What Is Synthetic Data?

Synthetic data is artificially generated information that mimics the statistical properties and structure of real-world data without being collected from real events, people, or transactions. Instead of recording what actually happened, a model learns the patterns inside a real dataset and then produces brand-new examples that follow those same patterns. The result is data that looks and behaves like the original, yet contains no real records and exposes no real individuals.

The defining trait of synthetic data is that it preserves the relationships in a dataset while replacing the actual values. A synthetic medical dataset, for example, keeps the realistic correlations between age, symptoms, and diagnoses that a model needs to learn from, but every patient in it is fictional. The same holds for synthetic financial transactions, synthetic images, or synthetic sensor readings: the statistical signal survives, the real-world source does not.

Synthetic data generally falls into three types:

• Fully synthetic data, where every value is generated by a model and no real records are present at all. This offers the strongest privacy protection and is the most common output of diffusion models.
• Partially synthetic data, where only the sensitive fields of a real dataset are replaced with generated values, while non-sensitive fields stay intact. This is often used when teams need to protect specific identifiers but keep the rest of the real signal.
• Hybrid synthetic data, where real and generated records are blended into one dataset, usually to expand a small real sample with extra synthetic examples.

These types are produced by training a model on real data and then sampling new examples from what it learned. Modern approaches use generative architectures such as diffusion models, which create data by reversing a noise process, or earlier methods like GANs. They differ from simple data augmentation, which only transforms existing records, such as rotating an image or adding small variations, rather than producing genuinely new samples drawn from the underlying distribution.

It is worth being precise about what synthetic data is not. It is not anonymized data, which starts from real records and strips identifying fields, and it is not random or fake data with no grounding in reality. Synthetic data sits between those two: it is fully artificial, so it carries no direct link to a real person, yet it is statistically faithful enough to train a model that will later perform on real inputs. That combination is exactly why teams reach for it when real data is too scarce, too expensive, or too restricted by privacy rules to use directly.