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 system and thus implicitly improves 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. Generally they are assigned the following objectives: Meet the users? requirements, provide a formal representation of the reality and be a basis for the implementation of the future information system. In model-driven-development, conceptual models are the basis of the transformations leading to the final system. 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.