Decision Modeling with DMN
Most business requirements center around processes and data. Knowledge, expertise, and predictive analytics are typically shoehorned into a process or data definition. This leads to solutions that quickly become complex and hard to change.
For business rules projects, rules analysis approaches tend to capture many individual rules. This results in large numbers of low-level rules, often with multiple versions, that quickly become hard to change and maintain.
For predictive analytics projects, CRISP-DM and other methods stress the importance of business understanding but lack a repeatable, understandable format.
Using existing tools and technologies to capture decision requirements is an exercise in frustration. Tying decision-making to business results is ad-hoc at best. Documenting process designs, use cases, requirements and business rules results in incomplete, disconnected and superficial models of decision-making. Only decision modeling empowers you to truly drive digital processes for transformative operational efficiency gains.
What people are saying about Decision Modeling:
“I have been modeling several taxation areas and defining their requirements for more than 10 years with Finnish Tax Administration. During this time I have created dozens of different models such as process charts, flowcharts and use case models. All these models have needed plenty of effort and both calendar and working time.
Against this background, I am truly amazed by decision modeling. This method gives me an agile tool for modeling quite complicated domains. Furthermore it only takes a couple of short workshops to draw a decision model. The notation is very simple, which gives the specialists the opportunity to focus on the contents instead of trying to understand the notation. Because of the simplified notation decision modeling is also a powerful tool for supporting discussions, orientation and presentations for different audiences – even the top management.”