MANAGING KNOWLEDGE
Knowledge
management is a set of processes to create, store, transfer, and apply
knowledge in the organization. Knowledge management is the fastest growing
areas of corporate and government software investment. The past decade has
shown an explosive growth in research on knowledge and knowledge management in
the economics, management, and information systems fields. Much of a firm’s
value depends on its ability to create and manage knowledge.
Knowledge management promotes
organizational learning by increasing the ability of the organization to learn
from its environment and to incorporate knowledge into its business processes. Knowledge
management has become an important theme at many large business firms as
managers realize that much of their firm’s value depends on the firm’s ability
to create and manage knowledge.
Studies have found that a substantial
part of a firm’s stock market value is related to its intangible assets, of
which knowledge is one important component, along with brands, reputations, and
unique business processes. Well-executed knowledge-based projects have been
known to produce extraordinary returns on investment, although the impacts of
knowledge-based investments are difficult to measure (Gu and Lev, 2001; Blair
and Wallman, 2001). There are three major types of knowledge management systems:
enterprise-wide knowledge management systems, knowledge work systems, and
intelligent techniques.
Enterprise-wide knowledge
management systems are firmwide efforts to collect, store, distribute, and
apply digital content and knowledge. Enterprise content management systems
provide databases and tools for organizing and storing structured documents and
tools for organizing and storing semistructured knowledge, such as e-mail or
rich media. Knowledge network systems provide directories and tools for
locating firm employees with special expertise who are important sources of
tacit knowledge. Often these systems include group collaboration tools
(including wikis and social bookmarking), portals to simplify information
access, search tools, and tools for classifying information based on a taxonomy
that is appropriate for the organization.
Enterprise-wide knowledge
management systems can provide considerable value if they are well designed and
enable employees to locate, share, and use knowledge more efficiently. Knowledge
work systems (KWS) support the creation of new knowledge and its integration
into the organization. KWS require easy access to an external knowledge base;
powerful computer hardware that can support software with intensive graphics,
analysis, document management, and communications capabilities; and a
user-friendly interface. Computer-aided design (CAD) systems, augmented reality
applications, and virtual reality systems, which create interactive simulations
that behave like the real world, require graphics and powerful modeling
capabilities.
KWS for financial professionals provide
access to external databases and the ability to analyze massive amounts of
financial data very quickly. Artificial intelligence lacks the flexibility,
breadth, and generality of human intelligence, but it can be used to capture,
codify, and extend organizational knowledge. Expert systems capture tacit knowledge
from a limited domain of human expertise and express that knowledge in the form
of rules. Expert systems are most useful for problems of classification or
diagnosis. Case-based reasoning represents organizational knowledge as a
database of cases that can be continually expanded and refined. Fuzzy logic is a software technology for
expressing knowledge in the form of rules that use approximate or subjective
values. Fuzzy logic has been used for controlling physical devices and is starting
to be used for limited decision-making applications.
Neural networks consist of
hardware and software that attempt to mimic the thought processes of the human
brain. Neural networks are notable for their ability to learn without
programming and to recognize patterns that cannot be easily described by
humans. They are being used in science, medicine, and business to discriminate
patterns in massive amounts of data. Genetic algorithms develop solutions to
particular problems using genetically based processes such as fitness,
crossover, and mutation.
Genetic algorithms are beginning
to be applied to problems involving optimization, product design, and
monitoring industrial systems where many alternatives or variables must be
evaluated to generate an optimal solution. Intelligent agents are software
programs with built-in or learned knowledge bases that carry out specific,
repetitive, and predictable tasks for an individual user, business process, or
software application. Intelligent agents can be programmed to navigate through
large amounts of data to locate useful information and in some cases act on
that information on behalf of the user.
Sources:
Kenneth C. Laudon and Jane P. Laudon. 2012. Management Information Systems: Managing the Digital Firm. Twelfth Edition:
Pearson.
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