Decision Support Systems or DSS, (sometimes called Business Intelligence or BI) is about synthesizing useful knowledge from large data sets. It's about integration, summarization and abstraction as well as ratios, trends and allocations. It's about comparing data-based generalizations with model-based assumptions and reconciling them when they're different. It's about good, data-facilitated creative thinking and the monitoring of those creative ideas that were implemented. It's about using all types of data wisely and understanding how derived data was calculated. It's about continuously learning, and modifying goals and working assumptions based on data-driven models and experience. In short, business intelligence should function like a virtuous cycle of decision making improvement.

Let's laundry-list these concepts to identify the minimum set of basic functions that comprise any DSS or BI framework:

• Data collection
• Data storage and access organization
• Dimensional structuring
• Data synthesis
• Intuitive representations and access models
• Predictive models
• Model verification
• Knowledge sharing
• Resource allocation strategies
• Scenario analysis
• Belief conflict resolution
• Prescriptions and decision implementation capabilities

A Cognitive Metaphor for DSS: Beyond Closed Loop Systems

The best metaphor that we can think of for understanding how all these business intelligence functions fit together is a cognitive one. In contrast, earlier metaphors focused on the uni-directional flow of information from 'raw' data to synthesized knowledge. Second generation metaphors, currently in vogue, focus on bi-directional, closed loop systems wherein the results of DSS analysis are fed back into production systems. The hallmark of a third generation cognitive metaphor is the interplay of two separate information loops. The first is akin to the closed loop system and we would characterize it as a data-driven loop. But in addition to that loop there exists an inner loop where data driven information meets model-driven goals and beliefs at the moment of decision. Although that inner loop is frequently provided by a living, breathing person, it is a function that needs to take place and in automated systems needs to take place in the form of software within the overall decision support system. AIl workers have known for a long time that it takes a combination of data-driven and model-driven information to produce high quality decisions. Using a cognitive metaphor, the universe of DSS functions is composed of five distinct functional layers: a sensory/motor layer, a primary memory layer, a data-based interpretive layer, a decision layer and a model-driven layer of goals and beliefs. This is illustrated in Figure 1 below. The BI process, shown in Figure 1, is the cyclical movement of information between layers. Note there are two separate information flows labeled 'I' and 'O'. The 'O' flow moves between the sensory/motor layer, the primary storage layer, the data based interpretive layer and the decision layer. The 'I' flow moves between the model-driven layer and the decision layer. Decisions are the result of interaction between the two flows.

Figure 1. A functional View of DSS

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