Multiple Big Data applications are being deployed worldwide to serve a very large number of clients nowadays. These applications vary in their performance and consistency requirements. Understanding such requirements at the storage system level is not possible. The high level semantics of an application are not exposed at the system level. In this context, the consequences of a stale read are not the same for all types of applications. In this work, we focus on managing consistency at the application level rather than at the system level. In order to achieve this goal, we propose an offline modeling approach of the application access behavior that considers its high-level consistency semantics. Furthermore, every application state is automatically associated with a consistency policy. At runtime, we introduce the Chameleon approach that leverages the application model to provide a customized consistency specific to that application. Experimental evaluations show the high accuracy of our modeling approach exceeding 96% of correct classification of the application states. Moreover, our experiments conducted on Grid'5000 show that Chameleon adapts, for every time period, according to the application behavior and requirements while providing best-effort performance.