David Straus of Corticon gave an engaging presentation about BR and BI, starting with the Wikipedia definitions about each, then characterizing BI as “understanding” and BR as “action” (not unlike my statement that BI in BPM is about visibility and BR in BPM is about agility). He started with the basic drivers for a business rules management system — agility (speed and cost), business control while maintaining IT compliance, transparency, and business improvement (reduce costs, reduce risk, increase revenue) — and went on to some generalized use cases for rules-driven analysis:
- Analyze transaction compliance, i.e., are the human decisions in a business process compliant with the policies and regulations?
- Analyze the effect of automation with business rules, i.e., when a previously manual step is automated through the application of rules
- Analyze business policy rules change (automated or non-automated)
He walked through a simplified claims scenario, where the claims agent is not replaced with rules but still makes a decision in the process, but their decision is compared against a decision made by a rules system and any discrepancies are investigated. In other words, although there’s still a person making the decision in the process, the rules system is acting as a watchdog to ensure that their decisions are compliant with the corporate policy. After some time, there can be some analysis of the results to detect pattens in non-compliance: is it an individual agent that’s causing non-compliance, or a particular product, or are the rules not aligned with the requirements? In some cases, the policies given to the agents are actually in conflict, so that they have two different “right” answers in some cases; in other cases, agents may have information that’s just not represented in the rules. By modeling the policies in a business rules system, these conflicts can be driven out to establish integrity across the entire set of rules. This can also be used in cases where an organization just isn’t ready to replace a human decision with a business rules system, in order to validate the rules and compare them to the human decisions; this can establish some trust of the decisioning system that may eventually lead them to replace some of the human decisions with automated ones to create more consistent and compliant decisions.
David had a number of case studies for this combination of rules and analytics, such as investment portfolio risk management, where mergers and acquisitions in the portfolio holdings may drive the portfolio out of compliance with the underlying risk profile: information about the holdings is fed back through the rules on a daily basis to establish if the portfolio is still in compliance, and trigger a (manual) rebalancing if it is out of compliance.
By combining business intelligence (and the data that it’s based on) and business rules, it’s also possible to analyze what-if scenarios for changes to rules, since the historical data can be fed through the new version of the rules to see what would have changed.
He’s challenged the BI vendors to do this sort of rules-based analysis; none of them do it now, but it would provide a hugely powerful tool for providing greater insight into businesses.
There was a question from the audience that led to a discussion about the iterative process of discovering rules in a business, particularly the ones that are just in people’s heads rather than encoded in existing systems; David did take this opportunity to make a plug for the modeling environment in their product and how it facilitates rules discovery. I’m seeing some definite opportunities for rules modeling tools when working with my customers on policies and procedures.