The article introduces a method called Dynamic Mode Decomposition (DMD) for extracting dynamic information from flow fields generated by numerical simulations or visualized/measured in physical experiments. DMD can be used to describe the underlying physical mechanisms captured in the data sequence or to project large-scale problems onto a dynamical system with fewer degrees of freedom. The method is particularly useful for focusing on subdomains where relevant dynamics is expected, allowing the dissection of complex flows into regions of localized instability phenomena. The authors demonstrate the method using plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane, and a jet passing between two cylinders. The DMD technique is validated through convergence behavior and compared with other decompositions such as Proper Orthogonal Decomposition (POD) and Principal Oscillation Patterns (POP). The results show that DMD can accurately capture the temporal behavior contained in the processed data sequence, even when the full extent of the instability mode is only partially captured by the measurements.The article introduces a method called Dynamic Mode Decomposition (DMD) for extracting dynamic information from flow fields generated by numerical simulations or visualized/measured in physical experiments. DMD can be used to describe the underlying physical mechanisms captured in the data sequence or to project large-scale problems onto a dynamical system with fewer degrees of freedom. The method is particularly useful for focusing on subdomains where relevant dynamics is expected, allowing the dissection of complex flows into regions of localized instability phenomena. The authors demonstrate the method using plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane, and a jet passing between two cylinders. The DMD technique is validated through convergence behavior and compared with other decompositions such as Proper Orthogonal Decomposition (POD) and Principal Oscillation Patterns (POP). The results show that DMD can accurately capture the temporal behavior contained in the processed data sequence, even when the full extent of the instability mode is only partially captured by the measurements.