Synthetic data generation creates artificial data to train or test AI models when real data is limited, sensitive, or imbalanced. The synthetic data mimics the statistical properties and patterns of real data without containing actual personal or proprietary information.
It is particularly useful when privacy regulations restrict access to real customer data, when certain edge cases are rare in historical data but important for model robustness, or when a new capability needs training data before any real examples exist. Rather than waiting to collect enough real data, teams generate representative synthetic data to unblock model development.
