In this paper, we present a framework for categorical data analysis which allows such data sets to be explored using a rich set of techniques that are only applicable to continuous data sets. We introduce the concept of separability statistics in the context of exploratory categorical data analysis. We show how these statistics can be used as a way to map categorical data to continuous space given a labeled reference data set. This mapping enables visualization of categorical data using techniques that are applicable to continuous data. We show that in the transformed continuous space, the performance of the standard k-nn based outlier detection technique is comparable to the performance of the k-nn based outlier detection technique using the best of the similarity measures designed for categorical data. The proposed framework can also be used to devise similarity measures best suited for a particular type of data set.