1. A method comprising: receiving, by a server computer, a data set from one or more client computers connected with the server computer via a communications network; storing, by the server computer, the data set in a distributed storage memory including a set of memory based compute nodes associated with the server, the dataset stored in parts across the set of memory based compute nodes; executing, by the set of memory based compute nodes, one or more analytical processes on the data set from the distributed storage memory to generate summary statistics based on each of the analytical processes, each compute node finding partial statistics of a data part stored on the respective compute node, the partial statistics representative of a respective data part; storing the summary statistics in a random access memory associated with the server computer, the random access memory being accessible by at least one of the compute nodes, the summary statistics being a combination of the partial statistics and representative of a full data set; identifying, for pre-model building data understanding, outlier data by comparing subsets of data in the data set, the identified outlier data accessible to a predictive model; generating, by the at least one of the compute nodes, a graphical representation of at least some summary statistics stored in the random access memory; and formatting, by the server computer, the graphical representation of at least some summary statistics for transmission to and display by the one or more client computers.