Core issues in Knowledge Management

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).