Some people claim "knowledge leads to power". Even if that claim is true companies only win when knowledge is shared among employees and other stakeholders. Today sharing knowledge when making decisions is more important than most people recognize. One way to share knowledge is to build computerized systems that can store and retrieve knowledge codified as probabilities, rules and relationships. Specialized software can process this knowledge and assist managers in making decisions. Specialized decision support and artificial intelligence (AI) tools can also help create knowledge. An umbrella term that describes these systems is Knowledge-Driven Decision Support Systems. These DSS provide suggestions to managers and the dominant component is a "knowledge" capture and storage mechanism. Knowledge and suggestions are the two major themes that link these different knowledge tasks.
Knowledge-Driven DSS, Suggestion DSS, Rule-Based DSS and Intelligent DSS are overlapping terms for management support systems built using artificial intelligence technologies. We usually use expert systems development shells and data mining tools to create these systems. Business analysts identify relationships in very large databases using data mining or knowledge discovery tools. When a manager or knowledge worker uses a DSS with a data mining tool the results from an analysis may suggest relationships and new knowledge.
This chapter is an introduction and overview of Knowledge-Driven DSS technologies and applications. The first part of the chapter emphasizes expert system technologies and the second part emphasizes data mining techniques and tools. The overall thrust is to provide a foundation for building Knowledge-Driven DSS with specialized artificial intelligence tools. These technologies have been "hyped" by some vendors as solutions to a wide variety of problems, but artificial intelligence technologies are still "leading edge" capabilities for most businesses. At some point in the future all managers and knowledge workers may be using Knowledge-Driven DSS and mining data, but that future is over the horizon waiting to be implemented.
So the focus is on examining how we can use software to store and process knowledge for business decision-making and to find and derive knowledge for business decision-making. The following sections emphasize: Defining Key Terms and Concepts; Identifying Characteristics of Knowledge-Driven DSS; Managing Knowledge-Driven DSS Projects; Knowledge-Driven DSS Examples; Understanding Data Mining; Examples of Data Mining; and Evaluating Development Packages.
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