However, and categorizing them according to a

However, a significant point to note is that, the actual efficacy in the usage or application of the organizational knowledge depends upon the corporate culture. More than eighty percent of an organization’s information exists as unstructured data and cannot be directly comprehended by the IT systems that store it. Most of this unstructured data is, in fact, textual. The challenge is not just to retrieve it.

Search engines that list every appearance of a word have no role in the development of IC because they cannot discern appropriate context. This necessitates the usage and deployment of text and data mining tools that can uncover information’s buried context: patterns, associations, themes, and trends. Mining involves: (a) Full-text search engines, which conduct in-depth document analysis while they index. This enables more sophisticated querying.

(b) Text analysis tools, which pick up on multi-word concepts, or features, without the aid of a domain dictionary. Intelligent clustering of documents into content-related groups and categorizing them according to a corporate classification schemes means other applications can extract summaries and key concepts. Important information can also be “pushed” to the right people at the right time. (c) Knowledge maps, which can be created as features, are extracted from documents, then clustered, categorized, and summarized. (d) Metadata modeling that creates a catalog or repository about an organization’s knowledge maps and information assets (such as their location, author, source, and date of creation). The enterprise metadata repositories can be vast, requiring scalable enterprise database platforms and a corporate commitment to keep them updated and investment in mining tools.

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