Conclusion
IN THESE SIX CHAPTERS we have traveled down through the higher levels of a knowledge automation project, from the very top—the motivating business strategy—down to the techniques used to implement business knowledge in decision services. This was our journey:
- Chapter 1 began with the fundamental truth that business knowledge has a measurable value and showed that this value can be realized as decision yield by using the techniques of decision management to automate operational business decision-making.
- Chapter 2 explained that such decision-making can be modeled as a set of decision points in the business process, and automated by decision services that encapsulate the business knowledge required to make those decisions. These services are exposed by a business rules management system (BRMS) and called by the business process management system (BPMS). Using decision services efficiently may involve some process redesign to rationalize the decision points; a template automated originations business process was described as an example.
- Chapter 3 presented the most important technologies used to encapsulate knowledge in decision services—business rules (including decision tables and decision trees), algorithms, and predictive analytics (including induction, scorecards, and neural nets)—and discussed their relative strengths and weaknesses. It presented the overall architecture used for automating decisions and showed that the four main components (BPMS, BRMS, predictive ...
