This paper presents an approach for using rich datasets to develop different building archetypes depending on the urban energy challenges addressed. Two cases (building retrofitting and electric heating) were analysed using the same city, Stockholm (Sweden), and the same input data, energy performance certificates and heat energy use metering data. The distinctive character of these problems resulted in different modelling workflows and archetypes being developed. The building retrofitting case followed a hybrid approach, integrating statistical and physical perspectives, estimating energy savings for 5532 buildings from seven retrofitting packages. The electric heating case provided an explicitly statistical data-driven view of the problem, estimating potential for improvement of power capacity of the local electric grid at peak electric power of 147 MW. The conclusion was that the growing availability of linked building energy data requires a shift in the urban building energy modelling (UBEM) paradigm from single-logic models to on-request multiple-purpose data intelligence services. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).