Many large entities such as corporations have vast and growing collections of assets to apply against opportunities. Examples of such assets include sales collateral, customer case studies, prior request for proposal (RFP) responses, manuals and documentation of a company's own products and its partners' products. But these assets are fragmented across expertise boundaries, for example: (i) between sales and marketing people and technologists; (ii) many product lines, each with its team of experts; and (iii) multiple disciplines (e.g., physics and computer science).
Experts know their individual area of expertise but have limited perception of other areas that could apply against customer opportunities. Moreover, there exist relatively few experts relative to the size of a company's workforce and opportunity pipeline. The basic problem is that vocabulary from one subject area is different from other subject areas. Consequently, an expert from one area rarely can articulate precise terms to retrieve relevant information from another subject area.
From a technical perspective, there exist techniques in query augmentation, information retrieval and thesaurus construction that globally analyze a given corpus, but these techniques do not address vocabulary mismatches across multiple subject areas.
From a business value perspective, such a vocabulary mismatch between subject areas results in sub-optimal utilization of a company's assets against opportunities in sales, RFP response preparation, etc. Such a vocabulary mismatch also results in unnecessary expense in re-doing a task while similar capability exists in other areas.