Decision Requirements Models based on the Decision Model and Notation (DMN) standard deliver a powerful ROI by improving processes, effectively managing business rules projects, framing predictive analytics efforts, and ensuring decision support systems and dashboards are action-oriented.
This paper describes the iterative steps to develop and complete an effective Decision Requirements Model using the DMN notation. Defining decision requirements as part of your overall requirements process offers many benefits:
- Modeling decisions makes business processes less complex and more robust in the face of change and easier to manage.
- A Decision Requirements Model provides the needed structure for the implementation of a Business Rules Management Systems (BRMS), streamlining rules analysis, simplifying rules maintenance and supporting iteration and agile development.
- Framing data mining and predictive analytics projects with a Decision Requirements Model links analytics to business results and helps ensure successful deployment.
- Understanding the decisions relevant to a dashboard or decision support environment structures knowledge and puts a premium on taking action.
- Decision Requirements Models are a common language across business, IT and analytic organizations improving collaboration, increasing reuse, and easing implementation.
This paper describes why modeling decisions is effective and necessary and walks through the steps involved in identifying, describing, modeling and refining Decision Requirements Models. Appendices include a discussion of the various kinds of decisions suitable for modeling and some techniques to help discover them.
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