Knowledge discovery through an analytic learning cycle

Knowledge discovery through analytic learning cycles is founded on a coherent, real-time view of data from across an enterprise, the data having been captured and aggregated and is available in real-time at a central repository. Knowledge discovery is an iterative process where each cycle of analytic learning employs data mining. Thus, an analytic learning cycle includes defining a problem, exploring the data at the central repository in relation to the problem, preparing a modeling data set from the explored data, building a model from the modeling data set, assessing the model, deploying the model back to the central repository, and applying the model to a set of inputs associated with the problem. Application of the model produces results and, in turn, creates historic data that is saved at the central repository. Subsequent iterations of the analytic learning cycle use the historic data, as well as current data accumulated in the central repository, thereby creating up-to-date knowledge for evaluating and refreshing the model.