Everyone makes decisions, every day. But the institutional history of decision making is vast and varied. Decision-making systems of the 1980s attempted to simulate the knowledge and analytical skills of human experts by structuring decisions as a hierarchy of goals, and then working backwards from these goals; a process known as backward chaining in AI lingo. The late 1990s saw the emergence of the “Business Rules Revolution,” turning the Expert System approach of the 80s on its head. The new prevailing thought was that decision-making should be defined primarily using declarative, modular and independent rules, which would be applied to data until a goal was reached, in a process known in AI lingo as forward chaining. The phase we are currently in began in the late 2000s. This phase shifts the focus from rules to decisions, reviving some of the expert system’s ideas, but preserving the benefits of the business rules approach: rules are still declarative, modular and independent, but they only have meaning in the context of a decision.
Let’s double click on the latest “wave” in the decision management world. How does it help you better codify and articulate analytically powered decision making processes? Well, the success of the Business Rules Revolution meant not only that we had great software like FICO Blaze Advisor, but also that a whole practice emerged around requirements gathering with a focus on business rules. The problem was, projects approached with this rules first mentality were prone to scope creep, and this was caused by the very nature of the Business Rules Revolution. As the name implies, the focus was on the rules, which naturally led to a bottom-up approach for requirements gathering.
The issue with focusing solely on business rules is simple: lack of context. Business rules are only relevant within the context of the greater decision that needs to be made. To improve upon this, the old approach was turned on its head and decision modeling was created. Now, context comes first and only then should the relevant rules be harvested. Hence, a top-down approach was born: Decision Requirements Analysis, which can be outlined with the creation of a Decision Requirements Diagram (DRD for short). Instead of starting with the individual business rules, or trying to force domain experts to think about everything required to make a decision up front, DRDs start with the decision you want to make, and then work your way back from that “goal”.
A Decision Requirements Diagram begins with a top level decision and maps the data and knowledge needed to get there. Made with FICO DMN Modeler.
So, you have a top level decision, what next? You need a way to work your way back from that goal. What is required to make this decision? Data and Business Knowledge for sure, but also the outcome of other decisions. These three components make up the structure of a DRD that can be visually represented in a diagram. By systematically decomposing all necessary decisions and only stopping when there are no decisions left to be decomposed, a DRD provides all the needed context for the knowledge required by the decisions.
Decision Modeling is the latest and greatest in analytically driven decision making, and FICO helped develop the standards and tools so businesses can take advantage of it. We were one of the first to leverage personal computers to put the power of decision-making systems in the hands of domain experts in the 1980s. In the 2000s we empowered business users to manage business logic directly with the introduction of the award-winning Business Rules Management System Blaze Advisor. Now, we have introduced FICO DMN Modeler, our way of learning from the past – and the manifestation of the evolution in decision-making technology. Anyone can use DMN Modeler and create their own DRDs; try the product for free on the FICO Analytic Cloud and join us in the decision modeling revolution.