Conventional manual data analysis cannot provide a complete analysis of datasets generated by the current generation of flow cytometry instruments generating 50 parameter data on each of hundreds of thousands of single cells for each of up to thousands of samples in study. Algorithms have reached a level of maturity that enables them to match and in many cases exceed the results produced by human experts [1,2,3]. An overview will be provided of robust, reproducible and rapid data analysis pipelines for both automatic identification of cell populations of known importance (e.g., diagnosis by identification of pre-defined cell population) and for exploratory analysis of cohorts (e.g., discovery of cell populations that correlate with patient subgroups). Real-word examples of how automated discovery and diagnosis approaches have been used in basic and clinical research will be used illustrate the power of these approaches in practice.