Why Synthetic
Real data carries privacy risk in test environments. Teams need realistic volumes and distributions to test AI features. Synthetic data bridges — realistic patterns without identifiable records.
Generation Approaches
Rule-based (faker-style libraries — good for simple patterns, unrealistic for complex relationships). Statistical (sample real distributions, generate aligned). Model-based (train on real, generate new — careful about privacy leaks).
Tool Landscape
Tonic, Gretel, Mostly AI for enterprise synthetic data. Salesforce Data Mask for on-platform generation. Open source (SDV) for DIY. Pick based on budget and compliance requirements.
AI Testing Specifics
Need synthetic conversations, not just records. Generate realistic customer queries, support cases, email threads. Volume matters — a few hundred doesn’t test rare-intent handling. Plan for thousands.