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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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