  Existing digital distribution network models, like those in the databases of
network utilities, are known to contain erroneous or untrustworthy information.
This can compromise the effectiveness of physics-based engineering simulations
and technologies, in particular those that are needed to deliver the energy
transition. The large-scale rollout of smart meters presents new opportunities
for data-driven system identification in distribution networks, enabling the
improvement of existing data sets. Despite the increasing academic attention to
system identification for distribution networks, researchers often make
troublesome assumptions on what data is available and/or trustworthy. In this
paper, we highlight some differences between academic efforts and first-hand
industrial experiences, in order to steer the former towards more applicable
research solutions.
