Synthetic Data - Page 3

Synthetic data is artificially generated information designed to resemble the structure or behavior of real data. It may be created with simulations, procedural rules, generative models, or combinations of these methods. Organizations use it to supplement rare examples, test systems, reduce collection costs, and limit exposure of sensitive records. Synthetic does not mean risk-free: generated data can reproduce bias, reveal patterns from source data, miss unusual cases, or create unrealistic relationships. Its value depends on the target task, not visual similarity alone. Teams compare distributions and downstream performance, document how generation occurred, separate synthetic evaluation from training, and apply privacy testing when the source contained personal or confidential information.