Synthetic Data
Data that is generated artificially rather than collected from the real world, often created by AI to help train or test other AI. Think of it as practice material made to order: examples that look and behave like the real thing without coming from real people or events. This made-up data is called synthetic data.
Synthetic data is information that has been manufactured rather than gathered. Instead of collecting real customer records or real photographs, a team generates examples that resemble the real thing closely enough to be useful. Increasingly, AI itself is used to produce this data, which is then fed into training or testing for another system.
There are sound reasons to use it. Real data can be scarce, expensive, or sensitive. If a hospital wants to build a tool but cannot freely share patient records, synthetic records that carry the same statistical patterns, without belonging to any real person, offer a way forward. It can also fill gaps, supplying examples of rare situations that hardly appear in real data.
There are honest limits too. Synthetic data is only as good as the process that made it. If that process misses something about the real world, or carries a bias of its own, the shortcoming passes straight into whatever learns from it. There is also a subtler worry about models trained heavily on the output of other models drifting away from reality over time.
For a beginner, the term is most useful when reading about how AI is built. When a company mentions training on synthetic data, it means at least some of the practice material was generated rather than drawn from the real world, with all the benefits and caveats that involves.