Knowledge Patterns in Databases

Abstract

Knowledge management (KM) transforms a firm’s knowledge-based resources into a source of competitive advantage. Knowledge creation, a KM process, deals with the conversion of tacit knowledge to explicit knowledge and moving knowledge from the individual level to the group, organizational, and interorganizational levels (Alavi & Leidner, 2001). Four modes?namely, socialization, externalization, combination, and internalization?create knowledge through the interaction and interplay between tacit and explicit knowledge. The “combination” mode consists of combining or reconfiguring disparate bodies of existing explicit knowledge (like documents) that lead to the production of new explicit knowledge (Choo, 1998). Transactional databases are a source of rich information about a firm’s processes and its business environment. Knowledge Discovery in Databases (KDD), or data mining, aims at uncovering trends and patterns that would otherwise remain buried in a firm’s operational databases. KDD is “the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). KDD is a typical example of IT-enabled combination mode of knowledge creation (Alavi & Leidner, 2001).