  With the explosion of applications of Data Science, the field is has come
loose from its foundations. This article argues for a new program of applied
research in areas familiar to researchers in Bayesian methods in AI that are
needed to ground the practice of Data Science by borrowing from AI techniques
for model formulation that we term ``Decision Modelling.'' This article briefly
reviews the formulation process as building a causal graphical model, then
discusses the process in terms of six principles that comprise \emph{Decision
Quality}, a framework from the popular business literature. We claim that any
successful applied ML modelling effort must include these six principles.
  We explain how Decision Modelling combines a conventional machine learning
model with an explicit value model. To give a specific example we show how this
is done by integrating a model's ROC curve with a utility model.
