  Synthetic data generation, a cornerstone of Generative Artificial
Intelligence, promotes a paradigm shift in data science by addressing data
scarcity and privacy while enabling unprecedented performance. As synthetic
data becomes more prevalent, concerns emerge regarding the accuracy of
statistical methods when applied to synthetic data in contrast to raw data.
This article explores the effectiveness of statistical methods on synthetic
data and the privacy risks of synthetic data. Regarding effectiveness, we
present the Synthetic Data Generation for Analytics framework. This framework
applies statistical approaches to high-quality synthetic data produced by
generative models like tabular diffusion models, which, initially trained on
raw data, benefit from insights from pertinent studies through transfer
learning. A key finding within this framework is the generational effect, which
reveals that the error rate of statistical methods on synthetic data decreases
with the addition of more synthetic data but may eventually rise or stabilize.
This phenomenon, stemming from the challenge of accurately mirroring raw data
distributions, highlights a "reflection point"-an ideal volume of synthetic
data defined by specific error metrics. Through three case studies, sentiment
analysis, predictive modeling of structured data, and inference in tabular
data, we validate the superior performance of this framework compared to
conventional approaches. On privacy, synthetic data imposes lower risks while
supporting the differential privacy standard. These studies underscore
synthetic data's untapped potential in redefining data science's landscape.
