A Philosophical Perspective on Knowledge Creation

A Philosophical Perspective on Knowledge Creation

Nilmini Wickramasinghe (Stuart School of Business, Illinois Institute of Technology, USA) and Rajeev K. Bali (Coventry University, UK)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-60566-010-3.ch236
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Today’s economy is increasingly based on knowledge and information (Davenport, & Grover 2001). Knowledge is now recognized as the driver of productivity and economic growth, leading to a new focus on the role of information, technology and learning in economic performance. Organizations trying to survive and prosper in such an economy are turning their focus to strategies, processes tools and technologies that can facilitate them to create knowledge. A vital and well respected technique in knowledge creation is data mining which enables critical knowledge to be gained from the analysis of large amounts of data and information. Traditional data mining and the KDD process (knowledge discovery in data bases) tends to view the knowledge product as a homogeneous product. Knowledge however, is a multifaceted construct, drawing upon various philosophical perspectives including Lockean/Leibnitzian and Hegelian/Kantian, exhibiting subjective and objective aspects as well as having tacit and explicit forms (Nonaka, 1994; Alavi & Leidner, 2001; Schultze & Leidner, 2002; Wickramasinghe et al, 2003). It is the thesis of this discussion that by taking a broader perspective of the resultant knowledge product from the KDD process; namely by incorporating both a people-based perspective and process-based perspective into the traditional KDD process, it will not only provide a more complete and macro perspective on knowledge creation but also a more balanced approach to knowledge creation which will in turn serve to enhance the extant knowledge base of an organization and facilitate the realization of superior decision making. The implications for data mining are clearly far reaching and are certain to help organizations more effectively realize the full potential of their knowledge assets, improve the likelihood of using/re-using the created knowledge and thereby enable them to be well positioned in today’s knowledge economy.
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Background: Knowledge Creation

Knowledge Creation through Data Mining and the KDD Process

Knowledge discovery in databases (KDD), (and more specifically data mining) approaches knowledge creation from a primarily technology driven perspective. In particular, the KDD process focuses on how data is transformed into knowledge by identifying valid, novel, potentially useful, and ultimately understandable patterns in data (Spiegler, 2003; Fayyad et al., 1996). KDD is primarily used on data sets for creating knowledge through model building, or by finding patterns and relationships in data.

From an application perspective, data mining and KDD are often used interchangeably. Figure 1 presents a generic representation of a typical knowledge discovery process. This figure not only depicts each stage within the KDD process but also highlights the evolution of knowledge from data through information in this process as well as the two major types of data mining; namely, exploratory and predictive. whereas the last two steps (i.e., data mining and interpretation/evaluation) in the KDD process are considered predictive data mining. It is important to note in figure 1 that typically in the KDD process the knowledge component itself is treated as a homogeneous block. Given the well established multifaceted nature of the knowledge construct (Boland & Tenkasi, 1995; Malhotra, 2000; Alavi & Leidner, 2001; Schultze & Leidner, 2002; Wickramasinghe et al., 2003) this would appear to be a significant limitation or over simplification of knowledge creation through data mining as a technique and the KDD process in general.

Figure 1.

Integrated view of the Knowledge Discovery Process (Adapted from Wickramasinghe et al, 2003)


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