Intelligent Business Process Methods




Business decisions permeate operational, tactical and strategic activities. And although business process management (BPM) provides a mechanism for tying some of these activities together with workflows, it says nothing about the decision itself. While BPM has been well served by the methodology encapsulated in BPMN (Business Process Modeling and Notation), the same has not existed for business decisions until very recently. DMN (Decision Model and Notation) provides a bridge between the world of the business process and that of the decision model. These latter involve big data, predictive analytics, optimization, data mining, statistics and business intelligence technologies. It is another universe focused exclusively on improving the efficiency and efficacy of business decisions. BPM serves to tie together decision points and other activities, but offers no framework for understanding them.

DMN provides diagramming conventions and structures which make the decision understandable by a wide range of interested parties – from managers to data scientists. The following excerpt from the DMN specification explains its purpose very well:

β€œThe primary goal of DMN is to provide a common notation that is readily understandable by all business users, from the business analysts needing to create initial decision requirements and then more detailed decision models, to the technical developers responsible for automating the decisions in processes, and finally, to the business people who will manage and monitor those decisions. DMN creates a standardized bridge for the gap between the business decision design and decision implementation. DMN notation is designed to be useable alongside the standard BPMN business process notation.”

Prior to DMN the business process and the decision model had no real meeting point. Decision models might exist as callable services, or be embedded in applications, but the most a business process model could do was simply acknowledge that a decision needed to be made. DMN splits the decision into two parts – the logic and the model. The former might be represented in PMML (Predictive Model Markup Language), while the model can now be specified using a Decision Requirements Diagram (DRD) – a higher level view of the decision specifying inputs and outputs. This in turn can be linked to the business process.

While DMN will accelerate the automation of decisions, there is need for a broader decision management capability. It is not atypical for larger organizations to employ thousands of decision models. They need to be documented, monitored for performance and integrity, easily modified, and accessible for those who might want to understand them (regulators for example). As such, an encompassing decision management discipline is needed, and without it businesses will flounder as the number and complexity of decision models proliferates.

It is important to realize that decision automation offers an opportunity for greatly improved agility as decision logic is extracted from process logic, for enhanced effectiveness as decisions become more accurate, and for very significant improvements in efficiency as throughput increases. Standards such as DMN in conjunction with a decision management platform provide the conceptual and operational infrastructure necessary for enterprise wide decision automation, and those with the foresight to put these things in place will benefit accordingly.

The previous article in this series is Adding Intelligence to Business Processes

The next article in this series is Applying Decisions to Business Processes

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