This month we return to a topic I’ve written about twice before, data validation in DMN models.  This post, in which I will describe a third method, is hopefully the last word.

Beginning decision modelers generally assume that the input data supplied at execution time is complete and valid.  But that is not always the case, and when input data is missing or invalid the invoked decision service returns either an error or an incorrect result.  When the service returns an error result, typically processing stops at the first one and the error message generated deep within the runtime is too cryptic to be helpful to the modeler.  So it is important to precede the main decision logic with a data validation service, either as part of the same decision model or a separate one.  It should report all validation errors, not stop at the first one, and should allow more […]