Core issues in Knowledge Management

By : |December 31, 2003 0



Knowledge Management (KM) is the collection of processes that govern the
creation, dissemination and utilization of knowledge. In one form or another,
knowledge management has been around for a very long time. Practitioners have
included philosophers, priests, teachers, politicians, scribes, librarians, etc.

Well, some of the tangible benefits of knowledge management are directly
related to their bottom line savings. In today’s information-driven economy,
companies continuously tap the most opportunities and ultimately derive most
value from intellectual rather than physical assets. According to many experts,
to get the most value from a company’s intellectual assets, knowledge must be
shared and served as the foundation for collaboration. Consequently, an
effective KM program should help a company leverage the assets by:

  • Fostering innovation by encouraging free flow of ideas
  • Improving customer service by streamlining response time
  • Boosting revenues by getting products and services to market faster
  • Enhancing employee retention rates by recognizing the value of employees’
    knowledge and rewarding them for it

  • Streamlining operations and reducing costs by eliminating redundant or
    unnecessary processes

A creative approach to KM can result in improved efficiency, higher
productivity and increased revenues in practically any business function.

Challenges faced

Getting employees on board

This is one of the major issues in an environment where an individual’s
knowledge is valued and rewarded, establishing a tradition that recognizes
tacit knowledge and encourages employees to share their knowledge. One way
companies motivate employees to participate in KM is by creating an incentive
program. However, there’s the danger that employees will participate solely to
earn incentives, without regard to the quality or relevance of the information
they contribute.

Careful analysis
KM is not a technology-based concept. It needs careful planning and analysis.
While technology can support KM, it’s not the be all and end all of KM. Make KM
decisions based on who (people), what (knowledge) and why (business objectives).

Unspecific business goal
A KM program should not be unattached from a business goal. While sharing best
practices is a good idea, there must be an underlying business reason to do so.
Without a solid business case, KM is a futile exercise.

KM is not static
            As with many
physical assets, the value of knowledge can erode over time. Since knowledge can
get stale fast, the     content in a KM program should be
constantly updated, amended and deleted. Further, the relevance of knowledge at
any given time changes, as do the skills of employees. Therefore, there is no
endpoint to a KM program. Like product development, marketing and R&D, KM is
a constantly evolving business practice.

Not all information is knowledge
Companies diligently need to be on the lookout for information overload.
Quantity rarely equals quality, and KM is no exception. Indeed, the point of a
KM program is to identify and disseminate knowledge gems from a sea of
information.

Tools supporting KM
KM tools run the gamut from standard, off-the-shelf e-mail packages to
sophisticated collaboration tools designed specifically to support community
building and identity. Generally, tools fall into one or more of the following
categories: knowledge repositories, expertise access tools, e-learning
applications, discussion and chat technologies, synchronous interaction tools
and search and data mining tools.

Says Ramesh Subramaniam, vice president-engineering,
Purple Yogi. “There are many different methods and tools available in the
knowledge management industry. However, all these tools do not allow for a
combination of automatic classification and human intervention, and do not
support processes for managing knowledge at all levels within an
organization.”

According to Zulfikar Deen, CEO, Agnitio Management Systems, some of the
generic tools available are:

Collaborative tools: Tools that enable sharing of knowledge across time
and distance. These tools may enable both structured and free-flow sharing of
knowledge, and best practices. Transcripts of the use of these tools may be
incorporated into a knowledge base for future use.

Content management tools: Technologies that allow the capture and
management of explicit experience — they allow people to capture, codify, and
organize experiences and ideas in central repositories. A more general term than
data management, content management includes structured and unstructured data.

Document management tools: Tools that would enable document creation,
review and retrieval.

Data mining tools: Applications of nontrivial algorithms to large amounts
of data for the purpose of extracting useful data patterns. Data mining tools
use a variety of techniques including case-based reasoning, data visualization,
fuzzy query and analysis, and neural networks. Case-based reasoning tools
provide a means to find records similar to a specified record or records. These
tools let the user specify the “similarity” of retrieved records. Data
visualization tools let the user easily and quickly view graphical displays of
information from different perspectives. Although, the term data mining is
sometimes used interchangeably with the term knowledge discovery, it is
generally accepted that data mining is one step in the knowledge discovery
process.

Decision Support Systems (DSS): Interactive computer-based systems
intended to help decision-makers utilize data and models to identify and solve
problems, and make decisions. The system must aid a decision-maker in solving
unprogrammed, unstructured (or “semi-structured”) problems. The system
must possess an interactive query facility, with a query language that is easy
to learn and use.

Modeling tools: Tools that would facilitate modeling disparate pieces of
relevant information into taxonomies (like hierarchical structures) and
ontologies (rule based associations).

Indexing and search engine tools: Tools that would crawl through various
kinds of documents and repositories and retrieve metadata about them — and
those that would map user queries into relevant result sets etc.

Intelligent agents: Software that works without the assistance of users
by making choices. The choices are based on rules of behavior that software
developers have identified and built into the software.

Connectors: The set of tools that would make the communication possible
between a corporate entity’s mail, database and (any such) legacy
application(s).

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