  After a one-year long effort of research on the field, we developed a machine
learning-based classifier, tailored to predict whether a mechanical water meter
would fail with passage of time and intensive use as well. A recurrent deep
neural network (RNN) was trained with data extrapolated from 15 million
readings of water consumption, gathered from 1 million meters. The data we used
for training were essentially of two types: continuous vs categorical.
Categorical being a type of data that can take on one of a limited and fixed
number of possible values, on the basis of some qualitative property; while
continuous, in this case, are the values of the measurements. taken at the
meters, of the quantity of consumed water (cubic meters). In this paper, we
want to discuss the fact that while the prediction accuracy of our RNN has
exceeded the 80% on average, based on the use of continuous data, those
performances did not improve, significantly, with the introduction of
categorical information during the training phase. From a specific viewpoint,
this remains an unsolved and critical problem of our research. Yet, if we
reason about this controversial case from a data science perspective, we
realize that we have had a confirmation that accurate machine learning
solutions cannot be built without the participation of domain experts, who can
differentiate on the importance of (the relation between) different types of
data, each with its own sense, validity, and implications. Past all the
original hype, the science of data is thus evolving towards a multifaceted
discipline, where the designitations of data scientist/machine learning expert
and domain expert are symbiotic
