Synthetic Data
Artificially generated training data produced by simulation or generative models instead of direct real-world collection
#Synthetic Data#synthetic dataset#generated data#artificial training data
What is synthetic data?
Synthetic data is artificially generated data created through simulators, rules, or generative models rather than direct collection from real users or environments.
It is widely used to augment rare cases, reduce privacy risk, and speed up experimentation.
Why does it matter?
When high-quality real-world data is expensive or restricted, synthetic data improves iteration speed and coverage.
It is especially useful in regulated or high-security domains where data access constraints are strict.
Practical checkpoints
- Distribution fidelity: Measure how closely synthetic distributions reflect real operational data.
- Bias control: Generation pipelines can amplify hidden assumptions if not audited.
- Hybrid training mix: Combining synthetic and real data is often more robust than relying on either alone.