Data Quality through Conceptual Model Quality - Reconciling Researchers and Practitioners through a Customizable Quality Model

Data quality has emerged as an important and challenging topic in recent years. In this article we are addressing the conceptual model quality since it has been widely accepted that better conceptual models produce better information systems and thus implicitly improve the data quality. Unfortunately there is neither standard nor agreed framework for managing conceptual models quality. This article presents an overview of existing approaches with their advantages and limitations. It then proposes a comprehensive model for evaluating the quality of conceptual models. A survey involving practitioners has been used as an initial validation. This validation exercise aims to collect the responders’ views on the holistic quality of the conceptual models in addition to their feedback over the newly proposed model. The received feedback has been evaluated and incorporated to the quality model. Furthermore, we propose a general approach for quality evaluation and improvement.