Business rule engines (BRE) have been around for a long time. Introduced in the early 1990s, BREs have found application in many industries, particularly those that are heavily regulated where compliance and auditability are key concerns. A BRE enables complex rules and regulations to be encoded in a rule language, some of which bear a passing resemblance to English. The BRE can then evaluate the rules against enterprise data to ensure that the business transactions, etc. that the enterprise is performing comply with those rules. Today one of the most popular rule engines is Drools, an open source engine sponsored by Red Hat with a powerful rule language, called DRL, and a highly efficient algorithm that can scale to support hundreds of thousands of rules and terabytes of data.

Rule engines are a great idea. It’s much easier to simply specify all the rules that should apply to a particular transaction than it is to write a program in a traditional language like Java to verify compliance. And it’s much easier to change the rules in a BRE when needed than to modify and test a traditional application. Today’s focus on digital transformation is finding ever wider applications for BREs, from cleansing big data, to fraud detection to identifying patterns in event streams from the Internet of Things.

However, rules engines have had difficulty gaining traction with the business community - the analysts and business experts that understand the rules of the business and would be the logical people to encode them in a rule language. Even though such languages can be tailored for ‘business users’, it is still a steep learning curve fraught with potential pitfalls - conflicts between rules, unseen overlaps, gaps and so on. These difficulties have limited the adoption of BREs to organizations with more complex needs, and with the skills needed to fully utilize the capabilities of a BRE.

Now, however, there is a new approach. In 2015 the Object Management Group published a specification for a graphical decision language designed specifically for business users. Now in its 3rd year and version 1.2, Decision Model and Notation (DMN) manages to combine a straightforward representation of the inputs, outputs and rules that govern a business decision, with the expressiveness needed to encompass the information required to fully automate any decision. At the heart of DMN is an expression language called FEEL, for Friendly Enough Expression Language. FEEL is designed from the outset for use by business people, allowing them to specify business logic with no greater complexity than a typical Excel spreadsheet.

Here at Red Hat we are proud to offer full DMN support with our rule engine - Red Hat Decision Manager. If you would like to learn more about DMN, and how it might help you better automate business decisions, we have joined forces with the IIBA to host a webinar on October 18 at 1pm ET to further explore the topic. I’ll be joined by Denis Gagne at Trisotech for a deeper dive into DMN and the Red Hat engine. If you would like to register you can do so here, and we’ll be delighted to have you join!