Data quality has emerged as an important and challenging topic in recent years. This article addresses the conceptual model quality as it has been widely accepted that better conceptual models produce better information systems and thus implicitly improve the data quality. Conceptual Models are designed as part of the analysis phase and serve as a communicating mediator between the users and the development team. Consequently, their understandability is a real challenge to avoid the propagation of inaccurate interpretation of the user requirements to the underlying system design and implementation. In this paper, we propose an adaptive quality model. We illustrate its usefulness by describing how it can be used to model and evaluate the understandability of conceptual models. Our quality evaluation is enriched with corrective actions provided to the designer, leading to a guidance modeling process. A first validation based on a survey is proposed.
MEHMOOD, K., SI-SAID CHERFI, S. and COMYN-WATTIAU, I. (2009). Data Quality Through Model Quality: A Quality Model for Measuring and Improving the Understandability of Conceptual Models. In: CIKM2009 - Workshop Proceedings. Association for Computing Machinery (ACM).