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

Amir Parssian (), Sumit Sarkar () and Varghese S. Jacob ()
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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

Management Science, 2004, vol. 50, issue 7, 967-982

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.

Keywords: information quality metrics; relational data model; relational algebra; probability calculus (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (9)

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