Data mining is a research technique using computational analysis to uncover patterns in large data sets. Data mining techniques range from machine learning applications, to GIS and mapping, to business intelligence. The range of data types makes data mining techniques harder to pin down.
Text mining is the process of deriving information from textual data. Text mining techniques might include sentiment analysis, network analysis, word frequency distributions, pattern recognition, tagging/annotation, information extraction, and the production of granular taxonomies or ontologies.
This kind of analytic tool is useful in numerous scholarly fields, from the humanities to the sciences, where useful data can be "mined" from large non-text datasets and from text databases of the published literature (Source: UMass Amherst Libraries).
TDM can help reveal new patterns or information from a large body of work -- leading to the development of new knowledge, of a larger evidence-based practice. TDM enables researchers to analyze thousands of documents and Terabytes of data, allowing for a comprehensive look into research questions.
TDM can help answer such a variety of questions it would be hard to list them all! Some potential use cases are listed below, but do consult the literature of your field to see potentially how TDM is being used to answer the questions of your domain.