Improving the quality of data models: empirical validation of a quality management framework

  • Authors:

  • Daniel L. Moody;Graeme G. Shanks

  • Affiliations:

  • Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim N-7491, Norway and School of Business Systems, Monash University, Melbourne 3800, A ...;Department of Information Systems, University of Melbourne, Melbourne 3052, Australia

  • Venue:
  • Information Systems
  • Year:
  • 2003

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Abstract

This paper describes the results of a 5-year research programme into evaluating and improving the quality of data models. The theoretical base for this work was a data model quality management framework proposed by Moody and Shanks (In: P. Loucopolous (Ed.), Proceedings of the 13th International Conference on the Entity Relationship Approach, Manchester, England, December 14-17, 1994). A combination of field and laboratory research methods (action research, laboratory experiments and systems development) was used to empirically validate the framework. This paper describes how the framework was used to: (a) quality assure a data model in a large application development project (product quality); (b) reengineer application development processes to build quality into the data analysis process (process quality); (c) investigate differences between data models produced by experts and novices; (d) provide automated support for the evaluation process (the Data Model Quality Advisor). The results of the research have been used to refine and extend the framework, to the point that it is now a stable and mature approach.