Informed and Timely Business Decisions - A Data-driven Approach.

One of the main characteristics of business rules is their propensity for frequent change, due to internal or external factors to an enterprise. As these rules change, their immediate dissemination across people and systems in an enterprise becomes vital. The delay in dissemination can adversely impact the reputation of the enterprise, and cause significant loss of revenue. The current BRMS are often maintained by the IT group within a company, therefore the modifications of the BRs intended by executive management would not be instantaneous, since they have to be coded, and tested before being deployed. Moreover, the executives might not have the possibility to take the best decisions, without having the benefit of analyzing historical data, and quickly simulating what-if scenarios to visualize the effects of a set of rules on the business. Some of the systems that provide this functionality are prohibitively expensive. This paper addresses these challenges by using the power of Big Data analysis to source, clean and analyze historical data that is used for mining business rules, which can be visualized, tested on what-if scenarios, and immediately deployed without the intervention of the IT group. The proposed approach is instantiated in this paper by using open source components to mine stop loss rules for financial systems.