We present a data analytics framework for offline analysis of batch processes. The framework provides a unified setting for implementing several variants of feature oriented analysis proposed in the literature, including a new methodology based on the process variables profiles presented in this article. It also integrates feature generation and feature analysis, in order to speed up the data exploration cycle, which is especially relevant for complex batch processes. The FOBA (Feature Oriented Batch Analytics platform) is described in detail and applied to several case studies regarding different analysis goals: visualization of the differences between the operation of two industrial units (dryers), quality prediction, and end-of-batch process monitoring. The performance of the proposed methodology is also critically assessed and compared with other alternative analytical approaches currently in use.