James Taylor of Fair Isaac is something of a business rules extremist (in the inference engine sense of "business rules"), but his views are interesting even so. Here are some of his thoughts about building analytic business processes around business rules and predictive analytics:
You could use business rule logging to record the critical decisions within a process instance, allowing you to "investigate process execution realities."
You could use rules-based and decision-model based simulation to see how changes in the rules or models used would have changed the outcome of processes (assuming your key decisions were automated they would determine the outcome of your process, at least the outcome that impacts the bottom line like the price offered or claim amount paid. This would let you "evaluate the business impact".
By making the rules easy to change when business users see value in so doing and by combining them with regularly updated, easy to deploy predictive models you could "improve" the business process.
By building a decision model, showing how the rules and models interact, you could go to the last step and run simulations that would let you "optimize" the process given the constraints under which you operate.
