  Testing in production-like test environments is an essential part of quality
assurance processes in many industries. Provisioning of such test environments,
for information-intensive services, involves setting up databases that are
rich-enough to enable simulating a wide variety of user scenarios. While
production data is perhaps the gold-standard here, many organizations,
particularly within the public sectors, are not allowed to use production data
for testing purposes due to privacy concerns. The alternatives are to use
anonymized data, or synthetically generated data. In this paper, we elaborate
on these alternatives and compare them in an industrial context. Further we
focus on synthetic data generation and investigate the use of recurrent neural
networks for this purpose. In our preliminary experiments, we were able to
generate representative and highly accurate data using a recurrent neural
network. These results open new research questions that we discuss here, and
plan to investigate in our future research.
