Abstract
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 (Wickramasinghe & 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.
TopKnowledge 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).
Key Terms in this Chapter
Knowledge Architecture: The blue prints of subjective and objective knowledge, its flows and cartography of knowledge within the organization.
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.
Knowledge as Object: This is when knowledge is conceptualized in the Lokean/Leibnitzian perspective and used to create efficient and effective solutions.
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 as Subject: This is when knowledge is conceptualized in the Hegelian/Kantian perspective and used to create shared meanings and support sense making.