Paul Armborst of the Westfield Group presented on his experiences in implementing business rules for their various insurance applications over the past 6 years. He sees one of the key problems is in documenting business rules, with two main choices for how to do it:
- Define the rules in a repository first (a rules management/modelling tool). Although this is the most likely approach for the first definition of rules within an organization, they’ll become out of sync as soon as they are implemented since the rules will be modified in the executing code or BRE rather than in the repository.
- Define the rules directly in a business rules engine, then generate the documentation in some automated fashion (likely provided by the BRE vendor)
He sees that the best way would be a combination of both approaches: a throw-away repository to document rules as they are discovered, and an extract from the BRE to provide ongoing documentation.
He pointed out one of the problems with introducing business rules: “real” developers want to write Java code, not this airy-fairy business rules stuff, which is an argument that I see against BPM as well. As IT budgets get squeezed, however, development teams will start to look for ways to reduce the amount of code that they have to write and pass some of the tuning capabilities directly over to the business; both BRE and BPM provide these types of capabilities.
He discussed various methods of implementing business rules:
- Decision tables, noting that the BRE vendor needs to provide some sort of analysis tool to detect gaps and overlaps in the rules, with the caveat that it’s possible to construct a decision table that’s too big to reasonably maintain.
- Decision trees, which can provide the same functionality in a decision table, but in a graphical form; if the decision points are not in the right order, the number of nodes can multiply unnecessarily, and overly-large trees can be difficult to follow.
He also discussed stateful and stateless implementations of business rules: although stateless is simpler to implement, stateful allows for running only the rules that use data that has changed since the last evaluation of each rule.
There were some last comments on end user rule maintenance: all of their rules are written by developers, but they’re thinking about how to offer some rule creation and modification capabilities to end users. It’s important to have a BRE that allows some sort of restricted view for less technical users, but it’s also necessary for the techies to do some helpful things like naming objects in a manner that users can understand rather than, say, reverse Hungarian notation. Users who have access to create rules need to have some notion of the logic required to do so, and there needs to be some provision for testing.
Decision tables is only part of the solution that discovers ‘gaps and overlaps’ in rules. To take this to a higer level, we would talk about rule verification that can detect any contradiction or redundancy in a collection of rules.
Trying to shoe-horn all logic into a single decision table is not desirable.
I’ll be giving a presentation how the Rule Manager is providing rule verification over a rule policy. This presentation is on Thursday in the co-hosted RuleML symposium.
Regards,
Marco
Marco, thanks for the clarification; I’m still getting up to speed on a lot of the details about rules, although this week at BRF has been a huge education for me.