Synthetic Data

Synthetic data is artificially generated data that mimics the statistical properties and structure of real-world data without containing any actual real records. It is created using statistical models, generative AI, or rule-based simulation seeded by real data characteristics.

Synthetic data is used for software testing (when real data is too sensitive for test environments), machine learning model training (when labeled real data is scarce), and privacy compliance (replacing personal data with realistic substitutes for sharing or analysis without GDPR concerns).

The quality of synthetic data is measured by how faithfully it preserves the statistical distributions, correlations, and edge cases of the real dataset it represents. Poor synthetic data can produce models that work in testing but fail on real-world inputs - a phenomenon called distribution shift.