| Option Matrix Service Framework - Knowledge Management |
| Knowledge Management ('KM') comprises a range of practices used by organizations to identify, create, represent, and distribute knowledge. It has been an established discipline since 1995 with a body of university courses and both professional and academic journals dedicated to it. Many large companies have resources dedicated to Knowledge Management, often as a part of 'Information Technology' or 'Human Resource Management' departments. Knowledge Management is a multi-billion dollar world-wide market. |
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| Overview |
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| Knowledge management may refer to the ways organizations collect, manage, and use the knowledge that they obtain. |
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| Knowledge Management programs are typically tied to organizational objectives such as improved performance, competitive advantage, innovation, developmental processes, lessons learnt transfer (for example between projects) and the general development of collaborative practices. |
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| Knowledge management is a term applied to techniques utilized for the methodical compilation, transfer, security and management of information in organizations, along with schemes designed to aid in making best use of that knowledge. |
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| Specifically, it refers to tools and techniques intended to safeguard the availability of information that is held by key persons and make decision-making easy. It also has a role to play in reducing risk. It is both a software market as well as an area in consultancy practice, associated with disciplines such as competitive aptitude. |
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| Knowledge management moreover designates an approach towards enhancing organizational results and organizational learning. This is achieved with the introduction of a collection of particular processes and practices for categorizing and capturing knowledge, experience, expertise and other intellectual resources. It also implies making such knowledge assets are accessible for transfer and are able to be used across the organization. |
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| Knowledge management programs are, in general, tied to definite organizational goals and are projected to lead to the achievement of particular targeted results such as superior performance, competitive advantage, or higher levels of modernization. |
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| Knowledge transfer, an aspect of knowledge management, has constantly existed in one form or another, such as through on-the-job discussions with peers, officially through apprenticeship, specialized training and mentoring programs, and recently technically through knowledge bases, professional systems, and other knowledge repositories. Knowledge management programs seek to intentionally appraise and manage the process of growth and application of intellectual capital. |
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| Although knowledge management programs are quite similar to organizational learning initiatives, knowledge management may be differentiated from organizational learning due to its greater focus on the management of particular knowledge assets. |
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| Knowledge Management System |
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| Knowledge Management System (KM System) refers to a (generally IT based) system for managing knowledge in organizations, supporting creation, capture, storage and dissemination of information. It can comprise a part (neither necessary or sufficient) of a Knowledge Management initiative. |
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| The idea of a KM system is to enable employees to have ready access to the organization's based documented of facts, sources of information, and solutions. For example a typical claim justifying the creation of a KM system might run something like this: an engineer could know the metallurgical composition of an alloy that reduces sound in gear systems. Sharing this information organization wide can lead to more effective engine design and it could also lead to ideas for new or improved equipment. |
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| A KM system could be any of the following: |
1. Document based i.e. any technology that permits creation/management/sharing of formatted documents such as Lotus Notes, web, distributed databases etc.
2. Ontology/Taxonomy based: these are similar to document technologies in the sense that a system of terminologies (i.e. ontology) are used to summarize the document e.g. Author, Subj, Organization etc. as in DAML & other XML based ontologies
3. Based on AI technologies which use a customized representation scheme to represent the problem domain.
4. Provide network maps of the organisation showing the flow of communication between entities and individuals
5. Increasingly social computing tools are being deployed to provide a more organic approach to creation of a KM system. |
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| KMS systems deal with information (although Knowledge Management as a discipline may extend beyond the information centric aspect of any system) so they are a class of information system and may build on, or utilize other information sources. Distinguishing features of a KMS can include: |
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1. Purpose: a KMS will have an explicit Knowledge Management objective of some type such as collaboration, sharing good practice or the like.
2. Context: One perspective on KMS would see knowledge is information that is meaningfully organized, accumulated and embedded in a context of creation and application.
3. Processes: KMS are developed to support and enhance knowledge-intensive processes, tasks or projects of e.g., creation, construction, identification, capturing, acquisition, selection, valuation, organization, linking, structuring, formalization, visualization, transfer, distribution, retention, maintenance, refinement, revision, evolution, accessing, retrieval and last but not least the application of knowledge, also called the knowledge life cycle.
4. Participants: Users can play the roles of active, involved participants in knowledge networks and communities fostered by KMS, although this is not necessarily the case. KMS designs are held to reflect that knowledge is developed collectively and that the "distribution" of knowledge leads to its continuous change, reconstruction and application in different contexts, by different participants with differing backgrounds and experiences.
5. Instruments: KMS support KM instruments, e.g., the capture, creation and sharing of the codifiable aspects of experience, the creation of corporate knowledge directories, taxonomies or ontologies, expertise locators, skill management systems, collaborative filtering and handling of interests used to connect people, the creation and fostering of communities or knowledge networks. |
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| A KMS offers integrated services to deploy KM instruments for networks of participants, i.e. active knowledge workers, in knowledge-intensive business processes along the entire knowledge life cycle. |
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| Key concepts in Knowledge Management |
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| Dimensions of knowledge |
| A key distinction made by the majority of knowledge management practitioners is Nonaka's reformulation of Polanyi's distinction between tacit and explicit knowledge. The former is often subconscious, internalized, and the individual may or may not be aware of what he or she knows and how he or she accomplishes particular results. At the opposite end of the spectrum is conscious or explicit knowledge -- knowledge that the individual holds explicitly and consciously in mental focus, and may communicate to others. In the popular form of the distinction, tacit knowledge is what is in our heads, and explicit knowledge is what we have codified. |
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| The focus upon codification and management of explicit knowledge has allowed knowledge management practitioners to appropriate prior work in information management, leading to the frequent accusation that knowledge management is simply a repackaged form of information management. |
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| Critics have argued that Nonaka and Takeuchi's distinction between tacit and explicit knowledge is oversimplified and that the notion of explicit knowledge is self-contradictory. Specifically, for knowledge to be made explicit, it must be translated into information (i.e., symbols outside of our heads). |
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| Another common framework for categorizing the dimensions of knowledge discriminates between embedded knowledge as knowledge which has been incorporated into an artifact of some type (for example an information system may have knowledge embedded into its design); and embodied knowledge as representing knowledge as a learned capability of the body's nervous, chemical, and sensory systems. These two dimensions, while frequently used, are not universally accepted. |
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| It is also common to distinguish between the creation of "new knowledge" (i.e., innovation) vs. the transfer of "established knowledge" within a group, organization, or community. Collaborative environments such as communities of practice or the use of social computing tools can be used for both creation and transfer. |
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| Knowledge access stages |
| Knowledge may be accessed at three stages: before, during, or after knowledge-related activities. Some people would argue that there is a life cycle to knowledge use. Starting with capture (although that word is itself contentious) or creation, moving on to use and reuse with the ultimate goal of enriching an organisation's capability. In counter to this many would state that such a life cycle view is too linear in nature and reflects an information centric view. |
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| For example, individuals undertaking a new project for an organization might access information resources to identify lessons learned for similar projects, access relevant information again during the project implementation to seek advice on issues encountered, and access relevant information afterwards for advice on after-project actions and review activities. Knowledge management practitioners offer systems, repositories, and corporate processes to encourage and formalize these activities with varying degrees of success. |
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| Similarly, knowledge may be accessed before the project implementation, for example as the project team learns lessons during the initial project analysis. Similarly, lessons learned during the project operation may be recorded, and after-action reviews may lead to further insights and lessons being recorded for future access. Note: In this context recording knowledge relates only to those aspects of knowledge which can be codified as text, or drawings. |
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| Different organizations have tried various knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans. There is considerable controversy over whether incentives work or not in this field and no firm consensus has emerged. |
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| Adhoc knowledge access |
| One alternative strategy to encoding knowledge into and retrieving knowledge from a knowledge repository such as a database, is for individuals to make knowledge requests of subject matter experts on an ad hoc basis. A key benefit claimed for this strategy is that the response from the expert individual is rich in content and contextualized to the particular problem being addressed and personalized to the particular person or people addressing it. The downside of this strategy is that it is tied to the availability and memory recall skill of specific individuals in the organization. It does not capture their insights and experience for future use should they leave or become unavailable, and also does not help in the case when particular technical issues or problems previously faced change with time to the point where a new synthesis is required, the experts' memories being out of date. The emergence of narrative approaches to knowledge management attempts to provide a bridge between the formal and the ad hoc, by allowing knowledge to be held in the form of stories. |
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| Knowledge Management Challenges |
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| Most of the challenges in knowledge management primarily stem from the types of knowledge reuse situations and purposes. Knowledge workers may produce knowledge that they themselves reuse while working. However, each knowledge re-use situation is unique in terms of requirements and context. Whenever these differences between the knowledge re-use situations are ignored, the organization faces various challenges in implementing its knowledge management practices. Some of the common challenges resulting due to this and other factors are listed below. |
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| Data Accuracy: Valuable raw data generated by a particular group within an organization may need to be validated before being transformed into normalized or consistent content. |
| Data Interpretation: Information derived by one group may need to be mapped to a standard context in order to be meaningful to someone else in the organization. |
| Data Relevancy: The quality and value of knowledge depend on relevance. Knowledge that lacks relevance simply adds complexity, cost, and risk to an organization without any compensating benefits. If the data does not support or truly answer the question being asked by the user, it requires the appropriate meta-data (data about data) to be held in the knowledge management solution. |
| Ability of the data to support/deny hypotheses: Does the information truly support decision-making? Does the knowledge management solution include a statistical or rule-based model for the workflow within which the question is being asked? |
| Adoption of knowledge management solutions: Do organizational cultures foster and support voluntary usage of knowledge management solutions? |
| Knowledge bases tend to be very complex and large: When knowledge databases become very large and complex, it puts the organization in a fix. The organization could cleanse the system of very old files, thus diluting its own knowledge management initiative. Alternatively, it could set up another team to cleanse the database of redundant files, thus increasing its costs substantially. Apart from these, the real challenge for an organization could be to monitor various departments and ensure that they take responsibility for keeping their repositories clean of redundant files. |
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