Clearly, KDD is a process used to generate new knowledge. Through interacting with available datasets, the analyst becomes more knowledgeable and has the opportunity to validate new assumptions or perceptions of causal networks. Much of this knowledge will remain tacit (i.e., contained in the analyst’s mind) but may also become explicit (recorded or written down). The methods and procedures used in KDD approaches can be integrated with Churchman’s (1971) discussion of five theoretical views of knowledge generation. Recall that Churchman’s articulated models were based on the classic philosophies of Leibniz, Locke, Kant, Hegel, and Singer. We refer the reader to Churchman’s (1971) original work for a complete description of each view.
At first glance, KDD appears to bear the strongest resemblance to the Lockean and Kantian approaches, as articulated by Churchman (see also, Courtney, 2001). The Lockean approach is a relatively open style of generating new knowledge. Data in the form of new observations are collected, and then new knowledge is generated via inductive logic. Consensus from the Lockean community serves as a check that the new knowledge is both valid and accurate. As noted by Courtney (2001),
“The primary knowledge management tools in Lockean organizations are repositories, such as data warehouses, for storing observations, data mining for analyzing
observations, and groupware tools, such as electronic meeting software and e-mail, for facilitating the communication process and the development of shared meaning.”
(p. 27)
This observation highlights the significant overlap existing between KDD and Lockean inquiry.
The Kantian approach to inquiry relies upon the comparison of multiple models or perspectives that surround a particular problem or issue. These models arise from consideration of different types of observations or data—from both within and outside the system. Like KDD analysts, Kantian inquirers are capable of constructing multiple explanatory models of the decision situation. New knowledge results from what essen- tially reduces to a goodness-of-fit process—a highly analytical method of comparing each model or perspective with other perspectives to see which best explains the variance in problem-relevant data. The decision style exhibited in Kantian inquiry is both theoretical and empirical and is likely to be an approach used by many KDD analysts who serve as data archeologists.
With its strong association with the classic scientific method, KDD fits well with the Lockean approach. New observations (i.e., data from outside an organization’s knowl- edge base) are sought and related to other data in the search for meaningful causal connections that manifest in the form of reliable patterns and associations. The Kantian approach is also appropriate in that KDD procedures frequently rely on procedures that generate and compare multiple models in order to determine which one best characterizes the emergent associations. The observations on which these models are based can originate from both within the organization’s archived knowledge or be adopted from external sources. Sophisticated statistical procedures, like structural equation modeling, bear strong resemblance to the spirit of the Kantian approach.
Courtney (2001), however, alludes to potential limitations of applying the Kantian approach to KDD. As noted by Courtney (2001), because the Kantian approach “is based on the belief that problems can be modeled analytically. There is little or no emphasis on human interpretation of the problem, nor on human involvement” (p. 28). This view suggests that the Kantian approach only requires knowledge management tools whose functionality includes capabilities of maintaining problem-relevant data and the devel- opment of alternative explanatory models. Arguably, because fully automated data mining tools could fulfill this function, the Kantian approach may not be fully consistent with KDD processes in which human analysts play a pivotal role.
The open-endedness of the data discovery process inherent in KDD limits the applica- bility of the Leibnizian approach to the KDD process. Although KDD analysts, like Leibnizian inquirers, may use formal logic and mathematical analysis to make inferences about cause-and-effect relationships, they are not limited by access to internally generated data and knowledge. Taking this view, new knowledge results from the generation of logical conclusions from knowledge already inherent in the organization’s archives—that is, new observations are not brought into the database. All new knowl- edge must be consistent with a set of axioms that define the core structure of the organization. Another distinction between KDD and Leibnizian inquiry lies in the importance of tacit knowledge: knowledge within a person’s head that is difficult to
express or codify (Nonaka & Takeuchi, 1995). As noted by Courtney (2001), tacit knowledge is afforded little importance within Leibnizian organizations (p. 26). In contrast, the effectiveness of KDD is typically driven by tacit knowledge. Analyst- centered KDD is consistent with Churchman’s (1971) allusions to libraries and library users, noting that “the state of knowledge resides in the combined system consisting of the library and an astute and adept human user” (p. 9).
The Hegelian view is not directly applicable to the KDD process described in the previous section. Hegelian inquiry creates knowledge via a process of debate between antithetical ideas about a topic. After one thesis is advanced, compelling arguments for a diametri- cally opposite viewpoint are developed. An objective third-party analyzes the debate between the antithetical views and resolves them via a synthesis that reflects the most plausible aspects of both. This process bears little resemblance to the KDD process. As noted previously, many techniques applied within DM and KDD involve statistical exploration and operate on actual data that exists within and outside the organization’s database. Although their colleagues may question the inferences made by KDD analysts, the KDD process does not require the development and resolution of conflicting viewpoints of the data.
The Singerian view transcends the other four approaches by arguing that new knowledge arises out of a consideration of a holistic mixture of specific observations (individual datum), implicit knowledge held by organizational employees, and ethical and personal characteristics of the user/analyst. The Singerian view, therefore, also clearly supports the notion of placing the analyst at the center of research into knowledge production (e.g., KDD research). This approach has been recognized as being highly suited for wicked problems (Rittel & Webber, 1973; see also, Courtney, 2001) and it applies to those DM and KDD processes that must consider both human and environmental factors during data interpretation. The Singerian approach is a combination of functional, interpretive, and critical views. As noted by Courtney (2001), “knowledge of all types must be supported in this environment, both tacit and explicit, both deep and shallow, both declarative and procedural, both exoteric and esoteric…every genre of software is required in the Singerian organization” (p. 28). As Churchman (1971) and Mitroff and Linstone (1993) note, the Singerian approach sweeps in all other inquiry and knowledge creation approaches; it will employ any or all of them to a particular decision-making process as well as information from both internal and external sources.
The overlap between the Singerian approach and KDD is evident in Mitroff and Linstone’s (1993) discussion of the management of real-life problems. These researchers maintain that a combination of technical (T), organizational and social (O), and personal and individual (P) factors are crucial to managing and solving real-world problems. They also contend that successful implementation of problem solutions “depends first and foremost on the use of human resources” (p. 102). Courtney (2001) adds, “the personal perspective is based on individual experiences, intuition, personal factors, and attitudes about risk, among other things…in a complex scenario, given the same external informa- tion, no two people might reach the same conclusion, as their background, training, experience, values, ethics, and mores, may differ” (p. 30). Singerian inquiry’s emphasis on organizational and social (O) factors, as well as personal and individual (P) perspec- tives in problem-solving situations is consistent with our contention that research
focused on the analyst’s interaction with data within the KDD process has considerable potential to help us better understand knowledge creation processes in inquiring organizations.