CHAPTER 11 "Managing Knowledge"
11.1 The Knowledge Management Landscape
Knowledge management and collaboration systems are among the
fastest growing areas of corporate and government software investment. Knowledge
that cannot be communicated and shared with others is nearly useless. Knowledge
becomes useful and actionable when shared throughout the firm. 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.
Important Dimensions of Knowledge
To transform information into knowledge, a firm
must expend additional resources to discover patterns, rules, and contexts where
the knowledge works. Finally, wisdom
is thought to be the
collective and individual experience of applying knowledge to the solution of
problems. Wisdom involves where, when, and how to apply knowledge. Knowledge
residing in the minds of employees that has not been documented is called tacit knowledge,
whereas knowledge that has been documented is called explicit knowledge.
Organizational Learning and Knowledge Management
Organizations that learn adjust their behavior to reflect that
learning by creating new business processes and by changing patterns of
management decision making. This process of change is called organizational learning.
Arguably, organizations that can sense and respond to their environments
rapidly will survive longer than organizations that have poor learning
mechanisms.
The Knowledge Management Value Chain
Knowledge management refers
to the set of business processes developed in an organization to create, store,
transfer, and apply knowledge. the five
value-adding steps in the knowledge management value chain. Each stage in the
value chain adds value to raw data and information as they are transformed into
usable knowledge.
- Knowledge acquisition
- Knowledge storage
- Knowledge dissemination
- Knowledge application
Types of Knowledge Management Systems
There are essentially three major types of knowledge management
systems: enterprise-wide knowledge management systems, knowledge work systems, and
intelligent techniques.
Enterprise-wide knowledge management systems are general-purpose firmwide efforts to collect, store, distribute,
and apply digital content and knowledge.
Knowledge work systems (KWS) are specialized systems built for engineers, scientists, and other knowledge workers
charged with discovering and creating new knowledge for a company.
Knowledge management also includes a diverse group of intelligent techniques,
such as data mining, expert systems, neural networks, fuzzy logic, genetic algorithms, and
intelligent agents.
11.2 Enterprise-Wide Knowledge Management Systems
Enterprise-wide knowledge management systems deal with all three
types of knowledge.
Enterprise Content Management Systems
Businesses today need to organize and manage both structured and
semistructured knowledge assets. Structured
knowledge is explicit knowledge that exists
in formal documents, as well as in formal rules that organizations derive by
observing experts and their decision-making behaviors. But, according to experts,
at least 80 percent of an organization’s business content is semistructured or
unstructured—information in folders, messages, memos, proposals, e-mails,
graphics, electronic slide presentations, and even videos created in different
formats and stored in many locations.
Enterprise content management systems help organizations manage both types of information. A key problem
in managing knowledge is the creation of an appropriate classification scheme,
or taxonomy, to organize information into meaningful categories so that it
can be easily accessed. Digital
asset management systems help companies classify,
store, and distribute these digital objects.
Knowledge Network Systems
Knowledge network systems,
also known as expertise location and
management systems, 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.
Collaboration Tools and Learning Management Systems
The major enterprise content management systems include powerful
portal and collaboration technologies. Companies are starting to use consumer Web
technologies such as blogs, wikis, and social bookmarking for internal use to
foster collaboration and information exchange between individuals and teams. Collaboration
tools from commercial software vendors, such as Microsoft SharePoint and Lotus
Connections, also offer these capabilities along with secure online collaborative
workspaces.
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. Companies need ways to keep track of and manage employee learning and
to integrate it more fully into their knowledge management and other corporate systems.
A learning management system
(LMS) provides tools for the management,
delivery, tracking, and assessment of various types of employee learning and
training.
11.3 Knowledge Work Systems
Knowledge Workers and Knowledge Work
Knowledge workers perform three key roles that are critical to the
organization and to the managers who work within the organization:
- Keeping the organization current in knowledge as it develops in the external world—in technology, science, social thought, and the arts
- Serving as internal consultants regarding the areas of their knowledge, the changes taking place, and opportunities
- Acting as change agents, evaluating, initiating, and promoting change projects
Requirements of Knowledge Work Systems
These systems require sufficient computing power to handle the
sophisticated graphics or complex calculations necessary for such knowledge
workers as scientific researchers, product designers, and financial analysts.
Examples of Knowledge Work Systems
Computeraided design (CAD) automates
the creation and revision of designs, using computers and sophisticated
graphics software. Virtual reality systems have visualization, rendering, and simulation capabilities that go
far beyond those of conventional CAD systems. Augmented reality (AR) is
a related technology for enhancing visualization. AR provides a live direct or
indirect view of a physical real-world environment whose elements are augmented by virtual computer-generated imagery.
Virtual Reality Modeling Language (VRML). VRML is a set of specifications for interactive, 3-D modeling on
the World Wide Web that can organize multiple media types, including animation,
images, and audio to put users in a simulated real-world environment. The
financial industry is using specialized investment
workstations to leverage the knowledge
and time of its brokers, traders, and portfolio managers.
11.4 Intelligent Techniques
Expert systems, case-based reasoning, and fuzzy logic are used for
capturing tacit knowledge. Neural networks and data mining are used for knowledge discovery.
They can discover underlying patterns, categories, and behaviors in large data
sets that could not be discovered by managers alone or simply through
experience. The other intelligent techniques discussed in this section are
based on artificial intelligence (AI)
technology, which consists
of computer-based systems (both hardware and software) that attempt to emulate
human behavior.
Capturing Knowledge: Expert Systems
Expert systems are
an intelligent technique for capturing tacit knowledge in a very specific and
limited domain of human expertise.
How Expert Systems Work
Expert systems model human knowledge as a set of rules that collectively
are called the knowledge base. The strategy used to search through the knowledge base is called
the inference engine. Two strategies are commonly used: forward chaining and backward
chaining. In forward chaining, the inference engine begins with the information entered by the
user and searches the rule base to arrive at a conclusion. In backward chaining, the
strategy for searching the rule base starts with a hypothesis and proceeds by
asking the user questions about selected facts until the hypothesis is either
confirmed or disproved.
Organizational Intelligence: Case-Based Reasoning
In case-based reasoning (CBR), descriptions of past experiences of human specialists, represented
as cases, are stored in a database for later retrieval when the user encounters
a new case with similar parameters. Case-based reasoning, in contrast,
represents knowledge as a series of cases, and this knowledge base is
continuously expanded and refined by users.
Fuzzy Logic Systems
Fuzzy logic is a
rule-based technology that can represent such imprecision by creating rules
that use approximate or subjective values. It can describe a particular
phenomenon or process linguistically and then represent that description in a
small number of flexible rules. Fuzzy logic provides solutions to problems
requiring expertise that is difficult to represent in the form of crisp IF-THEN
rules. Management also has found fuzzy logic useful for decision making and organizational
control.
Neutral Networks
Neural networks are
used for solving complex, poorly understood problems for which large amounts of
data have been collected. They find patterns and relationships in massive
amounts of data that would be too complicated and difficult for a human being
to analyze.
Genetic Algorithms
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. A genetic algorithm
works by representing information as a string of 0s and 1s. Genetic algorithms
are used to solve problems that are very dynamic and complex, involving
hundreds or thousands of variables or formulas.
Hybrid Al Systems
Genetic algorithms, fuzzy logic, neural networks, and expert
systems can be integrated into a single application to take advantage of the
best features of these technologies. Such systems are called hybrid AI systems.
Intelligent Agents
Intelligent agents are
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. Many complex phenomena can be modeled as systems of
autonomous agents that follow relatively simple rules for interaction. Agent-based modeling applications
have been developed to model the behavior of consumers, stock markets, and supply chains
and to predict the spread of epidemics
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source: "Management Information System" e-book, 12th edition, written by Kenneth C. Laudon and Jane P. Laudon.