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Knowledge Discovery Using Intelligent Agents: A Proposed Framework
The transformation of data into knowledge can be accomplished in several
ways. In general, it is a process that starts with data collection from various sources.
These data
are stored in a database. Then the data can be preprocessed and
stored in a data warehouse. To discover knowledge, the processed data may go
through a transformation that makes it ready for analysis. The analysis is done with
data mining tools, which look for patterns, and intelligent systems, which support
data interpretation. The result of all these activities is generated knowledge. Both
the data, at various times during the process, and the knowledge, derived at the
end of the process, may need to be presented to users. Such a presentation can
be accomplished by using presentation tools, and the created knowledge may be
stored in a knowledge base (Turban, Mclean and Wetherbe (1999).
Having understood the need for a change in organization theory
and managerial
style, it is essential to develop a system architecture for implementation of
knowledge management systems. Brook Manville, Director of Knowledge
Management at the consulting firm McKinsey & Co. in Boston, proposes three
architectures needed for implementing a shift from traditional emphasis on transaction
processing, integrated logistics, and work flows to systems that support competencies
for communication building:
•
A new information architecture that includes new languages, categories, and
metaphors for identifying and accounting for skills and competencies.
•
A new technical architecture that is more social, transparent, open, flexible, and
respectful of the individual user.
•
A new application architecture oriented towards problem solving and
representation, rather than output and transactions.
The application of this framework will facilitate business model innovation nec-
essary for sustainable competitive advantage in the new business environment
characterized by dynamic, discontinuous and radical pace of change. This pro-
posed architecture helps integrate the key ideas of this paper, i.e., a socio-techni-
cal perspective of intelligent agents facilitating the transition to the knowledge so-
ciety. The social and ethical implications are discussed in the next section.