from DSSResources.com

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                          DSS News
                       by D. J. Power
              December 2, 2001 -- Vol. 2, No. 25
         A Bi-Weekly Publication of DSSResources.COM

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         Check Southern California Edison (SCE) Case
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Featured:

* DSS Wisdom
* Ask Dan! - What is data mining and how is it related to DSS?
* DSS News Stories

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DSS Wisdom

Charles Stabell noted in a 1983 paper titled "A Decision-Oriented 
Approach to Building DSS" that "... the diagnosis of current decision 
making and the specification of changes in decision processes are the 
activities that provide the key inputs to the design of the DSS.  
Diagnosis is the identification of problems (or opportunities for 
improvement) in current decision behavior; it involves determining how 
decisions are currently made, specifying how decisions should be made, 
and understanding why decisions are not made as they should be. 
Specification of changes in decision processes involves choosing what 
specific improvements in decision behavior are to be achieved and thus 
defining the objectives for the development of a computer-based DSS."

Stabell, C. B., "A Decision-Oriented Approach to Building DSS", Chapter 
10 in Bennett, J. L., Building Decision Support Systems, Reading, MA: 
Addison-Wesley, 1983.

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Ask Dan!

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/).

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

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DSS News Stories: Nov. 18 to Dec. 2, 2001

11/28/2001 HealthSouth breaks ground for new medical center; first 
digital hospital focuses on physician and patient needs.

11/27/2001 Decision Support and Transoft Alliance offer cross-platform 
data access for legacy applications.

11/27/2001 Oracle announces that a growing number of Informix users are 
migrating to Oracle DB.

11/26/2001 Fujitsu Siemens computers selects Business Objects Analytic 
Applications.

11/26/2001 Thoma Cressey Equity Partners selects Interface Software’s 
InterAction for CRM.

11/21/2001 Spectra Logic implemented Crystal Decisions to improve user 
access to ERP.

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