A Semiotic Approach to Data Quality

Since the introduction of the ER-language in the late seventies, data modeling has been an important aspect of information systems development. The quality of data models has been investigated since the mid-nineties. In another strand of research, data and information quality has been investigated even longer. Data can also be looked upon as a type of model (on the instance level), as illustrated e.g. in the product models in CAD-systems. In this paper we present a specialization of a general framework for assessing quality of models to be able to evaluate the combined quality of data models and data. A practical application of the framework from assessing the potential quality of different data sources to be used in a collaborative work environment is used for illustrating the usefulness of the framework. We find on the one hand that the traditional properties of data quality and data model quality is subsumed by the generic SEQUAL-framework, and that there are aspects in this framework that are not covered by the existing work on data and data model quality. On the other hand, the comparison has resulted in a useful deepening of the generic framework for data quality, and has in this way improved the practical applicability of the SEQUAL-framework when applied to discussing and assessing data quality.