  The proliferation of deep learning techniques led to a wide range of advanced
analytics applications in important business areas such as predictive
maintenance or product recommendation. However, as the effectiveness of
advanced analytics naturally depends on the availability of sufficient data, an
organization's ability to exploit the benefits might be restricted by limited
data or likewise data access. These challenges could force organizations to
spend substantial amounts of money on data, accept constrained analytics
capacities, or even turn into a showstopper for analytics projects. Against
this backdrop, recent advances in deep learning to generate synthetic data may
help to overcome these barriers. Despite its great potential, however,
synthetic data are rarely employed. Therefore, we present a taxonomy
highlighting the various facets of deploying synthetic data for advanced
analytics systems. Furthermore, we identify typical application scenarios for
synthetic data to assess the current state of adoption and thereby unveil
missed opportunities to pave the way for further research.
