Definition
Synthetic data is artificial information generated by algorithms to copy the statistical patterns of real data, without containing any actual real-world records.[1]
At a glance
- Made by software, not collected from real customers or events.[1]
- Keeps the patterns of real data so AI and tests still behave realistically.[3]
- Cuts privacy exposure because there are no actual people’s records inside.[2]
- Not automatically safe or compliant — re-identification risk can remain.[4]
Why businesses care
It gives you data to train AI, test software, and run what-if analysis when real data is scarce, slow to get, or legally sensitive. Gartner expects synthetic data to overtake real data in AI training by 2030, making it a core supply for any data-driven product or model.[2]
The catch
Synthetic does not mean automatically anonymous. If the generated data still lets someone be re-identified through patterns or by linking other datasets, regulators like those under GDPR may treat it as personal data. Quality and bias also carry over — bad source data makes bad synthetic data.[4]
Bottom line
Synthetic data is a software-made stand-in for real data that lets you build and test safely at scale, but only if you verify it cannot be traced back to real people.