Knowledge management (KM) is the process of creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieve organisational objectives by making the best use of knowledge.
An established discipline since 1991, KM includes courses taught in the fields of business administration, information systems, management, library, and information science. Other fields may contribute to KM research, including information and media, computer science, public health and public policy. Several universities offer dedicated master's degrees in knowledge management.
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 organizations.
Knowledge management efforts typically focus on organisational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, integration and continuous improvement of the organisation. These efforts overlap with organisational learning and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and on encouraging the sharing of knowledge. KM is an enabler of organizational learning.
The most complex scenario for knowledge management may be found in the context of supply chain as it involves multiple companies without an ownership relationship or hierarchy between them, being called by some authors as transorganizational or interorganizational knowledge. That complexity is additionally increased by industry 4.0 (or 4th industrial revolution) and digital transformation, as new challenges emerge from both the volume and speed of information flows and knowledge generation.
Knowledge management efforts have a long history, including on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training, and mentoring programs. With increased use of computers in the second half of the 20th century, specific adaptations of technologies such as knowledge bases, expert systems, information repositories, group decision support systems, intranets, and computer-supported cooperative work have been introduced to further enhance such efforts.
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 organizations. 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 organizations. 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.
- Techno-centric with a focus on technology, ideally those that enhance knowledge sharing and creation.
- Organisational with a focus on how an organisation can be designed to facilitate knowledge processes best.
- Ecological with a focus on the interaction of people, identity, knowledge, and environmental factors as a complex adaptive system akin to a natural ecosystem.
- community of practice
- social network analysis
- intellectual capital
- information theory
- complexity science
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.
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). When talking about strategic knowledge management, the form of the knowledge and activities to share it defines the concept between codification and personalization.  The form of the knowledge means that it’s either tacit or explicit. Data and information can be considered as explicit and know-how can be considered as tacit. 
Hansen et al. defined the two strategies (codification and personalisation). Codification strategy is document-centered strategy, where knowledge is mainly codified as “people-to-document” method. Codification relies on information infrastructure, where explicit knowledge is carefully codified and stored. 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 only facilitates communication and knowledge sharing.
- 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
- Knowledge retention also known as Knowledge Continuation: activities addressing the challenge of knowledge loss as a result of people leaving
- Mapping knowledge competencies, roles and identifying current or future predicted gaps.
- Defining for each chosen role the main knowledge that should be retained, and building rituals in which the knowledge is documented or transferred on, from the day they start their job.
- Transfer of knowledge and information prior to employee departure by means of sharing documents, shadowing, mentoring, and more,
- Proximity & architecture (the physical situation of employees can be either conducive or obstructive to knowledge sharing)
- Storytelling (as a means of transferring tacit knowledge)
- Cross-project learning
- After-action reviews
- Knowledge mapping (a map of knowledge repositories within a company accessible by all)
- Communities of practice
- Expert directories (to enable knowledge seeker to reach to the experts)
- Expert systems (knowledge seeker responds to one or more specific questions to reach knowledge in a repository)
- Best practice transfer
- Knowledge fairs
- Competency-based management (systematic evaluation and planning of knowledge related competences of individual organisation members)
- Master–apprentice relationship, Mentor-mentee relationship, job shadowing
- Collaborative software technologies (wikis, shared bookmarking, blogs, social software, etc.)
- Knowledge repositories (databases, bookmarking engines, etc.)
- Measuring and reporting intellectual capital (a way of making explicit knowledge for companies)
- Knowledge brokers (some organisational members take on responsibility for a specific "field" and act as first reference on a specific subject)
- Knowledge farming (Using note-taking software to cultivate a knowledge graph, part of Knowledge Agriculture)
- Making available increased knowledge content in the development and provision of products and services
- Achieving shorter development cycles
- Facilitating and managing innovation and organisational learning
- Leveraging expertises across the organisation
- Increasing network connectivity between internal and external individuals
- Managing business environments and allowing employees to obtain relevant insights and ideas appropriate to their work
- Solving intractable or wicked problems
- Managing intellectual capital and assets in the workforce (such as the expertise and know-how possessed by key individuals or stored in repositories)
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.
- Planning and scheduling software—Software that automates schedule creation and maintenance. The planning aspect can integrate with project management software.
- Telepresence—Software that enables individuals to have virtual "face-to-face" meetings without assembling at one location. Videoconferencing is the most obvious example.
- Semantic technology such as ontologies—Systems that encode meaning alongside data to give machines the ability to extract and infer information.
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.
Just like knowledge transfer and knowledge sharing, the term "knowledge barriers" is not a uniformly defined term and differs in its meaning depending on the author. Knowledge barriers can be associated with high costs for both companies and individuals.
Knowledge retention is part of knowledge management. Knowledge retention is needed when expert knowledge workers leave the organization after a long career.  Retaining knowledge prevents losing intellectual capital. 
Knowledge retention projects are usually introduced in three stages: decision making, planning and implementation. There are differences among researchers on the terms of the stages. For example, Dalkir talks about knowledge capture, sharing and acquisition and Doan et al. introduces initiation, implementation and evaluation.  Furthermore, Levy introduces three steps (scope, transfer, integration) but also recognizes a “zero stage” for initiation of the project.
- Archives management
- Customer knowledge
- Dynamic knowledge repository
- Electronic Journal of Knowledge Management
- Ignorance management
- Information governance
- Information management
- Journal of Knowledge Management
- Journal of Knowledge Management Practice
- Knowledge cafe
- Knowledge community
- Knowledge ecosystem
- Knowledge engineering
- Knowledge management software
- Knowledge modeling
- Knowledge transfer
- Knowledge translation
- Legal case management
- Personal knowledge management
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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.
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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.
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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.
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