Conclusions and Commentary
Knowledge-Driven DSS and mining data are at the decision support frontier in organizations. During the 1980ís, unrealistic expectations were created for expert systems and the recent hyperbole about data mining has also created some skepticism. Managers and IS staff need to investigate how these technologies might solve real business problems, but caution should be used in selling Knowledge-Driven DSS and data mining projects.
Data mining techniques and tools are NOT fundamentally different from the older quantitative model-building techniques. The methods used in data mining are extensions and generalizations of analytical methods known for decades. Neural networks are a special case of what is called projection pursuit regression, a method developed in the 1940s. Classification and regression tree (CART) methods were used by social scientists in the 1960s (cf.,http://www.twocrows.com/iwk9701.htm). The computing technology used to implement these underlying methods has however greatly improved.
For the foreseeable future, modest Knowledge-Driven DSS projects can provide some benefits and can help companies develop experience using expert system and data mining tools. It is important for large companies to have projects in this category of Decision Support Systems, but only modest resources should be committed in many companies. The list of possible applications in this chapter should guide the selection of new projects.
|DSSResources.COMsm is maintained and all its pages are copyrighted (c) 1995-2002 by D. J. Power (see home page). Please contact email@example.com. This page was last modified Wednesday, May 30, 2007. See disclaimer and privacy statement.|