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ISM/U3 Topic 3 Knowledge Management

Knowledge management and collaboration systems are among the fastest growing areas of corporate and government software investment. The past decade has shown an explosive growth in search on knowledge and knowledge management in the economics, management, and information systems fields.

Important Dimensions of Knowledge

Data as a flow of events or transactions captured by an organization’s systems that, by itself, is useful for transaction but little else. To transform information into knowledge, a firm must expend additional resources to discover patterns, rules, and contexts where the knowledge works. Wisdom is thought to be the collective and individual experience of applying knowledge to the solution of problems. Wisdom involves where, when, and how to apply knowledge. Knowledge residing in the mind of employees that has not been documented is called tacit knowledge, whereas knowledge that has been documented is called explicit knowledge.

The Knowledge Management Value Chain

The knowledge management refers to the set of business processes developed in an organization to create, store, transfer, and apply knowledge.

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Fig. The Knowledge Management Value Chain

Knowledge Acquisition

Organizations acquire knowledge in a number of ways, depending on the type of knowledge they seek. 

Knowledge Storage

Once they are discovered, documents, patterns, and expert rules must be stored so they can be retrieved and used by employees.

Knowledge Dissemination

Portals, e-mail, instant messaging, wikis, social networks, and search engines technology have added to an existing array of collaboration technologies and office systems for sharing calendars, documents, data, and graphics.

Knowledge Application

Regardless of what type of knowledge management system is involved, knowledge that is not shared and applied to the practical problems facing firms and managers does not add business value. To provide a return on investment, organizational knowledge must become a systematic part of management decision making and become situated in decision-support system.

Types of Knowledge Management Systems

There are essentially three major types of knowledge management systems: enterprise-wide knowledge management systems, knowledge work systems, and intelligent techniques. 

Enterprise-wise knowledge management systems are general-purpose firmwide efforts to collect, store, distribute, and apply digital content and knowledge. Knowledge work system (KWS) are specialized systems built for engineers, scientists, and other knowledge workers charged with discovering and creating new knowledge for a company. Knowledge management also includes a diverse group of intelligent techniques, such as data mining, expert systems, neural networks, fuzzy logic, genetic algorithms, and intelligent agents.

Enterprise Content Management Systems

It help organizations manage both types of information. They have capabilities for knowledge capture, storage, retrieval, distribution, and preservation to help firms improve their business processes and decisions. Digital asset management systems help companies classify, store, and distribute these digital objects.

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Fig. An Enterprise Content Management System

Knowledge Network Systems

Knowledge network systems, also known as expertise location and management systems, address the problem that arises when the appropriate knowledge is not in the form of a digital document but instead resides in the memory of expert individuals in the firm.

Collaboration Tools And Learning Management Systems

The major enterprise content management systems include powerful portal and collaboration technologies. Enterprise knowledge portals can provide access to external sources of information such as news feeds and research, as well as to internal knowledge resources along with capabilities for e-mail, chat/instant messaging, discussion groups, and videoconferencing. Social bookmarking makes it easier to search for and share information by allowing users to save their bookmarks to Web pages on a public Web site and tag these bookmarks with keywords. The user-created taxonomies created for shared bookmarks are called folksonomies.

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Fig. An Enterprise Knowledge Network System

Knowledge Workers and Knowledge Work

Knowledge workers, include researchers, designers, architects, scientists, and engineers who primarily create knowledge and information for the organization. They perform three key roles that are critical to the organization and to the managers who work within the organization:

  • Keeping the organization current in knowledge as it develops in the external world – in technology, science, social thought, and the arts
  • Serving as internal consultants regarding the areas of their knowledge, the changes taking place, and opportunities
  • Acting as change agents, evaluating, initiating, and promoting change projects

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Fig. Requirements of Knowledge Work Systems

Intelligent Techniques

Artificial intelligence and database technology provide a number of intelligent techniques that organizations can use to capture individual and collective knowledge and to extend their knowledge base. Neural networks and data mining are used for knowledge discovery. Artificial intelligence (AI) technology, which consists of computer-based systems (both hardware and software) that attempt to emulate human behavior. 

Capturing Knowledge: Expert Systems

Expert systems are an intelligent technique for capturing tacit knowledge in an very specific and limited domain of human expertise. 

Organizational Intelligence: Case-Based Reasoning

Expert systems primarily capture the tacit knowledge of individual experts, but organizations also have collective knowledge and expertise that they have built up over the years. In case-based reasoning (CBR), descriptions of past experiences of human specialists, represented as cases, are stored in a database for later retrieval when the user encounters a new case with similar parameters.

Fuzzy Logic Systems

Fuzzy logic is a rule-based technology that can represent such impression by creating rules that use approximate or subjective values.

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Fig. How Case-Based Reasoning Works

Neural Networks

Neural networks are used for solving complex, poorly understood problems for which large amounts of data have been collected. They find patterns and relationships in massive amounts of data that would be too complicated and difficult for a human being to analyze. Neural networks discover this knowledge by using hardware and software that parallel the processing patterns of the biological or human brain.

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Fig. How A Neural Network Works

Genetic Algorithms

Genetic algorithms are useful for finding the optimal solution for a specific problem by examining a very large number of possible solutions for that problem. They are based on techniques inspired by evolutionary biology, such as inheritance, mutation, selection, and crossover (recombination).

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Fig. The Components of A Genetic Algorithm

Hybrid AI Systems

Genetic algorithms, fuzzy logic, neural networks, and expert systems can be integrated into a single application to take advantage of the best features of these technologies. Such systems are called hybrid AI systems

Intelligent Agents

Intelligent agents are software programs that work in the background without direct human intervention to carry out specific, repetitive, and predictable tasks for an individual user, business process, or software application. Agent-based modeling applications have been developed to model the behavior of consumers, stock markets, and supply chains and to predict the spread of epidemics.

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