What Is Synthetic Data? A Practical Explanation

Synthetic data is data that was generated rather than collected. A model, a simulator, or a rules engine produces records that look like real ones, and those records are then used to train, test, or demonstrate something. The defining property is that no real event produced them.

That property is the whole story, good and bad. Synthetic data is excellent at coverage and unreliable about truth. It can give you a hundred thousand examples of a situation that has happened to you twice. It cannot tell you anything about the world that was not already implied by whatever generated it.

The three things it is genuinely good for

Privacy-constrained work. When the real data is medical records, financial transactions, or anything with names attached, a synthetic set that preserves the statistical shape without corresponding to real people lets you build and share systems that would otherwise be legally impossible to work on. This is the oldest and least controversial use.

Rare event augmentation. Fraud, equipment failure, edge-case defects. Real datasets are dominated by the normal case, and a model trained on them learns to predict normal. Generating additional examples of the rare class gives the model something to learn from. The rare class is also exactly where generation is hardest to get right, which is the tension in this use case.

Test fixtures and demos. The most common practical use and the least discussed. Populating a staging environment, building a demo account that looks alive, writing test cases that cover formats you have not encountered yet. There is no truth claim here at all, so almost none of the risks apply. If you want the technique, see prompting AI to generate realistic test data.

The failure mode that matters

Synthetic data inherits the assumptions of whatever made it, and then those assumptions become invisible.

If you generate customer records with a model, you get records that reflect the model's idea of a customer, including its biases about names, locations, incomes and spending. Train on that and you have laundered an assumption into a measurement. The output looks like evidence. It is a restatement of a prior.

The stronger version of this problem appears when models are trained on the output of models, across generations. Research published in Nature in 2024 found that training on recursively generated data causes models to collapse, losing the tails of the original distribution first and drifting toward a narrower, more average output. The rare cases go first, which is unfortunate given that generating rare cases is one of the main reasons people reach for synthetic data. There is a fuller treatment in what model collapse is.

Real, synthetic, and where each belongs

The row worth memorising is the first. Evaluate on real data. A system that scores well on synthetic data has demonstrated that it understands your generator, which is not the claim anyone wanted to make.

How to use it without fooling yourself

Three rules cover most situations.

Keep a real holdout, always. However much synthetic data goes into training, the evaluation set is real, untouched, and collected the way production data will be. If the real holdout is too small to be meaningful, that is important information about your project rather than a reason to substitute generated data.

Label its origin and keep the label. Every synthetic record should be traceable as synthetic, in the data itself, permanently. Mixed datasets that lost their provenance are extremely difficult to untangle later, and "which of these rows were real" is a question you will eventually need to answer.

Check the tails, not the averages. Synthetic sets usually match the mean well and the extremes badly. Compare the 1st and 99th percentiles of your generated data against the real distribution. That is where the generator's assumptions show up, and it is a five minute check.

Is it worth it for a small team?

For test data and demo environments, yes, immediately, and it is one of the easier wins available. For training, only when you have a genuine data scarcity problem and a real evaluation set to check yourself against. For anything you plan to describe as a finding, no.

The useful mental model: synthetic data is a way to stretch what you already know, not a way to learn something new. Used for the first, it saves real time. Used for the second, it produces confident answers with nothing behind them.

FAQ

Is synthetic data the same as fake data?

Mechanically, yes. The distinction people draw is intent and rigour: synthetic data is usually generated to preserve specific statistical properties of a real source, while fake data just needs to look plausible. Test fixtures are fake data and that is fine.

Does synthetic data solve privacy problems completely?

No. Poorly generated synthetic data can leak information about the individuals in the source set, particularly for unusual records that the generator effectively memorised. Treat it as a strong risk reduction rather than an exemption.

Can I fine-tune a model on synthetic data?

You can, and it is common practice, especially for teaching format and structure rather than facts. Keep a real evaluation set, and expect diminishing returns as the training data drifts further from anything real. The trade-offs are laid out in what fine-tuning actually is.

How do I know if my synthetic data is any good?

Train on synthetic, evaluate on real, and compare against a baseline trained only on whatever real data you have. If the synthetic addition does not improve the real-data score, it is not helping, however realistic it looks.

For the wider context on how models learn from data in the first place, see how AI models work.