  Data synthesis is a privacy enhancing technology aiming to produce realistic
and timely data when real data is hard to obtain. Utility of synthetic data
generators (SDGs) has been investigated through different utility metrics.
These metrics have been found to generate conflicting conclusions making direct
comparison of SDGs surprisingly difficult. Moreover, prior research found no
correlation between popular metrics, concluding they tackle different
utility-dimensions. This paper aggregates four popular utility metrics
(representing different utility dimensions) into one using
principal-component-analysis and checks whether the new measure can generate
synthetic data that perform well in real-life. The new measure is used to
compare four well-recognized SDGs.
