Faking It to Make It: When Synthetic Data Actually Works
TL;DR for operators Synthetic data is not magic fake data that politely becomes real after a procurement cycle. It is a set of techniques for generating artificial records that imitate useful properties of real datasets, and its value depends on what bottleneck you are trying to remove. Li et al.’s tutorial proposal, Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era, is best read as a map of the modern synthetic-data stack: GANs, diffusion models, and LLMs; text, tabular, graph, sequential, visual, and multimodal data; evaluation criteria; and practical deployment settings in health, finance, and education.1 It is not a benchmark paper. It does not run a new experiment showing that synthetic data improves business outcomes by some conveniently rounded percentage. That is inconvenient, but also useful. The paper is trying to organise the field, not sell a miracle. ...