Synthetic Data is artificially generated data — created by algorithms or AI rather than collected from the real world — used to train or test models when real data is scarce, sensitive, or expensive.
What it means in plain English
Sometimes real data is hard to get: it may be private, rare, costly, or simply insufficient. Synthetic data fills the gap by generating realistic artificial examples that share the statistical properties of real data without exposing any real individual. It can also deliberately include rare cases that are hard to collect naturally, helping models learn to handle them.
Its usefulness depends on how faithfully it reflects the real world.
A simple example
To train a fraud-detection model without exposing real customers’ financial records, a company might generate synthetic transactions that behave statistically like real ones — including rare fraud patterns — to train on safely.
Why it matters
Synthetic data helps overcome shortages of real data and protects privacy, and it is increasingly used to train and test AI, including other AI models. It is a growing tool for building capable systems where real data is limited or sensitive.
Related terms
- Training Data — what synthetic data supplements or replaces.
- Data Augmentation — a related way of expanding data.
- Generative AI — often used to create synthetic data.
Frequently asked questions
What is synthetic data?
Synthetic data is artificially generated data (rather than collected from the real world) used to train or test models, often created to fill gaps or protect privacy.
Why use synthetic data?
It can supplement scarce real data, cover rare cases, avoid exposing sensitive real records, and reduce data-collection cost — though it must reflect reality to be useful.
