Knowledge management (KM) is the process of creating, sharing, using and managing the knowledge and information of an organisation. It refers to a multidisciplinary approach to achieving organisational objectives by making the best use of knowledge.
Many large companies, public institutions and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their business strategy, IT, or human resource management departments. Several consulting companies provide advice regarding KM to these organisations.
In the enterprise, early collections of case studies recognised the importance of knowledge management dimensions of strategy, process and measurement. Key lessons learned include people and the cultural norms which influence their behaviors are the most critical resources for successful knowledge creation, dissemination and application; cognitive, social and organisational learning processes are essential to the success of a knowledge management strategy; and measurement, benchmarking and incentives are essential to accelerate the learning process and to drive cultural change. In short, knowledge management programs can yield impressive benefits to individuals and organisations if they are purposeful, concrete and action-orientated.
KM emerged as a scientific discipline in the early 1990s. It was initially supported by individual practitioners, when Skandia hired Leif Edvinsson of Sweden as the world's first Chief Knowledge Officer (CKO). Hubert Saint-Onge (formerly of CIBC, Canada), started investigating KM long before that. The objective of CKOs is to manage and maximise the intangible assets of their organisations. Gradually, CKOs became interested in practical and theoretical aspects of KM, and the new research field was formed. The KM idea has been taken up by academics, such as Ikujiro Nonaka (Hitotsubashi University), Hirotaka Takeuchi (Hitotsubashi University), Thomas H. Davenport (Babson College) and Baruch Lev (New York University).
In 2001, Thomas A. Stewart, former editor at Fortune magazine and subsequently the editor of Harvard Business Review, published a cover story highlighting the importance of intellectual capital in organisations. The KM discipline has been gradually moving towards academic maturity. First, is a trend toward higher cooperation among academics; single-author publications are less common. Second, the role of practitioners has changed. Their contribution to academic research declined from 30% of overall contributions up to 2002, to only 10% by 2009. Third, the number of academic knowledge management journals has been steadily growing, currently reaching 27 outlets.
Multiple KM disciplines exist; approaches vary by author and school. As the discipline matured, academic debates increased regarding theory and practice, including:
Regardless of the school of thought, core components of KM roughly include people/culture, processes/structure and technology. The details depend on the perspective. KM perspectives include:
The practical relevance of academic research in KM has been questioned with action research suggested as having more relevance and the need to translate the findings presented in academic journals to a practice.
Different frameworks for distinguishing between different 'types of' knowledge exist. One proposed framework for categorising the dimensions of knowledge distinguishes tacit knowledge and explicit knowledge. Tacit knowledge represents internalised knowledge that an individual may not be consciously aware of, such as to accomplish particular tasks. At the opposite end of the spectrum, explicit knowledge represents knowledge that the individual holds consciously in mental focus, in a form that can easily be communicated to others.
The Knowledge Spiral as described by Nonaka & Takeuchi.
Ikujiro Nonaka proposed a model (SECI, for Socialisation, Externalisation, Combination, Internalisation) which considers a spiraling interaction between explicit knowledge and tacit knowledge. In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 're-internalised' into implicit knowledge.
Hayes and Walsham (2003) describe knowledge and knowledge management as two different perspectives. The content perspective suggests that knowledge is easily stored; because it may be codified, while the relational perspective recognises the contextual and relational aspects of knowledge which can make knowledge difficult to share outside the specific context in which it is developed.
Early research suggested that KM needs to convert internalised tacit knowledge into explicit knowledge to share it, and the same effort must permit individuals to internalise and make personally meaningful any codified knowledge retrieved from the KM effort.
Subsequent research suggested that a distinction between tacit knowledge and explicit knowledge represented an oversimplification 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 our heads). More recently, together with Georg von Krogh and Sven Voelpel, Nonaka returned to his earlier work in an attempt to move the debate about knowledge conversion forward.
A second proposed framework for categorising knowledge dimensions distinguishes embedded knowledge of a system outside a human individual (e.g., an information system may have knowledge embedded into its design) from embodied knowledge representing a learned capability of a human body's nervous and endocrine systems.
A third proposed framework distinguishes between the exploratory creation of "new knowledge" (i.e., innovation) vs. the transfer or exploitation of "established knowledge" within a group, organisation, or community. Collaborative environments such as communities of practice or the use of social computing tools can be used for both knowledge creation and transfer.
Knowledge may be accessed at three stages: before, during, or after KM-related activities. Organisations have tried knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans. Considerable controversy exists over whether such incentives work and no consensus has emerged.
One strategy to KM involves actively managing knowledge (push strategy). In such an instance, individuals strive to explicitly encode their knowledge into a shared knowledge repository, such as a database, as well as retrieving knowledge they need that other individuals have provided (codification).
Another strategy involves individuals making knowledge requests of experts associated with a particular subject on an ad hoc basis (pull strategy). In such an instance, expert individual(s) provide insights to requestor (personalisation).
Hansen et al. defined the two strategies. Codification focuses on collecting and storing codified knowledge in electronic databases to make it accessible. Codification can therefore refer to both tacit and explicit knowledge. In contrast, personalisation encourages individuals to share their knowledge directly. Information technology plays a less important role, as it is only facilitates communication and knowledge sharing.
Other knowledge management strategies and instruments for companies include:
Knowledge sharing (fostering a culture that encourages the sharing of information, based on the concept that knowledge is not irrevocable and should be shared and updated to remain relevant)
Make knowledge-sharing a key role in employees' job description
Inter-project knowledge transfer
Intra-organisational knowledge sharing
Inter-organisational knowledge sharing
Proximity & architecture (the physical situation of employees can be either conducive or obstructive to knowledge sharing)
Knowledge management (KM) technology can be categorised:
Groupware—Software that facilitates collaboration and sharing of organisational information. Such applications provide tools for threaded discussions, document sharing, organisation-wide uniform email, and other collaboration-related features.
Workflow systems—Systems that allow the representation of processes associated with the creation, use and maintenance of organisational knowledge, such as the process to create and utilise forms and documents.
Content management and document management systems—Software systems that automate the process of creating web content and/or documents. Roles such as editors, graphic designers, writers and producers can be explicitly modeled along with the tasks in the process and validation criteria. Commercial vendors started either to support documents or to support web content but as the Internet grew these functions merged and vendors now perform both functions.
Enterprise portals—Software that aggregates information across the entire organisation or for groups such as project teams.
eLearning—Software that enables organisations to create customised training and education. This can include lesson plans, monitoring progress and online classes.
Telepresence—Software that enables individuals to have virtual "face-to-face" meetings without assembling at one location. Videoconferencing is the most obvious example.
Ontological Approach—An ontology-based knowledge model for knowledge management. This model can facilitate knowledge discovery that provides users with insight for decision making.
These categories overlap. Workflow, for example, is a significant aspect of a content or document management systems, most of which have tools for developing enterprise portals.
Proprietary KM technology products such as Lotus Notes defined proprietary formats for email, documents, forms, etc. The Internet drove most vendors to adopt Internet formats. Open-source and freeware tools for the creation of blogs and wikis now enable capabilities that used to require expensive commercial tools.
KM is driving the adoption of tools that enable organisations to work at the semantic level, as part of the Semantic Web. Some commentators have argued that after many years the Semantic Web has failed to see widespread adoption, while other commentators have argued that it has been a success.
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^ abcHayes, M.; Walsham, G. (2003). "Knowledge sharing and ICTs: A relational perspective". In Easterby-Smith, M.; Lyles, M.A. (eds.). The Blackwell Handbook of Organizational Learning and Knowledge Management. Malden, MA: Blackwell. pp. 54–77. ISBN978-0-631-22672-7.
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^Bakke, Sturla; ygstad, Bendik (May 2009). "Two emerging technologies: a comparative analysis of Web 2.0 and the Semantic Web". CONF-IRM 2009 Proceedings (28). Our research question is: how do we explain the surprising success of Web 2.0 and the equally surprising non-fulfillment of the Semantic Web. Building on a case study approach we conducted a in depth comparative analysis of the two emerging technologies. We propose two conclusions. First, traditional top-down management of an emerging global technology has proved not to be effective in the case of the Semantic Web and Web 2.0, and second, the success for such global technologies is mainly associated with bootstrapping an already installed base.
^Grimes, Seth (7 January 2014). "Semantic Web business: going nowhere slowly". InformationWeek. Retrieved 5 September 2017. SemWeb is a narrowly purposed replica of a subset of the World Wide Web. It's useful for information enrichment in certain domains, via a circumscribed set of tools. However, the SemWeb offers a vanishingly small benefit to the vast majority of businesses. The vision persists but is unachievable; the business reality of SemWeb is going pretty much nowhere.
^Cagle, Kurt (3 July 2016). "Why the Semantic Web has failed". LinkedIn. Retrieved 5 September 2017. This may sound like heresy, but my personal belief is that the semantic web has failed. Not in "just give it a few more years and it'll catch on" or "it's just a matter of tooling and editors". No, I'd argue that, as admirable as the whole goal of the semantic web is, it's just not working in reality.
^Zaino, Jennifer (23 September 2014). "The Semantic Web's rocking, and there ain't no stopping it now". dataversity.net. Retrieved 5 September 2017. Make no mistake about it: The semantic web has been a success and that's not about to stop now. That was essentially the message delivered by W3C Data Activity Lead Phil Archer, during his keynote address celebrating the semantic web's ten years of achievement at last month's Semantic Technology & Business Conference in San Jose.