The performance gap between processors and off-chip memories has widened in the last years and is expected to widen even more. Today, it is widely accepted that caches improve significantly the performance of programs, since most of the programs exhibit temporal and/or spatial locality in their memory reference patterns. However, conflict misses can be a major obstacle preventing an application from utilizing the data cache effectively. While array padding can reduce conflict misses it can also increase the data space requirements significantly. In this paper, we present a compiler-based data transformation strategy, called the "generalized data transformations," for reducing inter-array conflict misses in embedded applications. We present the theory behind the generalized data transformations and discuss how they can be integrated with compiler-based loop transformations. Our experimental results demonstrate that the generalized data transformations are very effective in improving data cache behavior of embedded applications.