knowledge moment

Knowledge Management Systems, Technologies and Tools I

• KM technologies

Knowledge Management requires technologies to support the new strategies, processes, methods and techniques to better create, disseminate, share and apply the best knowledge, anytime and anyplace, across the team, across teams, across the organisation and across several organisations, especially its clients, customers, partners, suppliers and other key stakeholders.

The key technologies are communication and collaboration technologies that are web based for internet and intranet usage, as well as mobile technologies such as PDA’s, PC’s, telephone and videoconferencing. New technologies are rapidly emerging that act as intelligent agents and assistants to search, summarise, conceptualise and recognise patterns of information and knowledge.

For an effective KM initiative across the organisation, there needs to be in place, at least:

▪ Knowledge Portal

There is often confusion between the terms ‘information portal’ and ‘knowledge portal’.

An information portal is often described as a gateway to information to enable the user to have one, more simplified way of navigating towards the desired information.

However a ‘knowledge portal’ is far more than an information portal because, as well as information navigation and access, it contains within it software technologies to, at least, support the processes of virtual team communication and collaboration and software technologies to support the 9 step process of managing knowledge. Furthermore, it contains intelligent agent software to identify and automatically distribute information and knowledge effectively to knowledge workers based on knowledge profiling.

▪ Knowledge Profiles

Within the knowledge portal, each knowledge worker can update and maintain a personal ‘knowledge profile’ which identifies his/her specific knowledge needs, areas of interest and frequency of distribution.

▪ Collaborative workspaces

Within the knowledge portal, shared work spaces can be set up for each new team or project. These will become knowledge repositories from which new knowledge will be distilled regularly and systematically and shared across other teams in the organisation. Within the shared and collaborative workspace, at least, the following communication and collaboration functions could be performed:

▪ Urgent requests

Within the knowledge portal, it is very useful to have a facility and underlying process to enter any ‘Urgent Request’ into the portal and receive back any responses from across the organisation. Rather than needing to know ‘who might know’ the request is entered blindly and responses will be made if it is known in the organisation and people are willing to support and respond to this activity. This is a very effective way of better leveraging the knowledge across the organisation.

▪ Document Libraries

The document library is typically the location where all documents are stored. The library should be context relative and allow the ease of control over any document type. Many organisations now employ an Electronic Document and Records Management System (EDRMS) for this requirements but the integration of the EDRMS with all other relevant information and knowledge sources is imperative.

▪ Knowledge Server and services

In order to foster knowledge networking across the entire organisation and support knowledge processes for creating, retaining, leveraging, reusing, measuring and optimising the use of the organisational knowledge assets, a centralised knowledge server is required that will:

▪ manage the communications and collaboration between networks of people

▪ enable the access, creation and sharing of knowledge between them

The centralised knowledge server will manage the processes and knowledge services that generate and disseminate knowledge assets.

The key components of a generic knowledge server are:

▪ a knowledge portal interface designed around a knowledge asset schema (see KM consulting section) as a gateway to user access, security and applications

▪ Knowledge banks

▪ Advanced search capabilities ▪ collaboration services ▪ search and discovery services ▪ publishing services based on user knowledge needs and knowledge profiling ▪ a knowledge map (taxonomy) ▪ knowledge repository for information and process management ▪ Text summarising and conceptualising ▪ Intelligent agentware ▪ an Intranet infrastructure for integrated email, file servers, internet/intranet services

Knowledge Bases (Banks)

For each key knowledge area identified, there needs to be a Knowledge Base.

A Knowledge Base contains:

▪ both structured and unstructured discussion forums

▪ rich ‘knowledge objects’ that have been designed for the efficient and effective transfer of knowledge using multimedia, video, audio

▪ embedded communications theory (eg storytelling)

▪ KM processes to:

▪ critically review knowledge nominations and turn them into improved knowledge

▪ automatically find and publish knowledge to users according to users knowledge profiles

▪ transfer knowledge effectively

• Artificial intelligence as a facilitating technology for knowledge management

AI is “buzzword” in the industry today. However, AI has been around for decades. The intent of AI is to enable computers to perform tasks that normally require human intelligence, as such AI will evolve to take many jobs once performed by humans.

KM and AI are at their core about knowledge. AI provides the mechanisms to enable machines to learn. AI allows machines to acquire, process and use knowledge to perform tasks and to unlock knowledge that can be delivered to humans to improve the decision-making process.

AI and KM are closely related. KM allows an understanding of knowledge to occur, while AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined.

The connection of KM and AI has lead the way for cognitive computing.

Cognitive computing uses computerized models to simulate human thought processe--s. Cognitive computing involves self/deep learning artificial neural network software that use text/data mining, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive computing is leading the way for future applications involving AI and KM.

In recent years, the ability to mine larger amounts of data, information and knowledge to gain competitive advantage and the importance of data and text analytics to this effort is gaining momentum. As the proliferation of structured and unstructured data continues to grow we will continue to have a need to uncover the knowledge contained within these big data resources. Cognitive computing will be key in extracting knowledge from big data. Strategy, process centric approaches and interorganizational aspects of decision support to research on new technology and academic endeavors in this space will continue to provide insights on how we process big data to enhance decision making.

• Knowledge capture systems and knowledge engineering

Knowledge Capture Systems support the process of retrieving either explicit or tacit knowledge that resides within people, artifacts, or organizational entities. These systems can help capture knowledge that resides within or outside organizational boundaries including within consultants, competitors, customers, suppliers, and prior employers of the organization's new employees.

The earliest mechanisms for knowledge capture dates to the anthropological use of stories - the earliest form of art, education and entertainment. Storytelling is the mechanism by which early civilizations passed on their values and their wisdom from one generation to the next.

The importance of using metaphors and stories as a mechanism for capturing and transferring tacit knowledge is increasingly drawing the attention of organizations.

Technology can also facilitate capturing the knowledge of experts. One type of knowledge capture system is based on the use of concept maps as a knowledge-modeling tool. A concept map is a diagram showing the relationships among concepts.

In the 1960s, Joseph D. Novak (1993) at Cornell University began to study the concept mapping technique. His work was based on the theories of David Ausubel (1968), who stressed the importance of prior knowledge in being able to learn about new concepts. Novak concluded that "Meaningful learning involves the assimilation of new concepts and propositions into existing cognitive structures."

A concept map is a graphical representation where nodes (points or vertices) represent concepts, and links (arcs or lines) represent the relationships between concepts. The concepts, and sometimes the links, are labelled on the concept map. The links between the concepts can be one-way, two-way, or non-directional. The concepts and the links may be categorized, and the concept map may show temporal or causal relationships between concepts (Plotnick, 1997).

Concept maps provide an effective methodology to organize and structure the concepts representing the expert's domain knowledge.

Knowledge engineering