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Decision Support Systems DSS

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High Performance Data Warehousing

Decision Support Systems And Expert Systems 

Decision Support Systems And Data Warehouses

Decision support systems also allow the user to create what-if scenarios. These are essentially modeling tools that allow the user to define an environment and simulate the behavior of that environment under changing conditions. For example, the user of a DSS for finance could create a hypothetical stock portfolio and then direct the DSS to model the behavior of that stock portfolio under different market conditions. Once these behaviors are specified, the user may vary the contents of the portfolio and view the results.

The types of output from decision support systems include:

* Management information systems (MIS)--Standard reports and forecasts of sales.

* Hypothesis testing--Did sales decrease in the Eastern region last month because of changes in buying habits? This involves iterative questioning, with one answer leading to another question.

* Model building--Creating a sales model, and validating its behavior against the historical data in the warehouse. Predictive modeling is often used to forecast behaviors based on historical factors.

* Discovery of unknown trends--For example, why are sales up in the Eastern region? Data mining tools answer questions in those instances where you may not even know what specific questions to ask.

The role of human intuition in this type of problem solving has stirred great debate. Decision support systems allow the user to control the decision-making process, applying his or her own decision-making rules and intuition to the process. However, the arguments for and against using artificial intelligence to manage the intuitive component of these systems has strong proponents on both sides.

Now that expert systems and decision support systems have been described, letís take a look at how databases are used to develop these systems.

This is an excerpt from "High Performance Data Warehousing", copyright 1997.

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