Many organizations use information technology to improve the effectiveness of their decision making. Known as decision support systems (DSS), these software products transform data into useful information such as statistical tables and comparative results reports (e.g., year-over-year performance). They allow for more objective decision making, improve management control, decrease the need for training through automation, and facilitate communication.
A DSS is made up of a database, a model, and a user-interface provides analysis and answers that help managers make choices. The database consists of structured records such as sales and cost figures. The model links variables that represents an understanding of cause and effect relationships; for example, if wages are increased, the labor cost per unit goes up. The user interface is how decision makers interact with the system; for example, the fields where they enter queries or the series of steps they take to produce graphs, charts, and reports.
Decision support systems are used in a wide variety of business functions and industries. For example, to make marketing and pricing decisions at the local level, the rental car company Hertz uses a DSS to bring together data about cities, holidays, business cycles, tourist activities, competitors’ actions, and customer behavior. Medical personnel use DSS for diagnosis and treatment; for example, a doctor might input test results and symptoms to identify the alternative diagnoses and recommended treatment. Farmers use such systems to determine crop rotation and other ways of optimizing their use of land. Loan officers and credit card companies use them to determine applicant risk and the appropriate interest rate to charge.
An enterprise level DSS is used for certain types of decisions. For example, Executive Information Support Systems serve the decision making needs of top executives by providing access to information about critical success factors and key performance indicators. With an ESS, executives can manipulate and refine data to be more meaningful and strategically significant to them. One use is for forecasting to project trends; another is getting real time data on factors relevant to a decision.
References
Turban, E., E. McLean, and J. Weatherbe. Information Technology for Management (4th Edition) (2004). New York: John Wiley & Sons.
