The organizational approach in enterprise content and records management systems has traditionally relied upon the use of document attribute data as the primary information source for content storage and retrieval. Attributes such as business units, document types, expiration dates, functional areas, and similar things are common.
In the past few years content and records management applications have often used a “facetted” taxonomy design to define how the content and/or records are classified.
Is this sufficient? It might be acceptable in some cases for content management, but falls short of expectations for knowledge management.
Large volumes of knowledge content are often well suited to auto-categorization. he tools and methods most commonly used for auto-categorization are text analytics or image analysis. This can be expensive to implement. This makes it challenging for small law offices to benefit from this technology, who also have large volumes of data to contend with.
In coming blogs, I will talk about how to deliver inexpensive solutions to the problem.