Evaluating Completeness of an Information Product.

Research in data quality has identified several dimensions for evaluating data quality including accuracy, timeliness, completeness, and relevance just to name a few. While dimensions such as accuracy and timeliness have received considerable attention from researchers, completeness has not received explicit consideration. In this paper we propose a theoretical method for evaluating completeness as a data quality dimension. This method is built on top of the information product map (IPMAP), a representation scheme for modeling the manufacture of an information product (IP). Moreover, the methods presented in literature for evaluating data quality (focusing on accuracy and timeliness) take an objective view of the evaluation and do not permit the decision-maker to inject his/her own subjective interpretations. We adopt the view that evaluating data quality is not completely objective and requires some subjective interpretation. We take this view because in the dynamic decision environments of today it is not sufficient to inform the decision-maker about the quality of the data. It is also necessary to permit the decision-maker to evaluate the quality by him/herself. This is especially important in dynamic decision environments in which decision-makers deal with very large volumes and widely distributed data sources and with a wide variety and frequency of decision-tasks. The Internet and associated technologies help create such dynamic decision environments within the e-business and m-business concepts that they support. The framework for completeness proposed here is designed to support data quality management in such decision environments.