What is data mining and how is it related to DSS?

Data mining is one of the IS/DSS buzzwords of the 1990s. Academics tend to use the related terms Knowledge Discovery and Intelligent Decision Support Methods (Dhar and Stein, 1997) or more derogatory terms like data surfing or data dredging. In general, data mining is "a class of analytical applications that help analysts and managers search for patterns in a data base". Both data mining and knowledge discovery can be considered as both a process and as a set of tools.

The data mining process involves identifying an appropriate data set to "mine" or sift through to discover data content relationships. Data mining tools include techniques like case-based reasoning, cluster analysis, data visualization, fuzzy query and analysis, and neural networks. Data mining sometimes resembles the traditional scientific method of identifying a hypothesis and then testing it using an appropriate data set. Sometimes however data mining is reminiscent of what happens when data has been collected and no significant results were found and hence an ad hoc, exploratory analysis is conducted to find a significant relationship.

Data mining has helped identify meaningful relationships and when it is done well the results should be useful in business decision making. In particular, data mining can conceivably be a major part of a special decision study. Data can be mined with a specific purpose in mind and statistically significant results can be reported to managers. What is not always clear is how data mining is related to building Decision Support Systems. Some commentators imagine we should provide managers with a data mining tool and let them mine data until they have thoroughly understood the relationships that are "hidden" in the data set. This vision doesn't seem too fruitful or too desirable. It is appropriate for a trained decision support analyst to work with data mining tools to prepare special decision studies, but most managers won't have the interest or skills to participate in such an activity.

So is data mining relevant to building DSS? Yes, I think it is if we are realistic about what is possible. First, data mining can help identify relations and rules that can be incorporated in Knowledge-driven DSS. Second, case-based reasoning can be used to create a specific Knowledge-driven DSS that can be used by a manager or a knowledge worker who is trying to diagnosis problems in that "case" environment. Third, data visualization tools can be incorporated with a structured data set to assist managers in making a recurring decision where the data set is routinely updated. For example, a stock portfolio manager may find that a Data-driven DSS with visualization tools may help understand the composition of the portfolio and help identify what changes need to be made in its component stocks. Fourth, other tools like neural networks may also have a place in creating capabilities in specific DSS. For example, rather than using only a heuristic scoring model and possibly a risk analysis model for supporting commercial loan decision making there may be some situations where a neural network model from a database of prior loans could also inform and support the decision maker. One can also identify DSS applications that use data mining tools in fraud detection, category management and direct marketing.

Well I think the above comments provide enough examples of using data mining tools to build DSS. My conclusion is that data mining tools are relevant to building DSS when the decision situation warrants the use of such tools and when the DSS Builder understands there uses and limitations.

For more information on data mining visit Gregory Piatetsky-Shapiro's KDnuggets website (http://kdnuggets.com/) or the ACM Special Interest Group on Knowledge Discovery in Data and Data Mining web site (http://www.acm.org/sigkdd/).

References

Dhar, V. and R. Stein, Intelligent Decision Support Methods: The Science of Knowledge, Upper Saddle River, NJ: Prentice-Hall, 1997.