Data caches significantly reduce the average memory access time and are necessary for an efficient design. Due to its direct dependency on input data is is difficult to predict the worst case timing behavior, which is crucial for a reliable system. While simulation is too time-consuming, current worst case execution time approaches focus on instruction caches only. Current approaches to data cache analysis restrict cache behavior to predictable data accesses or classify input dependent memory accesses as non-cacheable. In this paper we propose a worst case timing analysis for direct mapped data caches that classifies memory accesses as predictable or unpredictable. For unpredictable memory accesses, a novel analysis framework is proposed that tightly bounds the impact on the existing cache contents as well as cache behavior of unpredictable memory accesses themselves. For predictable memory accesses, we use a local cache simulation and data flow techniques. Furthermore, we describe an implementation of the analysis framework. Several experiments demonstrate its applicability. The approach targets real-time software verification but is also useful for design space exploration.