Definition
Synthetic data is data that is generated artificially rather than collected from real events, designed to mimic the statistical properties of real data. It can be tables that look like customer transactions, images of products that were never photographed, or conversations that never happened. Synthetic data is used to train and test AI systems when real data is scarce, sensitive, expensive to label or legally restricted.
In a company, synthetic data solves three practical problems. Privacy: you can develop and test with realistic records without exposing customer personal data. Scarcity: rare events such as fraud or equipment failures can be multiplied so a model learns them. Testing: you can generate edge cases that real data never produced. Language models are now commonly used to generate training and evaluation examples for other AI systems, including the test cases for evals.
Synthetic data has become a core ingredient in training modern models, with labs generating large volumes of reasoning and code examples, and it is increasingly used to build eval suites. The main misconception is that synthetic data is a free substitute for real data. Poorly generated data copies its own biases and errors, and models trained only on their own outputs degrade. It works best as a complement to real data with careful validation.
In practice
A bank needed to test a new fraud model but could not share real transactions with its development partner. It generated a synthetic data set with the same patterns and rare fraud cases, and the partner built and tested against it safely.
Why it matters
Synthetic data lets you build and test AI without putting customer data at risk or waiting years to collect rare cases. It is a practical answer to privacy and data scarcity in most industries.
Frequently asked questions
- Is synthetic data as good as real data?
- For many purposes it is good enough, especially for testing and for adding rare cases, and it is better for privacy. It is not a full replacement: its quality is bounded by the model or rules that generated it, so teams validate performance on real data before trusting a system trained on synthetic data.
- Does synthetic data solve GDPR concerns?
- It helps a lot when properly generated, because it contains no real individuals. But if it is derived from real records, it must be checked so that no real person can be re-identified. Treat it as a privacy tool that still needs review, not as an automatic exemption.