The creation of plausible, factually-grounded data for training of machine models rather than, or in addition to, importing real-world data. Synthetic data use is intended to reduce bias, quickly train models, and improve accuracy. For example, synthesizing factually-accurate data about the population of a university might be preferable to risking leaks of individuals' real addresses, grades, or other private information.