Data Mining involves the extraction of new knowledge from large data sets. Despite the growing research interest in data mining, however, integrating this extra knowledge into the subsequent decision making processes has received little attention. Within the context of operations management, this integration can occur in two different ways: by providing inputs for an optimization procedure and by analyzing the output of an optimization procedure. In this dissertation, I will begin by introducing a database exploration technique, which is used to improve the drug discovery process of a pharmaceutical company (Samorani et al., 2011). The same procedure is also applied to a mental health clinic's database to predict whether patients will show up at their scheduled appointments. The knowledge obtained with this procedure is then used to improve patient scheduling procedures (Samorani and LaGanga, 2011). I will finally discuss how data mining can be used to learn useful information about the structure of a problem (Samorani and Laguna, 2012).