Extracting actionable insight from large high dimensional data sets, and its use for more effective decision-making, has become a pervasive problem across many fields in research and industry. We describe an investigation of the application of tightly coupled statistical and visual analysis techniques to this task. The approach we choose is "unsupervised learning" where we investigate the advantages offered by close coupling of the self-organizing map algorithm with new combinations of visualization components and techniques for interactivity.