Communicating and sharing knowledge have become
increasingly important in today’s businesses. Knowledge is most useful and actionable when
it is shared throughout the firm.
Knowledge has several dimensions that must be understood.
Knowledge is a firm asset. It is an intangible asset and is not subject
to the law of diminishing returns as are physical assets, but it experiences
network effects as its value increases as more people share it. The transformation of data into useful
information and knowledge requires organizational resources. Knowledge also has different forms. It can be either tacit or explicit and it
involves know-how, craft, and skill. It
involves knowing how to follow procedures and knowing why, not simply when,
things happen. Knowledge has
location. It is a cognitive event
involving mental models and maps of individuals, having both a social and an
individual basis of it. It is “sticky”
(hard to move), situated, and contextual.
Lastly, knowledge is situational.
It is conditional, knowing when to apply a procedure is just as
important as knowing the procedure. It
is related to context, meaning it must be known how to use a certain tool and
under what circumstances.
Organizations create and gather knowledge using
a variety of organizational learning mechanisms. Organizational learning is the process of
learning to adjust the organization’s behavior to reflect learning by creating
new business processes and by changing patterns of management decision
making. Organizations that can sense and
respond to their environments rapidly will survive longer than organizations
that have poor learning mechanisms.
Knowledge management is the set of processes
developed in an organization to create, gather, store, maintain, and
disseminate the firm’s knowledge. It
increases the ability of the organization to learn from its environment and to
incorporate knowledge into its business processes. To maximize the return on investment in
knowledge management projects, supportive values, structures, and behavior
patterns must be built. Knowledge
management involves both information systems activities and a host of enabling
management and organizational activities.
Information system activities include knowledge acquisition (knowledge
discovery, data mining, neural networks, genetic algorithms, knowledge
workstations, expert knowledge networks), knowledge storage (document
management systems, knowledge databases, expert systems), knowledge
dissemination (Intranet portals, push email reports, search engines,
collaboration), and knowledge application (decision support systems, enterprise
applications).
Essentially there are three major types of
knowledge management systems.
Enterprise-wide knowledge management systems are general-purpose
firmwide efforts to collect, store, distribute, and apply digital content and
knowledge. Knowledge work systems (KWS)
refer to specialized systems built for engineers, scientists, and other
knowledge workers charged with discovering and creating new knowledge for a
company. Intelligent techniques have
different objectives from a focus on discovering knowledge (data mining and
neural networks) to distilling knowledge in the form of rules for a computer
program (expert systems and fuzzy logic) to discovering optimal solutions for
problems (genetic algorithms).
Enterprise-wide knowledge management systems
deal with three types of knowledge: the
knowledge that exists within the firm in the form of structured text documents;
the knowledge that is semistructured; and the knowledge that resides in the
heads of employees where there is no formal or digital information of any
kind.
Enterprise content management systems help organizations
manage structured and semistructured knowledge, providing corporate
repositories of documents, reports, presentations, and best practices and
capabilities for collecting and organizing email and graphic objects. One main issue in managing knowledge is the
creation of an appropriate classification scheme (known as taxonomy) to
organize information into meaningful categories so that it can be easily
accessed. Once created each knowledge
object needs to be “tagged” so that it can be easily retrieved. Enterprise content management systems have
this capability. These systems also
include powerful portal and collaboration technologies. The portals can provide access to external
sources of information as well as to internal knowledge resources. Social bookmarking makes it easier to search
for and share information by allowing users to save their bookmarks to Web
pages on a public Web site and tag these bookmarks with keywords. A learning management system provides tools
for the management, delivery, tracking, and assessment of various types of
employee learning and training.
Knowledge network systems are also known as
expertise location and management systems.
They address the problem that arises when the appropriate knowledge is
not in the form of a digital document but instead resides in the memory of expert
individuals in the firm. These systems
provide an online directory of corporate experts in well-defined knowledge
domains and use communication technologies to make it easy for employees to
find the appropriate expert in a company.
Knowledge workers include researchers,
designers, architects, scientists, and engineers who primarily create knowledge
and information for the organization.
They perform three key roles that are critical to the organization and to
the managers who work within the organization:
they keep the organization current in knowledge as it develops in the
external world; they serve as internal consultants regarding the areas of their
knowledge, the changes taking place, and opportunities; and they act as change
agents, evaluating, initiating, and promoting change projects. They require highly specialized knowledge
work systems with powerful graphics, analytical tools, and communications and
document management capabilities.
Knowledge work applications include CAD
systems, virtual reality systems for simulation and modeling, and financial
workstations. CAD (computer-aided
design) automates the creation and revision of designs, using computers and
sophisticated graphics software. Virtual
reality systems use interactive graphics software to create computer-generated
simulations that are so close to reality that users almost believe they are
participating in a real-world situation.
Virtual Reality Modeling Language (VRML) is a standard used by these
applications and is a set of specifications for interactive, 3-D modeling on
the World Wide Web that can organize multiple media types to put users in a
simulated real-world environment.
Augmented reality (AR) provides a live direct or indirect view of a
physical real-world environment whose elements are augmented by virtual
computer-generated imagery.
Artificial intelligence (AI) technology
consists of computer-based systems (both hardware and software) that attempt to
emulate human behavior. This along with
database technology provides a number of intelligent techniques that
organizations can use to capture individual and collective knowledge and to
extend their knowledge base. These
techniques include expert systems, case-based reasoning, fuzzy logic, neural
networks and data mining, genetic algorithms, and intelligent agents.
Expert systems are used for capturing tacit
knowledge and capturing the knowledge of skilled employees in the form of a set
of rules in a software system that can se used by others in the
organization. However, they lack the
breadth of knowledge and the understanding of fundamental principles of a human
expert and they typically perform very limited tasks that can be performed by
professionals in a few minutes or hours.
Yet, by capturing human expertise in limited areas, expert systems can
provide benefits, helping organizations make high-quality decisions with fewer
people. These systems model human
knowledge as a set of rules that collectively are called the knowledge
base. Inference engine is the strategy
used to search through the knowledge base.
Two strategies commonly used are forward chaining and backward
chaining. Forward chaining begins with
the information entered by the user and searches the rule base to arrive at a
conclusion. Backward chaining acts like
a problem solver by beginning with a hypothesis and seeking out more
information until the hypothesis is either proved or disproved. Benefits of expert systems include improved
decisions, reduced errors, reduced costs, reduced training time, and higher
levels of quality and service. But, they
require large, lengthy, and expensive development efforts and the environment
in which it operates is continually changing so that the expert system must
also continually change. Also, some of
these systems are so complex that in a few years the maintenance costs equal
the development costs.
Case-based reasoning is also used for capturing
tacit knowledge. It represents knowledge
as a series of cases, and this knowledge base is continuously expanded and
refined by users. It is found in
diagnostic systems in medicine or customer support where users can retrieve
past cases whose characteristics are similar to the new case. It suggests a solution or diagnosis based on
the best-matching retrieved case.
Fuzzy logic is rule-based AI that tolerates
imprecision by using nonspecific terms called membership functions to solve
problems. Organizations can use this
logic to create software systems that capture tacit knowledge where there is
linguistic ambiguity. It also provides
solutions to problems requiring expertise that is difficult to represent in the
form of crisp IF-THEN rules. Management
also has found it useful for decision-making and organizational control.
Neural networks are used for knowledge
discovery; solving complex, poorly understood problems for which large amounts
of data have been collected. These
networks “learn” patterns from large quantities of data by sifting through
data, searching for relationships, building models, and correcting over and
over again the model’s own mistakes.
Neural network designers seek to put intelligence into the hardware in
the form of a generalized capability to learn.
The applications are used in medicine, science, and businesses to
address problems in pattern classification, prediction, financial analysis, and
control and optimization. However,
neural networks cannot always explain why they arrived at a particular solution
and they cannot always guarantee a completely certain solution, arrive at the
same solution again with the same input data, or always guarantee the best
solution. Also, they are very sensitive
and may not perform well if their training covers too little or too much data.
Genetic algorithms are useful for finding the
optimal solution for a specific problem by examining a very large number of
possible solutions for that problem.
They are based on techniques inspired by evolutionary biology. They are used to solve problems that are very
dynamic and complex, involving hundreds or thousands of variables or
formulas.
Intelligent agents can automate routine tasks
to help firms search for and filter information for use in electronic commerce,
supply chain management, and other activities. It is software programs that work in the
background without direct human intervention to carry out specific, repetitive,
and predictable tasks for an individual user, business process, or software
application. These applications can be
found in operating systems, application software, email systems, mobile
computing software, and network tools.
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