Supporting data quality management in decision-making

In the complex decision-environments that characterize e-business settings, it is important to permit decision-makers to proactively manage data quality. In this paper we propose a decision-support framework that permits decision-makers to gauge quality both in an objective (context-independent) and in a context-dependent manner. The framework is based on the information product approach and uses the Information Product Map (IPMAP). We illustrate its application in evaluating data quality using completeness--a data quality dimension that is acknowledged as important. A decision-support tool (IPView) for managing data quality that incorporates the proposed framework is also described.

[1]  Richard Y. Wang,et al.  Manage Your Information as a Product , 1998 .

[2]  Joseph Moses Juran,et al.  Quality-control handbook , 1951 .

[3]  Richard C. Morey,et al.  Estimating and improving the quality of information in a MIS , 1982, CACM.

[4]  Salvatore J. Stolfo,et al.  Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem , 1998, Data Mining and Knowledge Discovery.

[5]  Charles Møller,et al.  Proceedings of the Tenth Americas Conference on Information Systems , 2004 .

[6]  Diane M. Strong,et al.  Information quality benchmarks: product and service performance , 2002, CACM.

[7]  Matthias Jarke,et al.  Architecture and Quality in Data Warehouses: An Extended Repository Approach , 1999, Information Systems.

[8]  Stuart E. Madnick,et al.  Data quality requirements analysis and modeling , 2011, Proceedings of IEEE 9th International Conference on Data Engineering.

[9]  Donald P. Ballou,et al.  Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems , 1985 .

[10]  Richard Y. Wang,et al.  Anchoring data quality dimensions in ontological foundations , 1996, CACM.

[11]  Anany Levitin,et al.  The Notion of Data and Its Quality Dimensions , 1994, Inf. Process. Manag..

[12]  Donald P. Ballou,et al.  Modeling Completeness versus Consistency Tradeoffs in Information Decision Contexts , 2003, IEEE Trans. Knowl. Data Eng..

[13]  Thomas Redman,et al.  Data quality for the information age , 1996 .

[14]  InduShobha N. Chengalur-Smith,et al.  The Impact of Experience and Time on the Use of Data Quality Information in Decision Making , 2003, Inf. Syst. Res..

[15]  Richard Y. Wang,et al.  Modeling Information Manufacturing Systems to Determine Information Product Quality Management Scien , 1998 .

[16]  Yu Cai,et al.  A Data Quality Assurance Model in the B2B Networked Environment , 2004, AMCIS.

[17]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Dependent Variable , 1992, Inf. Syst. Res..

[18]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[19]  Diane M. Strong,et al.  AIMQ: a methodology for information quality assessment , 2002, Inf. Manag..

[20]  Richard Y. Wang,et al.  IP-MAP: Representing the Manufacture of an Information Product , 2000, IQ.

[21]  Varghese S. Jacob,et al.  Assessing data quality for information products , 1999, ICIS.

[22]  G. Shankaranarayan,et al.  Managing Data Quality in Dynamic Decision Environments: An Information Product Approach , 2003, J. Database Manag..

[23]  Donald P. Ballou,et al.  Designing Information Systems to Optimize the Accuracy-Timeliness Tradeoff , 1995, Inf. Syst. Res..

[24]  Richard Y. Wang,et al.  Data quality assessment , 2002, CACM.