Text analytics can provide a wide breadth of valuable information, including summarization, clustering, classification, and categorization to enable better functional interaction with the text. This includes improved search, translation, optimization, and learning. In this paper, we describe advanced analytical approaches used to enable improved utility of the text documents and information later. This adds value to the preservation of the information and provides new access points to the information. We emphasize the role of functional approaches to testing and configuration of these systems, with the view that the primary role of archiving is to make the content as re-usable, re-purposeable, and discoverable as possible.