1. A data mining method, comprising: providing a computing system comprising a computer readable medium and a plurality of computing devices electrically coupled through an interface apparatus, wherein a data mining modeling algorithm is stored on said computer readable medium, and wherein each of said plurality of computing devices comprises at least one central processing unit (CPU) and an associated memory device; receiving, by each computing device of said plurality of computing devices, a copy of a database managing software application; receiving, by said computing system, a steady stream of data; dividing, by the computing system, said data into a plurality of data subsets; associating, by the computing system, each data subset of said plurality of data subsets with a different customer number associated with a different customer; placing, by the computing system, a different data subset of said data subsets in each said associated memory device, wherein said receiving, said dividing, and said placing are performed simultaneously; selecting a lift chart technique for generating data mining models, wherein said data mining models comprise associated data mining models applied to each of said randomly placed data subsets; receiving simultaneously, by each of said plurality of computing devices, said data mining modeling algorithm; running simultaneously, on each of said plurality of computing devices, said data mining modeling algorithm on a different associated data subset of said plurality of data subsets using said selected lift chart technique to generate an associated data mining model on each of said plurality of computing devices; calculating, by said computing system, a lift for each of said data mining models, wherein said calculating comprises calculating a first lift for first data mining model of said data mining models by dividing a percentage of expected responses predicted by said first data mining model by a percentage of expected responses predicted by a random selection, wherein a normal density of responses to a direct mail campaign for a service offer is equal to 10 percent, wherein a determination generated by focusing on a top quartile of a case set predicted to respond to said direct mail campaign by said first data mining model, wherein said determination comprises a density of responses increasing to 30 percent, and wherein said first lift is equal to 30/10; simultaneously comparing, by a coordinator node of said computing system, each said lift to each other; determining based on results of said comparing, by said computing system, a best data mining model from said data mining models, wherein said best data mining model is said first data mining model; removing each said different data subset from each said associated memory device; and deploying, by said computing system, said best data mining model with respect to said direct mail campaign.