Learninganoptimalclassificationmodelintrinsicallydepends on data quality. Despite many efforts for its characterization, existing methods have often limited quality measures to specific criteria, lead- ing to the lack of comprehensive definitions and rigorous formulations. Indeed, its evaluation is related to the context and often requires exter- nal elements, which implies a process that is long and prone to errors. Therefore, there is still a strong need for solutions that enable effective data quality assessment. This paper addresses the resulting scientific challenges and introduces a new metric, specifically designed for numerical classification problems. Unlike existing measures, the proposed solution is based on the corre- lated evolution between classification performance and data deteriora- tion. Therefore, it offers three main advantages: Being model indepen- dent, not requiring the use of external reference data while offering a solution that is easy to adapt for several real-world scenarios. Addition- ally, we provide a comprehensive interpretation of the quality scores and illustrate the main evaluation levels with use cases. We demonstrate its effectiveness through extensive experiments and comparisons with the state of the art.