What Is Synthetic Data?

Synthetic data is artificially generated data designed to resemble real datasets.

In machine learning, synthetic data can be useful when:

• real data is scarce,

• privacy restrictions limit sharing,

• edge cases are rare,

additional training examples are needed.

Synthetic data can be generated using:

• generative models,

• simulation systems,

• rule-based generators,

hybrid approaches combining real and artificial examples.

When used carefully, synthetic datasets can help expand training coverage and improve model robustness.

However, synthetic data must still be evaluated carefully. Poorly generated examples can introduce bias or reinforce incorrect patterns.

The goal is not simply to generate more data, but to generate useful training signals that improve model behavior.