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Building a New Year’s Resolution Decision Model with DMN


Back in November I posted a humorous Thanksgiving guest decision model to LinkedIn. I just repeated the exercise with a decision model to help you assess a New Year’s Resolution.

While these are just for fun, I thought it might be worth sharing how I built this one. Normally we like to work top-down talking to business experts but in this case I did not have any to work with so I had to start bottom-up with research.

  1. I started with some articles – found using google – and each became a Knowledge Source in the diagram.
  2. I looked over each and identified the things it implied you should decide about a New Year’s resolution to help you decide if it was a good one or not.
  3. As I added these Decisions to my model, I connected them to the Knowledge Sources that related to them (some Decisions recurred in several articles, of course).
  4. One set of Decisions – deciding if a resolution met the five criteria to be SMART – could be grouped as sub-decisions of a higher-level Decision.
  5. Others were grouped based on thematic elements – a common approach where there is not a specific structure driven by regulation or similar.
  6. This gave me a structure – the Decision I was trying to model, some high-level sub-decisions and logically grouped sub-decisions.
  7. Cleaning up the diagram required putting copies of the Knowledge Sources on the diagram (though these point to the same instance in the underlying repository).

In this case I didn’t deal with Input Data as the model seemed useful with just Decisions and Knowledge Sources. To finish it, we would need to identify data elements and write decision logic (or develop predictive models) for each element.

If you are interested in decision modeling, why not register for our upcoming live online Decision Modeling with DMN class in March.

Happy New Year.


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