What is Synthetic Data?

Where it genuinely helps

Rare event coverage is the strongest case. If catastrophic equipment failures occur twice a year, you will never collect enough real examples. Simulation can generate thousands of physically plausible failure scenarios.

Simulation-first domains — robotics, autonomous vehicles, industrial control — depend on it. Testing edge cases in reality is expensive or unsafe; in simulation it is free and repeatable.

Development and testing is an underrated everyday use. Realistic-looking non-real data lets engineers build and test pipelines without production access, which is good security practice regardless of any modelling benefit.

Deliberate augmentation — rotating and re-lighting images, paraphrasing text — is synthetic data generation by another name and reliably reduces overfitting.

Constrained data sharing allows a vendor or research partner to work on data resembling yours without receiving customer records.

The limitation that matters most

A generator can only produce patterns present in what it learned from. If your real dataset lacks a phenomenon, synthetic data will not contain it either. This makes synthetic data good at interpolation — filling in around known structure — and unreliable at extrapolation into genuinely unfamiliar territory.

The failure this produces is model collapse: train a model on synthetic data from a generator trained on limited real data, and errors compound while diversity narrows. Each generation drifts further from reality while looking internally consistent, which makes it hard to notice.

There is also a reality gap in simulation. Physics engines approximate friction, lighting, and material behaviour, and models trained purely in simulation often fail on real inputs. Domain randomisation — varying simulation parameters aggressively so the model cannot depend on any single approximation — narrows the gap without closing it.

The practical rule: use synthetic data to supplement real data, never to replace it. Always keep a real held-out evaluation set. A model validated only on synthetic data has been tested against its own assumptions.

It does not automatically solve privacy or bias

Both claims are frequently overstated by vendors and deserve scepticism.

On privacy: synthetic data generated from personal data is not automatically anonymous. Generative models can memorise and reproduce training examples, particularly outliers — and outliers are exactly the records most identifying. A synthetic dataset containing a near-copy of the one patient with a rare combination of attributes has not protected that person. Where privacy is a legal requirement, the defensible route is a generator trained under differential privacy, which provides a mathematical guarantee at a measurable cost to fidelity, plus empirical membership-inference testing. 'It is synthetic, therefore GDPR does not apply' is not a position that survives scrutiny.

On bias: a generator learns the distribution it was shown, including its skews. Synthesising more data from a biased source reproduces the bias at greater volume and can lend it false authority. Synthetic data can help correct imbalance, but only through deliberate conditional generation of under-represented groups, and even then the model is inventing plausible members of a group it has seen little of. Verify against real data for those groups. See training data on provenance and consent.