Introduction
Technologists never evangelize without a disclaimer: "Technology is just an enabler." True enough - and the disclaimer discloses part of the problem: enabling what? One flaw in knowledge management is that it often neglects to ask what knowledge to manage and toward what end. Knowledge management activities are all over the map: building databases, measuring intellectual capital, establishing corporate libraries, building intranets, sharing best practices, installing groupware, leading training programs, leading cultural change, fostering collaboration, creating virtual organizations - all of these are knowledge management, and every functional and staff leader can lay claim to it. But no one claims the big question: why?

CONTENT


Introduction


What is knowledge management?

Knowledge management: a cross-disciplinary domain

KM roles and organizational structure


Knowledge capture stages

KM roles and organizational structure

Why we need knowledge management now

Conclusion

References

What is knowledge management?
Knowledge management is hard to define precisely and simply. (The definition also leapfrogs the task of defining "knowledge" itself. We’ll get to that later.) That’s not surprising. How would a nurse or doctor define "health care" succinctly? How would a CEO describe "management"? How would a CFO describe "compensation"? Each of those domains is complex, with many sub-areas of specialization. Nevertheless, we know "health care" and "management" when we see them, and we understand the major goals and activities of those domains.
Knowledge management may refer to the ways organizations gather. Man age and use the knowledge that they acquire.

The term also designates an approach to improving organizational outcomes and organization learning by introducing into an organization a range of specific processes and practices for identifying and capturing knowledge. Know-how.

Expertise and other intellectual capital. And for making such knowledge assets available for transfer and reuse across the organization.

Knowledge management programs are typically tied to specific organizational objective and are intended to lead to the achievement of specific targeted results such as improved performance. Competitive advantage or higher levels of innovation

While knowledge transfer has always existed in one form or another. For example through on-the-job discussions with peers. Formally through apprenticeship. Professional training and mentoring programs. And- since the late twentieth century –technologically through knowledge base. Expert systems. And other knowledge repositories. KM programs seek to consciously evaluate and manage the process of accumulation and application of intellectual capital. KM has therefore brought together various strands of though and practice relating to:
- intellectual capital and the knowledge worker in the knowledge worker in the knowledge economy
- the idea of the learning organization
- various enabling organizational practices such as communities of practice and corporate yellow page directories for accessing key personnel and expertise.
- And various enabling technologies such as knowledge bases and expert systems. Help desk. Corporate intranets and extranets. Content management. Wikis , and document management.
While knowledge management programs are closely related to organizational learning initiatives. Knowledge management may be differentiated from organizational learning by its greater focus on the management of specific knowledge assets.

The rise of KM has seen an increasing understanding of the relevance of the distinction between tacit and explicit knowledge. Sophisticated perspectives on the management. Assessment and use of intellectual capital. And the emergence of new organizational roles and responsibilities such as position of Chief Knowledge Officer (CKO).

Knowledge management: a cross-disciplinary domain

Knowledge management draws from a wide range of disciplines and technologies.
o Cognitive science. Insights from how we learn and know will certainly improve tools and techniques for gathering and transferring knowledge.
o Expert systems, artificial intelligence and knowledge base management systems (KBMS). AI and related technologies have acquired an undeserved reputation of having failed to meet their own — and the marketplace’s — high expectations. In fact, these technologies continue to be applied widely, and the lessons practitioners have learned are directly applicable to knowledge management.
o Computer-supported collaborative work (groupware). In Europe, knowledge management is almost synonymous with groupware … and therefore with Lotus Notes. Sharing and collaboration are clearly vital to organizational knowledge management — with or without supporting technology.
o Library and information science. We take it for granted that card catalogs in libraries will help us find the right book when we need it. The body of research and practice in classification and knowledge organization that makes libraries work will be even more vital as we are inundated by information in business. Tools for thesaurus construction and controlled vocabularies are already helping us manage knowledge.
o Technical writing. Also under-appreciated — even sneered at — as a professional activity, technical writing (often referred to by its practitioners as technical communication) forms a body of theory and practice that is directly relevant to effective representation and transfer of knowledge.
o Document management. Originally concerned primarily with managing the accessibility of images, document management has moved on to making content accessible and re-usable at the component level. Early recognition of the need to associate "metainformation" with each document object prefigures document management technology’s growing role in knowledge management activities.
o Decision support systems. According to Daniel J. Power, "Researchers working on Decision Support Systems have brought together insights from the fields of cognitive sciences, management sciences, computer sciences, operations research, and systems engineering in order to produce both computerised artifacts for helping knowledge workers in their performance of cognitive tasks, and to integrate such artifacts within the decision-making processes of modern organisations." [See Powers’ DSS Research Resources Home page.] That already sounds a lot like knowledge management, but in practice the emphasis has been on quantitative analysis rather than qualitative analysis, and on tools for managers rather than everyone in the organization.
o Semantic networks. Semantic networks are formed from ideas and typed relationships among them — sort of "hypertext without the content," but with far more systematic structure according to meaning. Often applied in such arcane tasks as textual analysis, semantic nets are now in use in mainstream professional applications, including medicine, to represent domain knowledge in an explicit way that can be shared.
o Relational and object databases. Although relational databases are currently used primarily as tools for managing "structured" data — and object-oriented databases are considered more appropriate for "unstructured" content — we have only begun to apply the models on which they are founded to representing and managing knowledge resources.
o Simulation. Knowledge Management expert Karl-Erik Sveiby suggests "simulation" as a component technology of knowledge management, referring to "computer simulations, manual simulations as well as role plays and micro arenas for testing out skills." (Source: Email from Karl-Erik Sveiby, July 29, 1996 )
o Organizational science. The science of managing organizations increasingly deals with the need to manage knowledge — often explicitly. It’s not a surprise that the American Management Association’s APQC has sponsored major knowledge management events.
That’s only a partial list. Other technologies include: object-oriented information modeling; electronic publishing technology, hypertext, and the World Wide Web; help-desk technology; full-text search and retrieval; and performance support systems.


Knowledge capture stages

Knowledge may be accessed, or captured, at three stages: before, during, or after knowledge-related activites.
For example, individuals undertaking a new project for an organization might access KM resources to learn best practices and lessons learned for similar projects undertaken previously, access the KM network again during the project, implementation to seek advice on issue encountered, and access the system afterwards for advice on after-project actions and reviews activities.
Similarly, knowledge may be captured and recorded into the system before the project implementation, for example as the project team learns information and lessons during the initial project analysis. Similarly, lessons learned during the project operation may be entered into the KM system, and after-action reviews may lead to further insights and lessons being recorded in the KM system for future access.
Drivers of KM


KM roles and organizational structure

Knowledge Management activities may be centralized in a Knowledge Management Office or responsibility for knowledge management may be located in existing departmental functions, such as the HR or IT departments.
However, many of the more successful Knowledge Management initiatives have begun in more limited, tactical areas such as customer or end user support, where metrics for success are easily quantifiable.
Organizations committed to knowledge management on an ongoing basis may create a specific position such as a chief Knowledge Officer (CKO) or similar, or assign responsibilities for management of a knowledge management program to a specific relevant organizational department.
KM lexicon


KM professionals may use a specific lexicon in order to articulate and discuss the various issues arising in Knowledge Management. For example, terms such as intellectual capital, metric, and tacit vs. explicit knowledge typically form an indispensable part of the KM professional’s vocabulary.


Why we need knowledge management now

Why do we need to manage knowledge? Ann Macintosh of the Artificial Intelligence Applications Institute (University of Edinburgh) has written a "Position Paper on Knowledge Asset Management" that identifies some of the specific business factors, including:
o Marketplaces are increasingly competitive and the rate of innovation is rising.
o Reductions in staffing create a need to replace informal knowledge with formal methods.
o Competitive pressures reduce the size of the work force that holds valuable business knowledge.
o The amount of time available to experience and acquire knowledge has diminished.
o Early retirements and increasing mobility of the work force lead to loss of knowledge.
o There is a need to manage increasing complexity as small operating companies are trans-national sourcing operations.
o Changes in strategic direction may result in the loss of knowledge in a specific area.
To these paraphrases of Ms. Macintosh’s observations we would add:
o Most of our work is information based.
o Organizations compete on the basis of knowledge.
o Products and services are increasingly complex, endowing them with a significant information component.
o The need for life-long learning is an inescapable reality.
In brief, knowledge and information have become the medium in which business problems occur. As a result, managing knowledge represents the primary opportunity for achieving substantial savings, significant improvements in human performance, and competitive advantage.
It’s not just a Fortune 500 business problem. Small companies need formal approaches to knowledge management even more, because they don’t have the market leverage, inertia, and resources that big companies do. They have to be much more flexible, more responsive, and more "right" (make better decisions) — because even small mistakes can be fatal to them.


Conclusion

This article opened with the observation that although KM activities are "all over the map" in terms of technology implementations, however, no one has asked the "big question": why? Despite diverse propositions about "getting the right information to the right person at the right time," almost everyone neglects to ask what knowledge to manage and toward what end. A review of the industry case studies of companies characterized in the recent years as RTE business enterprises surfaced some interesting insights. Recent industry analyses that have demonstrated inverse correlations between IT investments and business performance provided some motivation for these analyses. Additional impetus was provided by the contrast between the hype about "RTE technologies" propagated by some IT analysts and our in-depth analysis of companies that achieved success as RTE benchmarks. To some extent the search for the "next big thing" and the "killer application" is to blame for its narrow focus on IT and innovation as ends rather than means for achieving sustainable business.

References

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