Synthetic data can come from simulations, statistical methods or generative AI. A team might create sample financial transactions, medical-looking records, images or textbook-style questions to develop a model or test an application.
It is useful when genuine examples are expensive, scarce, sensitive or unbalanced. Engineers can design examples for rare conditions and create repeatable tests. However, generated records are only as representative as the assumptions and models used to make them.
Artificial does not automatically mean private, unbiased or accurate. Some generators can reproduce sensitive source patterns, miss unusual events or exaggerate features already present in training data. A large synthetic set can appear diverse while covering the same narrow situations repeatedly.
Label synthetic records, describe their generation method, compare them with independent real examples and check privacy and licensing conditions before distributing them.