Having evaluated all the measures and selected the best model for this case, and much of the machine learning process has been clarified, our understanding of the problem context is still relatively immature. That is, while we have carefully specified the problem, we still do not fully understand what drives that target. Convincing management to support the implementation of the model typically includes explaining the answers to “why,” “what,” “where,” and “when” questions embedded in the model. While the model may be the best overall possible model according to selected measures, for the particular problem related to hospital readmissions, it is still not clear why the model predicts the readmission of some patients will be readmitted and that others will not. It also remains unknown what features drive these outcomes, where the patients who were readmitted come from, or whether or not this is relevant. In this case, access to time information is also unavailable––when, so it is not relevant, but it is easy to imagine that patients admitted in the middle of the night might have worse outcomes due to tired staff or lack of access to the best physicians. If we can convince management that the current analysis is useful, we can likely also make a case for the collection of additional data. The new data might include more information on past interactions with this patient, as well as date and time information to test the hypothesis about the effect of time-of-admission and whether the specific staff caring for a patient matters.