David M. Steiger

Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing

Decision support systems (DSS) are becoming increasingly more critical to the daily operation of organizations. Data warehousing, an integral part of this, provides an infrastructure that enables businesses to extract, cleanse, and store vast amounts of data. The basic purpose of a data warehouse is to empower the knowledge workers with information that allows them to make decisions based on a solid foundation of fact. However, only a fraction of the needed information exists on computers; the vast majority of a firms intellectual assets exist as knowledge in the minds of its employees. What is needed is a new generation of knowledge-enabled systems that provides the infrastructure needed to capture, cleanse, store, organize, leverage, and disseminate not only data and information but also the knowledge of the firm. The purpose of this paper is to propose, as an extension to the data warehouse model, a knowledge warehouse (KW) architecture that will not only facilitate the capturing and coding of knowledge but also enhance the retrieval and sharing of knowledge across the organization. The knowledge warehouse proposed here suggests a different direction for DSS in the next decade. This new direction is based on an expanded purpose of DSS. That is, the purpose of DSS in knowledge improvement. This expanded purpose of DSS also suggests that the effectiveness of a DSS will, in the future, be measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s) and thereby how well it improves his/her decision making.

Tacit to Explicit Knowledge Conversion: Knowledge Exchange Protocols

In the knowledge management domain, the conversion of tacit knowledge to explicit knowledge is critical because it is a prerequisite to the knowledge amplification process wherein knowledge becomes part of an organization’s knowledge network. In this article, knowledge exchange protocols are examined as a vehicle for improving the tacit to explicit knowledge conversion process. In an experiment testing the use of knowledge exchange protocols, it is learned that while structure may significantly improve the tacit to explicit knowledge conversion process, it also may matter how the structure is employed in this process.

Decision Support as Knowledge Creation: A Business Intelligence Design Theory

The primary purpose of decision support systems (DSS) is to improve the quality of decisions. Since decisions are based on an individual’s mental model, improving decision quality is a function of discovering the decision maker’s mental model, and updating and/or enhancing it with new knowledge; that is, the purpose of decision support is knowledge creation. This article suggests that BI techniques can be applied to knowledge creation as an enabling technology. Specifically, the authors propose a business intelligence design theory for DSS as knowledge creation, a prescriptive theory based on Nonaka’s knowledge spiral that indicates how BI can be focused internally on the decision maker to discover and enhance his/her mental model and improve the quality of decisions.

Instance-based cognitive mapping : a process for discovering a knowledge worker's tacit mental model

This paper addresses tacit-to-explicit knowledge externalization, arguably the most critical, and yet problematic, phase of Nonakas knowledge creation theory. Specifically, we propose and describe instance-based cognitive mapping (ICM), a unique externalization process that analyzes multiple decision instances using the inductive learning algorithms of artificial intelligence to generate a polynomial representation of the knowledge workers mental model, explicitly relating how the knowledge worker implicitly selects and weighs key factors in making decisions within a specific problem domain. After reviewing current externalization techniques, we describe the characteristics, and evaluate the advantages, of the ICM process. An exploratory test of the process suggests that inductive learning algorithms, such as the group method of data handling, can be used to discover a reasonable polynomial estimate of a knowledge workers tacit mental model. This estimate can then be compared with other explicit models and standards, updated with new information and knowledge, and internalized by all interested knowledge workers.

Decision Support as Knowledge Creation: An Information System Design Theory

The primary purpose of decision support systems (DSS) is to improve the quality of decisions. This paper suggests that improvement in decision quality is a function of the creation of new data and information applicable to the decision domain and the understanding and learning of that new knowledge by the decision maker; i.e., the purpose of decision support is knowledge creation. We propose an information system design theory for DSS as knowledge creation based on Nonakas knowledge spiral, including multiple kernel theories. These kernel theories and the associated process design methods are drawn from five distinct literatures: cognitive science, the theory of learning, artificial intelligence, knowledge management, and decision theory

Knowledge Management in Decision Making: Instance-Based Cognitive Mapping

Knowledge management deals with explicit knowledge and tacit (or implicit) knowledge. One form of tacit knowledge is an individuals mental model - a hypothetical knowledge structure that integrates the ideas, assumptions, relationships, insights, facts, and misconceptions that together shape the way an individual views and interacts with reality. The purpose of this paper is to explore these mental models, why they need to be made explicit, and how such externalization can be accomplished. Specifically, after a review of the mental model theory, we propose a new technique for determining an individuals mental model based on his/her decisions in several selected situations within a specific decision domain

Knowledge Exchange Protocols: A Second Study

In the knowledge management domain, the conversion of tacit knowledge to explicit knowledge is critical because it is a prerequisite to the knowledge amplification process wherein knowledge becomes part of an organizations knowledge network. Moreover, this process is strategically important because it enhances an organizations ability to create new knowledge that is inevitably expressed through the organizations capabilities, products, and services. The conversion of tacit to explicit knowledge is particularly relevant to information technology (IT), because IT can only partially facilitate tacit knowledge management, while it offers a substantial number of techniques to support the management and sharing of explicit knowledge. In this paper, knowledge exchange protocols are examined as a vehicle for improving the tacit-to-explicit knowledge conversion process. In a second experiment testing the use of knowledge exchange protocols, initial findings are confirmed: while structure may significantly improve the tacit-to-explicit knowledge conversion process, it also matters how the structure is employed in this process.