In recent years, most large companies and many other large organizations have implemented database systems called data warehouses and some have also implemented OLAP systems. OLAP is an abbreviation for Online Analytical Processing. Some organizations have implemented Business Intelligence Systems and some have created Executive Information Systems. Many managers and information systems specialists are interested in learning more about these relatively new types of Data-Driven Decision Support Systems. For many years, the prospects and problems of providing managers with real-time management information have been discussed and debated (cf., Dearden, 1966). The debate about costs, advantages, problems and possibilities must continue. This chapter explains Data-Driven Decision Support Systems from both a managerial and a technical point of view.
The DSS framework discussed in Chapter 1categorized data warehouses, Executive Information Systems, Spatial DSS, and OLAP systems as Data-Driven Decision Support Systems. Some authors include data mining as a Data-Driven DSS, but that software is discussed in Chapter 10 on "Building Knowledge-Driven DSS and Mining Data".
Data-Driven DSS are often very expensive to develop and implement in organizations. Despite the large resource commitments that are required, many companies have implemented Data-Driven Decision Support Systems. Technologies are changing and managers and MIS staff will need to make continuing investments in this category of DSS software. So it is important that managers understand the various terms and systems that use large databases to support management decision-making. This chapter emphasizes: identifying sub-categories of Data-Driven DSS, comparing DSS data and operating data, understanding an inter-connected Data-Driven DSS architecture, implementing a Data-Driven DSS, and finding success in building Data-Driven DSS. Now letís begin our exploration of this category of DSS by defining and explaining the key term Ė Data-Driven DSS.