Synthetic Data
Artificially generated data that mimics the statistical properties of real data, used to train or test models without exposing real (often sensitive) records.
Synthetic data generation lets a team produce training or test datasets that preserve the statistical patterns of a real dataset — useful when the real data is sensitive (PII, health, financial records), scarce, or imbalanced for the case being modeled. This category overlaps with, but is distinct from, data labeling/annotation services (which apply human or AI-assisted labels to existing real data rather than generating new data outright) — several vendors in this space offer both. Quality is judged on fidelity (does the synthetic data preserve the patterns models actually need to learn) and on privacy guarantees (can the synthetic data be re-identified back to source records), which vendors validate and disclose differently.
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