information Age. Boston: Artech House, 1996.
[10] D.N. Stone and D.A. Schkade, “Numeric and Linguistic Information Representation in Multivariate Choice,” Organizational Behavior and Human Decision Processes, vol. 49, pp. 42-59, 1991.
[11] R.Y. Wang and S.E. Madnick, “A Polygon Model for Heterogeneous Database Systems: The Source Tagging Perspective,” Proc. 16th Int'l Conf. Very Large Databases, pp. 519-538, Brisbane, Australia, 1990.
[12] R.Y. Wang, V.C. Storey, and C.P. Firth, “A Framework for the Analysis of Data Quality Research,” IEEE Trans. Knowledge and Data Eng., vol. 7, no. 4, pp. 623-639, Aug. 1995.
[13] R.Y. Wang and D.M. Strong, “Beyond Accuracy: What Data Quality Means to Data Consumers,” J. Management Information Systems, vol. 12, no. 4, pp. 5-34, 1996.
[14] R.W. Zmud, “An Empirical Investigation of the Dimensionality of the Concept of Information,” Decision Sciences, vol. 9, pp. 187-195, 1978.

Index Terms:
Data quality, data tagging, decision making, decision complacency, decision consensus, decision consistency.
Citation:
InduShobha N. Chengalur-Smith, Donald P. Ballou, Harold L. Pazer, "The Impact of Data Quality Information on Decision Making: An Exploratory Analysis," IEEE Transactions on Knowledge and Data Engineering, vol. 11, no. 6, pp. 853-864, Nov.-Dec. 1999, doi:10.1109/69.824597
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