But does it? When people leave or retire, do we lose knowledge at the strategic, tactical or operational levels? Institutionalizing knowledge requires a comprehensive culture with an integrated approach to identifying, capturing, distributing, enhancing and using all enterprise information assets in a user-friendly manner. It combines various disciplines, including KM, BPM and instructional psychology, and aims to create a continuously learning knowledge organization.
Knowledge management is the systematic capture of insights and experiences to enable an organization to identify, create, represent, and distribute knowledge. The insights and the experiences of individuals in the organization comprise the knowledge that is created in the organization and is embedded in the form of practices and processes.
Knowledge Management is an organizational function that concerns itself with the capture, storage, and dissemination of the knowledge that is inherent in the organization by using software or a Processual tool to capture, store, and disseminate knowledge. The objective of knowledge management is to enhance organizational competitiveness, improve performance, the sharing of lessons learnt, and the continuous improvement of the organizational processes. Typically, organizations have well-established tools and software to capture, store, and disseminate the learning’s that accrue because of the organizational processes.
The goal of a knowledge management system is to provide managers with the ability to organize and locate relevant content and the expertise required to address specific business tasks and projects. Some knowledge management systems can analyze the relationships between content, people, topics and activity and produce a knowledge map report or knowledge management dashboard.
What are the components of a knowledge management framework? At the most basic level, KM consists of the following steps:
There are essentially three questions that a knowledge management framework may choose to answer:
What/How - refers to the actual processes of knowledge management.
Why - refers to an indication of the reasons behind using one method or the other.
When - refers to the timing for using one method or another, and is very closely related to "why"
A KM framework outlined by Botha et al (2008) titled the "knowledge management broad categories" is given below, which focuses on how new knowledge creation is more important than just knowledge sharing/access/etc,
You don't know
Explore, Research, Create
Knowledge Sharing and Transfer
Knowledge you have
Knowledge you don't have
Knowledge management systems refer to any kind of IT system that stores and retrieves knowledge, improves collaboration, locates knowledge sources, mines repositories for hidden knowledge, captures and uses knowledge, or in some other way enhances the KM process.
Tools and systems that can help knowledge management (KM) fulfil its goals.
Groupware is a term that refers to technology designed to help people collaborate and includes a wide range of applications.
Communication tools: Tools for sending messages and files, including email, webpublishing, wikis, filesharing, etc.
Conferencing tools: e.g. video/audio conferencing, chat, forums, etc.
Collaborative management tools: Tools for managing group activities, e.g. project management systems, workflow systems, information management systems, etc.
The intranet is essentially a small-scale version of the internet, operating with similar functionality, but existing solely within the firm. Like the internet, the intranet uses network technologies such as Transmission Control Protocol/Internet Protocol (TCP/IP). It allows for the creation of internal networks with common internet applications that can allow them to communicate with different operating systems (Newell et al 2000).
The extranet is an extension of the intranet to the firm's external network, including partners, suppliers and so on. The term is sometimes used to refer to a supplementary system working alongside the intranet or to a part of the intranet that is made available to certain external users.
The data warehouse provides the data foundation for the data - the place where the data that goes into the process of knowledge discovery is stored. Data mining may be used to automatically perform knowledge discovery by giving the mining algorithm loose cues about potential relationships and letting the algorithm work on the data to discover the relationships and items to focus on further. OLAP is complimentary to data mining and is most likely the first, and most preferred, manner of discovering knowledge. OLAP works through a user performing specific, rather than general, interactive analysis with the data. If a data warehouse is present in the environment, either it or a data mart, would be the database used by OLAP.
The role of these systems is to access and manipulate data. They usually work with a data warehouse, use an online analytical processing system (OLAP), and employ data mining techniques. The goal is to enhance decision-making and solve problems by working with the manager rather than replacing him.
Content management systems are very relevant to knowledge management (KM) since they are responsible for the creation, management, and distribution of content on the intranet, extranet, or a website.
Document management systems, as the name implies, are systems that aid in the publishing, storage, indexing, and retrieval of documents. Although such systems deal almost exclusively with explicit knowledge, the sheer volume of documents that an organization has to deal with makes them useful and in some cases even mandatory.
Knowledge management takes advantage of AI tools used to capture, filter, represent or apply knowledge. Using for example knowledge repositories like corporate Wikis or document storages, AI tools provide applications for the selection, parsing, analysis and classification of text, automated reasoning and visualisations to facilitate decision-making. Further, AI provides the means to process human input such as handwriting and voice recognition with the help of the advent of natural language processing. Moreover, expert and AI recommender systems help to boost knowledge management and provide the intelligence to use infrastructure more efficiently. Even more, AI helps users in the field of knowledge engineering and enables knowledge workers to see benefits in the usage of knowledge management tools.