High-fidelity compressor flow field simulations based on DES-series methods produce vast spatiotemporal datasets, capturing intricate unsteady flow phenomena with high resolution. Extracting and interpreting meaningful features—such as dominant frequencies, coherent vortex structures, and causal relationships within turbulence—poses significant challenges. This chapter systematically introduces various techniques for analyzing massive unsteady compressor flow field data, including frequency-domain methods, vortex identification criteria (Q, λ2, and Liutex), and advanced modal decomposition algorithms (POD, DMD, and SPOD). These methods serve complementary roles: frequency-domain analysis helps isolate characteristic frequencies indicative of flow regime transitions; vortex identification criteria pinpoint spatially coherent flow structures, differentiating strong and weak vortices; and modal decomposition approaches decouple complex spatiotemporal interactions, enabling the extraction of fundamental flow patterns and their temporal evolution.By integrating these techniques, researchers can more effectively identify critical structures—such as tip leakage vortices, corner separation vortices, and hairpin vortices—and investigate their formation, propagation, and influence on compressor performance and stability. Moreover, the advanced modal decomposition methods (POD, DMD, and SPOD) facilitate the elucidation of underlying flow physics, distinguishing key frequencies and growth rates, characterizing stability or instability trends, and reconstructing flow fields dominated by principal modes. Ultimately, the synergistic application of these data analysis methods provides a powerful toolkit for refining our understanding of complex, multi-scale compressor flow fields, guiding both fundamental research and engineering design improvements.