A Philosophical Perspective on Knowledge Creation

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Author(s): Nilmini Wickramasinghe (Stuart School of Business, Illinois Institute of Technology, USA)and Rajeev K. Bali (Coventry University, UK)
Copyright: 2009
Pages: 8
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch236

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Abstract

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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