There are several kinds of such systems, however, in this subsection I will look at only at data-driven decision support systems (from now on referred to solely as decision support systems). The role of these systems is to access and manipulate data. They usually work with a data warehouse, use an online analytical processing system (OLAP), and employ data mining techniques. The goal is to enhance decision-making and solve problems by working with the manager rather than replacing him.
A decision support system can be a valuable tool. However, in order to be able to provide the information that each expert would find relevant, the user must be involved in the development and the post audit evaluation of the decision support system (Liebowitz 1999). This involvement must span not just the content issues, but also the presentation and the organization of the information. This is necessary to ensure that the system fulfills the three criteria that determine its success, namely compatibility, understandability, and effectiveness (Rouse in Liebowitz 1999).
If these three criteria are met, decision support systems can be invaluable in expanding the scope of information that each expert can handle. As a result, cognitive limitations become less important in determining the amount of source material that the expert can use.
One advantage and limitation of the decision support system is that it is user driven. This implies that the system answers queries what the expert inputs, but does not carry out further analysis on its own. It is therefore not a form of artificial intelligence like other decision-making tools.
Knowledge management (KM) is involved in two ways here. Normally the area that is emphasized is that decision support systems can enhance the manager's knowledge through knowledge discovery and supply of relevant information. However, knowledge and KM activities are key components in how the manager uses the system, i.e. the direction of the analysis that he carries out, and the knowledge that he is looking for. Kiku (2006) emphasizes that a decision support system must be designed in light of KM. An effective decision support system thus requires that the organization:
- Investigates the decisions made within their firm
- Compares these decisions with KM activities
- Evaluates any current decision support system in light of this
- Modifies said system if necessary