Synthetic Data
Definition
Synthetic data replicates selected characteristics of real data with artificially generated examples.
Synthetic data is generated through rules, simulation, or generative models to replicate characteristics of real data for development or testing.
Simply explained
You create practice or test data instead of using only real cases. These artificial examples are intended to replicate the required characteristics.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A company generates rare error scenarios for testing and validates whether distributions and edge cases are realistically represented.
Limits and pitfalls
- Synthetic does not automatically mean anonymous or unbiased; models can leak training data or amplify existing biases.
Origin & history
Simulation has long been established; privacy-friendly and generative methods made synthetic datasets more popular in the 2010s.
Use in mid-sized companies
- Possible application: A company generates rare error scenarios for testing and validates whether distributions and edge cases are realistically represented.
Frequently asked questions
What does Synthetic Data mean?
Synthetic data is generated through rules, simulation, or generative models to replicate characteristics of real data for development or testing. What should you watch out for? Synthetic does not automatically mean anonymous or unbiased; models can leak training data or amplify existing biases.
Sources
- Differential Privacy and Synthetic Data · NIST, Differential Privacy and Synthetic Data
Source checked on 10.10.2026. Editorial use cases are hypothetical; no evidence of real customer results.
Editorially reviewed on: