Synthetic data defined, uses, risks, and best practices

The article presents a five‑stage operating map: (1) define the target distribution and constraints, (2) generate records with a model or simulator, (3) filter invalid and duplicate samples, (4) measure fidelity, diversity, utility, and leakage, and (5) mix or iterate according to the application. Each stage requires clear ownership, documented inputs and outputs, and validation evidence so teams can trace failures back to earlier assumptions.

A central warning is that recursive training on narrow synthetic outputs can amplify artifacts and reduce diversity, making downstream performance brittle. The guide recommends rigorous evaluation—using untouched test sets, shadow‑mode trials, and explicit stop conditions—and thorough versioning of all inputs and configurations to ensure that synthetic data benefits, such as lower data‑collection cost and privacy protection, are realized without compromising model quality.