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 roles 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 the creation of 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). The thesis of this article is that taking a broader perspective of the resultant knowledge product from the KDD process, namely by incorporating a people-based perspective into the traditional KDD process, not only provides a more complete and macro perspective on knowledge creation but also a more balanced approach, which in turn serves to enhance the knowledge base of an organization and facilitates the realization of effective knowledge. 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/reusing the created knowledge, and thereby enables them to be well positioned in today’s knowledgeable economy.