In parts 1 and 2 we described two levels of Decisions support systems (DSS) systems, which analyze the “what and the why” of business performance. In a third level of DSS functionality which is more advanced, analysis enables modeling of mechanisms, to predict the outcome of one action or alternative actions: ‘What shall be the outcome if a certain measure is taken’ (what-if analysis).

Predictions may relate to any of the following (indicatively):

· How much shall revenue be increased, due to a marketing campaign

· Which products must be promoted to which Customer groups – ‘propensity to buy’ scoring

* Which Customers are about to defect (churn or attrition modelling)
* Which tax payers are likely to evade tax (tax evasion risk scoring)

* How would a product price-change affect sales volume (price elasticity analysis)
* How would the application of a new tax policy affect state revenues

Certain risk management forms (e.g. credit risk modeling), belong to this DSS level. Statistical analysis, data mining and modeling tools are usually employed. The development of an accurate predictive system which is information driven, leads to a sustainable competitive advantage which differentiates leading Organizations. While the ability to capture detailed results (level 1) is the background, the abilities to analyze and predict results (levels 2 and 3) enable the full exploitation of business information and the high competitiveness of an Organization.

DSS infrastructures, if developed and used aligned to the Strategy, contribute to the formation of an Organization which fully exploits information and takes actionable decisions based on the latter. Copyright 2006 – Kostis Panayotakis