(303d) Managing Unstructured Data for Content Intelligence

The functions of organizing, providing access to, and facilitating the exchange of information have long been essential responsibilities of organizations in government, commercial and industrial firms, and other sectors. Many if not most of the systems and work processes that are currently used for these functions are based on earlier approaches that were devoted exclusively to printed information. Today's knowledge managers must contend with information in numerous formats, including structured and unstructured data. The term ?Content Intelligence? refers to work processes that enable organizations to access and utilize information across the enterprise based on its content, without regard to the formats used to create or store the information.

In a world of printed, digital, and ?meta? data types, that comprise everything from text to photos, video and sound, engineering drawings, and many other data sources, there are numerous challenges facing developers of Content Intelligence systems. As a first step, the needs of all contributors, organizers, and producers of information in the organization must be defined, and current methodologies for accessing and exchanging information should be catalogued. Although new Content Intelligence system(s) may be implemented as a part of an overall strategy, it is likely that existing systems will be enhanced to work with a broader range of data types. Hence, all current information management methods and tools should be encompassed in the development of a Content Intelligence system.

Next, all subject areas that are of interest to the organization should be classified using conventional taxonomy schemes, such as indexes and keywords, as well as newer methods that may include domain-specific XML (and other) standards. The subject matter classification scheme will provide a backbone for managing content because it offers a consistent point of reference based on the content in the document or other media.

The final step in implementing a Content Intelligence system is to aggregate the information sources across the enterprise into a common classification scheme and enable access to and sharing of the information for all end-users. Text mining and data analytics are examples of functions that may need to be supported in such a system. A large number of technology vendors are available to assist in the developing Content Intelligence systems, including vendors of Business Intelligence, Content Management, Document Management, Advanced Search, and types of systems. However, the best solution is the one that meets an organization's needs efficiently, reliably, and cost-effectively; thus, Content Intelligence system developers should look for opportunities to adapt existing methods and tools to the new system in addition to reviewing off-the-shelf technology products and services.