  The switch from a Model-Centric to a Data-Centric mindset is putting emphasis
on data and its quality rather than algorithms, bringing forward new
challenges. In particular, the sensitive nature of the information in highly
regulated scenarios needs to be accounted for. Specific approaches to address
the privacy issue have been developed, as Privacy Enhancing Technologies.
However, they frequently cause loss of information, putting forward a crucial
trade-off among data quality and privacy. A clever way to bypass such a
conundrum relies on Synthetic Data: data obtained from a generative process,
learning the real data properties. Both Academia and Industry realized the
importance of evaluating synthetic data quality: without all-round reliable
metrics, the innovative data generation task has no proper objective function
to maximize. Despite that, the topic remains under-explored. For this reason,
we systematically catalog the important traits of synthetic data quality and
privacy, and devise a specific methodology to test them. The result is DAISYnt
(aDoption of Artificial Intelligence SYnthesis): a comprehensive suite of
advanced tests, which sets a de facto standard for synthetic data evaluation.
As a practical use-case, a variety of generative algorithms have been trained
on real-world Credit Bureau Data. The best model has been assessed, using
DAISYnt on the different synthetic replicas. Further potential uses, among
others, entail auditing and fine-tuning of generative models or ensuring high
quality of a given synthetic dataset. From a prescriptive viewpoint,
eventually, DAISYnt may pave the way to synthetic data adoption in highly
regulated domains, ranging from Finance to Healthcare, through Insurance and
Education.
