Introduction
The information age has made information communication technology (ICT) a necessity for conducting business. This in turn has led to the exponential increase in the electronic capture of data and its storage in vast data warehouses. In order to respond quickly to fast changing markets, organizations must maximize these raw data and information resources. Specifically, they need to transform them into germane knowledge to aid superior decision-making (W ick-ramasinghe & von Lubitz, 2006). To do this effectively not only involves the analysis of the data and information but also requires the use of sophisticated tools to enable such analyses to occur. Knowledge discovery technologies represent a spectrum of new technologies that facilitate the analysis of data to find relationships from the data to finding reasons behind observable patterns (i.e., transform the data into relevant information and germane knowledge). Such new discoveries can have a profound impact on decision making in general and the designing of business strategies. With the massive increase in data being collected and the demands of a new breed of intelligent applications like customer relationship management, demand planning, and predictive forecasting, these knowledge discovery technologies are becoming competitive necessities for providing a high performance and feature rich intelligent application servers for intelligent enterprises.
Knowledge management (KM) tools and technologies are the systems that integrate various legacy systems, databases, ERP systems, and data warehouse to help facilitate an organization's knowledge discovery process. Integrating all of these with advanced decision support and online real time events enables an organization to understand customers better and devise business strategies accordingly. Creating a competitive edge is the goal of all organizations employing knowledge discovery for decision support (Thorne & Smith, 2000).
The following provides a synopsis of the major tools and critical considerations required to enable an organization to successfully effect appropriate knowledge sharing, knowledge distribution, knowledge creation, as well as knowledge capture and codification processes and hence embrace effective knowledge management (KM) techniques and advanced knowledge discovery.
background
A necessary but not sufficient consideration to facilitate the generation of superior knowledge discovery solutions is the establishment of a sound KM infrastructure (Wickramas-inghe et al., 2006). The KM infrastructure, in terms of tools and technologies, (hardware as well as software) should be established so that knowledge can be created from any new event or activity, which in turn will ensure that the extant knowledge base continuously grows (Wickramasinghe, Fadlalla, Geisler, & Schaffer, 2003; Wickramasinghe & Bali, 2006). The entire new know-how or new knowledge can only be created for exchange if the KM infrastructure is established effectively. Critical components of such a KM infrastructure include a repository of knowledge, and networks to distribute the knowledge to the members of organization and a facilitator system for the creation of new knowledge. Such a knowledge-based infrastructure will foster the creation of knowledge, and provide an integrated system to share and diffuse the knowledge in the organization (Srikantaiah & Koenig, 2000).
knowledge architecture
Architecture, specifically the information technology architecture is an integrated set of technical choices used to guide an organization in satisfying its business needs (Weil & Broadbent, 1998). Underlying the knowledge architecture (Wickramasinghe, 2003; Wickramasinghe, 2005; refer to Figure 1) is the recognition of the binary nature of knowledge; namely its objective and subjective components. What we realize when we analyze the knowledge architecture closely, is that knowledge is not a clearly defined, easily identifiable phenomenon, rather it has many forms which makes managing it even more challenging (Schultz & Leidner, 2002; Wickramasinghe, 2005).
The knowledge architecture depicted in Figure 1 recognizes the two different, yet key aspects of knowledge; namely, knowledge as an object and a subject. By doing so, it provides the blue prints for an all encompassing knowledge management system (KMS). The pivotal function underlined by the knowledge architecture is the flow of knowledge. The flow of knowledge is fundamentally enabled (or not) by the knowledge management system.
Figure 1. The knowledge architecture
KNOWLEDGE MANAGEMENT SYSTEM
Given the importance of knowledge, systems are being developed and implemented in organizations that aim to facilitate the sharing and integration ofknowledge (i.e., support and facilitate the flow of knowledge). Such systems are called knowledge management systems (KMS) as distinct from transaction processing systems (TPS), management information systems (MIS), decision support systems DSS), and executive information systems (EIS) (Alavi & Leidner, 1999). For example, Cap Gemini Ernst & Young, KPMG, and Acenture all have implemented KMS (Wickramsinghe, 2003). In fact, the large consulting companies were some of the first organizations to realize the benefits of knowledge management and plunge into the knowledge management abyss. These companies treat knowledge management with the same high priority as they do strategy formulation, an illustration of how important knowledge management is viewed in practice (Wickramasinghe, 2003). Essentially, these knowledge management systems use combinations of the following technologies: the Internet, intranets, extranets, browsers, data warehouses, data filters, data mining, client server, multimedia, groupware, and software agents to systematically facilitate and enable the capturing, storing, and dissemination ofknowledge across the organization (Alavi et al., 1999; Davenport & Prusak, 1998; Kanter, 1999). Unlike, other types of information systems, knowledge management systems can vary dramatically across organizations. This is appropriate if we consider that each organization's intellectual assets, intangibles, and knowledge should be to a large extent unique and thus systems enabling their management should in fact differ.
KNOWLEDGE MANAGEMENT TOOLS AND TECHNIQUES
KM tools and techniques are defined by their social and community role in the organization in (1) the facilitation of knowledge sharing and socialization of knowledge (production of organizational knowledge); (2) the conversion of information into knowledge through easy access, opportunities of internalization and learning (supported by the right work environment and culture); (3) the conversion of tacit knowledge into "explicit knowledge" or information, for purposes of efficient and systematic storage, retrieval, wider sharing, and application. The most useful KM tools and techniques can be grouped as those that capture and codify knowledge and those that share and distribute knowledge (Duffy, 2000, 2001; Maier, 2001).
capture and codify Knowledge
There are various tools that can be used for capture and codify knowledge. These include databases, various types of artificial intelligence systems including expert systems, neural networks, fuzzy logic, genetic algorithms, and intelligent or software agents.
Databases
Databases store structured information and assist in the storing and sharing of knowledge. Knowledge can be acquired from the relationships that exist among different tables in a database. For example, the relationship that might exist between a customer table and a product table could show those products that are producing adequate margins, providing decision-makers with strategic marketing knowledge. Many different relations can exist and are only limited by the human imagination. These relational databases help users to make knowledgeable decisions, which is a goal of knowledge management. Discrete, structured information still is managed best by a database management system. However, the quest for a universal user interface has led to the requirement for access to existing database information through a Web browser.
Case-Based Reasoning Applications
Case-based reasoning (CBR) applications combine narratives and knowledge codification to assist in problem solving. Descriptions and facts about processes and solutions to problems are recorded and categorized. When a problem is encountered, queries or searches point to the solution. CBR applications store limited knowledge from individuals who have encountered a problem and found the solution and are useful in transferring this knowledge to others.
Expert Systems
Expert systems represent the knowledge of experts and typically query and guide users during a decision making process. They focus on specific processes and typically lead the user, step by step, toward a solution. The level of knowledge required to operate these applications is usually not as high as for CBR applications. Expert systems have not been as successful as CBR in commercial applications but can still be used to teach knowledge management.
Using I-Net Agents: Creating Individual Views from Unstructured Content
The world of human communication and information has long been too voluminous and complex for any one individual to monitor and track. Agents and I-net standards are the building blocks that make individual customization of information possible in the unstructured environment of I-nets. Agents will begin to specialize and become much more than today's general purpose search engines and "push" technologies.
Two complimentary technologies have emerged that allow us to coordinate, communicate, and even organize information without rigid, one-size-fits-all structures. The first is the Internet/Web technologies that are referred as I-net technology and the second is the evolution of software agents. Together, these technologies are the new-age building blocks for robust information architectures, designed to help information consumers find what they are looking for in the way that they want to find it. The Web and software agents make it possible to build sophisticated, well performing information brokers designed to deliver content, from multiple sources, to each individual, in the individual's specific context and under the individual's own control. The software agents supported with I-net infrastructure can be highly effective tools for individualizing the organization and management of distributed information.
systems to share and Distribute Knowledge
Computer networks provide an effective medium for the communication and development of knowledge management. The Internet and organizational intranets are used as a basic infrastructure for knowledge management. Intranets are rapidly becoming the primary information infrastructure for enterprises. An intranet is basically a platform based on Internet principles accessible only to members of an organization/community. The intranet can provide the platform for a safe and secured information management system within the organization, help people to collaborate as of virtual teams, crossing boundaries of geography and time. While the Internet is an open-access platform, the intranet, however, is restricted to members of a community/organization through multi-layered security controls. The same platform, can be extended to an outer ring (e.g., dealer networks, registered customers, online members, etc.), with limited accessibility, as an extranet. The extranet can be a meaningful platform for knowledge generation and sharing, in building relationships, and in enhancing the quality and effectiveness of service/support. The systems that are used to share and distribute knowledge could include group collaboration systems, groupware, intranets, extranets, and Internet, office systems, word processing, desktop publishing, or Web publishing.
future trends
Just implementing a knowledge management system does not make an organization a knowledge based business. For an organization to become a knowledge-based business, several aspects must be considered. An organization that values knowledge must integrate knowledge into its business strategy and sell it as a key part of its products and services. To do this requires a strong commitment to knowledge management directed from the top of the organization. Furthermore, it is necessary for specific people, process, and technology issues to be considered. First, the knowledge architecture should be designed that is suitable given the context of a particular organization in its industry as well as the activities, products or services it may provide. From the knowledge architecture, it is important to focus on the organization's structure and culture and key people issues. Do the structure and culture support a knowledge-sharing environment or perhaps a more team focussed, sharing culture needs to be fostered. In addition, strategies need to be adopted for continuous training and fostering of knowledge workers. Then, it is necessary to consider the processes of generating, representing, accessing and transferring knowledge throughout the organization. This also requires and evaluation of the technologies required enabling this. Finally, a knowledge based business should also enable organizational learning to take place so that the knowledge that is captured is always updated and current, and the organization is continually improving and refining its product or service as well as enhancing its extant knowledge base.
conclusion
In today's hyper competitive business environment, organizations that can make superior and timely decisions have a greater chance of succeeding. This can only be done effectively and efficiently through the transformation of an organization's data and information assets into germane knowledge and relevant information. By utilizing the spectrum of intelligent technologies as well as embracing the tools and techniques of KM, organizations can successfully effect this required transformation and thereby create appropriate knowledge discovery solutions. Hence, the incorporation of KM and judicious adoption of intelligent technologies becomes a competitive necessity for all organizations.
KEY TERMS
KM Infrastructure: The tools and technologies (the specific tools required to capture and codify organizational knowledge, specific tools required to share and distribute organizational knowledge) that are required to support and facilitate KM in the organization. KM tools and technologies are the systems that integrate various legacy systems, databases, ERP systems, and data warehouse to help organizations to create, and use KM systems in the organization.
Knowledge: Knowledge is more comprehensive than data or information. It is a mix of experience, values, contextual information, expert insights, and grounded intuition that actively enables performance, problem solving decision-making, learning, and teaching.
Knowledge Architecture: The blue prints of subjective and objective knowledge, its flows and cartography of knowledge within the organization.
Knowledge as Object: This is when knowledge is conceptualized in the Lokean/Leibnitzian perspective and used to create efficient and effective solutions.
Knowledge as Subject: This is when knowledge is conceptualized in the Hegelian/Kantian perspective and used to create shared meanings and support sense making.
Knowledge Assets: The knowledge regarding markets, products, technologies, processes, and organizations, that a business owns or needs to own and which enable its business processes to generate profits, add value, etc.
Knowledge-Based Enterprises: Knowledge-based enterprises are those enterprises who derive the most value—from intellectual rather than physical assets. Knowledge-based enterprise is a firm that is fully embracing knowledge management and committed to fostering continuous learning.
Knowledge Management (KM): KM is the process through which organizations generate value from their intellectual and knowledge-based assets. Most often, generating value from such assets involves sharing them among employees, departments and even with other companies in an effort to devise best practices. KM is newly emerging, interdisciplinary business approach that involves utilizing people, processes, and technologies to create, store and transfer knowledge.