Synthetic Data
Last updated June 14, 2026
What is Synthetic Data in simple terms?
In simple terms, synthetic data is fake-but-realistic data made by a computer instead of collected from the real world. Like a flight simulator standing in for real flying hours, it lets an AI practice when real examples are scarce.
What is Synthetic Data?
Synthetic data is artificially generated information — produced by an algorithm or simulation rather than recorded from real-world events — that is used in place of, or alongside, real data to train, test, or protect the privacy of machine learning systems.
Machine learning systems learn from examples, and gathering enough real examples is often the hardest part of building one. The data may be expensive to collect, slow to accumulate, legally sensitive, or simply too rare — there just aren't many recorded cases of the thing you care about. Synthetic data is the workaround: instead of recording real events, you generate realistic stand-ins with a program. That program might be a simple set of rules and random numbers, a physics simulation, or a generative model that has learned to produce convincing new examples. The result looks and behaves enough like the real thing to be useful for training or testing, without being a recording of any actual event or person.
It earns its keep in three main ways. First, **volume and rarity**: a self-driving system needs to handle a child darting into the road, but you can't ethically stage thousands of those — a simulator can. Second, **privacy**: a hospital can generate a synthetic patient dataset that preserves the statistical patterns researchers need while corresponding to no real individual, sidestepping a lot of risk. Third, **control and labeling**: when you generate the data yourself, you already know the correct answer for every example, so it arrives perfectly labeled, which spares the slow, costly human work of data labeling. Increasingly, synthetic data is also used to train large AI models when high-quality real text or images run short.
There's a real catch, and it's worth understanding. Synthetic data is only as good as the process that made it, so it inherits that process's blind spots — if your generator never imagines a rare situation, your model never learns it. Worse, train a model heavily on data produced by another model and small errors can compound across generations, a degradation researchers call model collapse, where outputs drift toward bland sameness and lose the variety of the real world. So synthetic data is best treated as a powerful supplement — filling gaps, protecting privacy, multiplying rare cases — rather than a wholesale replacement for the messy, surprising data of reality.
Real-world example of Synthetic Data
A startup is building software to spot fraudulent insurance claims, but it has a problem familiar to anyone who's tried: genuine fraud is rare, so out of a million real claims only a tiny handful are confirmed scams — far too few examples for the AI to learn the pattern reliably. Rather than wait years to accumulate more, the team studies the structure of the known fraud cases and generates thousands of synthetic claims that follow the same suspicious patterns — inflated repair costs, conveniently missing paperwork, dates that don't line up — each one fabricated, none tied to a real policyholder. They train the model on this enriched mix, and it gets noticeably better at flagging the real thing. The synthetic claims were never filed by anyone; they existed only to teach the model what fraud tends to look like.
Related terms
Frequently asked questions about Synthetic Data
What is the difference between synthetic data and real data?
Real data is recorded from actual events — real transactions, real photos, real patients. Synthetic data is generated by a program to resemble real data without being drawn from any actual event or person. The trade-off is control versus authenticity: synthetic data can be produced cheaply, in bulk, already labeled, and free of privacy concerns, but it can only reflect what its generator knows to include. Real data carries the full, surprising messiness of the world but is slower, costlier, and often legally sensitive to collect.
How is synthetic data generated?
Through several methods of increasing sophistication. The simplest use rules and randomness to fabricate plausible records. Simulations recreate an environment — a virtual road, a factory floor — and capture data from it, complete with known correct answers. The most advanced use generative models that have learned the statistical patterns of real data and can produce convincing new examples. In every case the goal is the same: output that matches the patterns of reality closely enough to be useful, while corresponding to no real-world event.
What is synthetic data used for?
It's used wherever real data is scarce, expensive, sensitive, or hard to label. Teams use it to multiply rare cases, like dangerous scenarios for self-driving systems; to protect privacy by replacing sensitive records with realistic non-real ones; and to produce perfectly labeled training sets cheaply. It's also increasingly used to help train large AI models as high-quality real-world text and images become harder to source in sufficient quantity.