Boosting Data Analytics With Synthetic Volume Expansion

TL;DR

This paper introduces a framework for evaluating the effectiveness and privacy risks of synthetic data in data analytics, demonstrating how generative models can improve statistical analysis while maintaining privacy standards.

Contribution

The paper proposes the Synthetic Data Generation for Analytics framework, incorporating transfer learning and identifying an optimal synthetic data volume called the reflection point.

Findings

Error rate decreases with more synthetic data up to the reflection point

Synthetic data enhances statistical method performance in case studies

Lower privacy risks with differential privacy standards

Abstract

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.