In both cases, the business processes and Applications make calls to the Decision Services that encapsulate the behavior of the decision being automated. In analytical decision management, the history of decisions is analyzed to build a model that can be used to predict the best decision response for the future. In operational decision management, policy, best practices, and business experience is used to write rules that describe how to make those decisions or identify situations that need to be reacted to. In many cases, the models that result from analytical decision management can also be used in the operational decision management automation.There are situations where all the decision automation needed for a solution can initially be provided by one of these forms of decision management. However, as decision making become more complex, the need for synergistic use of both forms of technology becomes greater. When starting from analytical decision management, the models that define best practices or probability can be encapsulated and applied in a broader systems context through operational decision management. When starting from operational decision management, the decision logic that defines best practices can benefit from the data mining, segmentation, and insight provided by analytical decision management.
In both cases, there needs to be some measure of how good the decisions are. These key performance indicators (KPIs) relate to the overall goals of the business and, when used with Scenario Analysis and Simulation, allow for a real-world method of assessing how decision changes affect the behavior of business systems.