Data mining and text mining can help decision makers obtain business intelligence and make informed decisions, but using one of them gives us only a partial picture. The application of data mining can lead to questions that cannot be answered with only numbers. Therefore, decision makers will need text mining to drill the textual data to find explanations for numbers. On the other hand, the application of text mining will also raise questions that cannot be answered with only text. We need to examine and utilize findings from both. However, most of the current text mining applications and data mining applications are not integrated. In this paper, a framework for combining these two technologies is described. In this framework, a taxonomy complemented by feature indexing and full-text indexing will bridge data mining and text mining. The technical challenges of the integration are also discussed.