Synthetic Data
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
Artificially generated data with the statistical properties of real data but no real records, used when real data is scarce, private, or risky to collect.
What is Synthetic Data?
Synthetic data is artificially generated data that matches the statistical properties of real data without containing any real records. A synthetic patient file behaves like a real one in aggregate, yet describes no actual person.
Teams reach for it when real data is scarce, locked behind privacy rules, or dangerous to collect. It also fills gaps that real-world collection cannot: rare events a system must handle but almost never observes.
How Synthetic Data Works
A generator is fitted to or designed around the distributions of real data: a probabilistic model, a physics simulation, or a generative model such as a generative adversarial network. It then produces as many new records as needed, in whatever mix the task requires.
The core tension is fidelity versus privacy. A generator that copies the real distribution too faithfully risks leaking actual records; one that copies it too loosely produces data that trains models poorly.
One everyday use sits inside imbalanced data problems: techniques like SMOTE generate synthetic minority-class examples to rebalance a training set.
Synthetic Data vs Data Augmentation
Data augmentation transforms real data you already have; synthetic data is generated from scratch. An augmented image still contains the original photo, while a synthetic record contains no real record at all. That difference is why synthetic data serves privacy use cases (sharing realistic hospital data for research, for instance) that augmentation cannot.
| Synthetic data | Data augmentation | |
|---|---|---|
| Starting point | A generator, simulation, or statistical model | Real examples, transformed one at a time |
| Output | New records that match real data only statistically | Variations that still contain the original data |
| Typical use | Data that is scarce, private, or unsafe to collect | More training variety, less overfitting |
Example of Synthetic Data
Autonomous-vehicle developers train perception systems on simulated driving scenes: night rain, sun glare, a pedestrian stepping out between parked cars. The simulator renders millions of miles of such footage without a single real drive.
The rare, dangerous cases matter most. A real fleet waits years to encounter some of them; the simulator produces thousands of variations on demand, so the training set covers situations the road rarely offers.
Related AI terms: Data Augmentation · Imbalanced Data · Generative Adversarial Network · Training Set
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About