Synthetic data generation, a cornerstone of Generative Artificial
Intelligence, signifies a paradigm shift in data science by addressing data
scarcity and privacy while enabling unprecedented performance. As synthetic
data gains prominence, questions arise concerning the accuracy of statistical
methods when applied to synthetic data compared to raw data. In this article,
we introduce the Synthetic Data Generation for Analytics framework. This
framework employs statistical methods on high-fidelity synthetic data generated
by advanced models such as tabular diffusion and Generative Pre-trained
Transformer models. These models, trained on raw data, are further enhanced
with insights from pertinent studies. A significant discovery within this
framework is the generational effect: the error of a statistical method on
synthetic data initially diminishes with added synthetic data but may
eventually increase or plateau. This phenomenon, rooted in the complexities of
replicating raw data distributions, highlights a "reflection point"--an optimal
threshold in the size of synthetic data determined by specific error metrics.
Through three illustrative case studies-sentiment analysis of texts, predictive
modeling of structured data, and inference in tabular data--we demonstrate the
effectiveness of this framework over traditional ones. We underline its
potential to amplify various statistical methods, including gradient boosting
for prediction and hypothesis testing, thereby underscoring the transformative
potential of synthetic data generation in data science.

合成数据生成是一种通过解决数据稀缺性和隐私问题、实现前所未有的性能而在数据科学领域引起了一场转变的生成人工智能的基石。本文介绍了用于分析的合成数据生成框架，该框架利用了统计方法在高保真度合成数据上的运用，这些数据由诸如表扩散和预训练生成转换模型等先进模型生成，并通过相关研究得到进一步的改进。通过三个案例研究：文本情感分析、结构化数据预测建模和表格数据推断，我们证明了该框架在传统方法之上的有效性，强调了合成数据生成在数据科学中的转化潜力。