Synthetic data only helps regulated teams if it holds up under measurement. A practical evaluation stack—fidelity, utility, and privacy—lets data leads justify synthetic datasets for analytics and ML while quantifying leakage risk for compliance sign-off.
AWS lays out a three-part scorecard for synthetic data: fidelity, utility, privacy
AWS’s Machine Learning Blog published a framework for evaluating synthetic data quality through three dimensions: fidelity (how closely synthetic data matches the statistical properties of the source), utility (how well it supports downstream tasks like analytics and machine learning), and privacy (how much sensitive information might leak through similarity or record reproduction).
The post highlights concrete measurement approaches. For fidelity, it points to statistical comparisons such as histogram similarity and mutual information scores to test whether distributions and relationships are preserved. For utility, it emphasizes prediction scores that compare model performance when trained/evaluated on synthetic versus original data. For privacy, it calls out checks including an exact match score (whether any real records appear verbatim) and a neighbors’ privacy score (how close synthetic rows are to real rows), aimed at detecting memorization or near-duplicate leakage.
- Gives regulated teams an auditable acceptance test. Framing evaluation as fidelity/utility/privacy turns “is this synthetic data good?” into measurable gates you can document for internal risk reviews and external scrutiny.
- Prevents the common failure mode: high fidelity, low usability. Statistical similarity alone doesn’t guarantee models behave the same; utility testing via prediction scores forces task-level validation before synthetic data enters ML pipelines.
- Moves privacy from claims to quantification. Exact-match and nearest-neighbor style checks provide defensible evidence about leakage risk—useful when privacy, legal, and security teams need more than vendor assurances.
- Clarifies stakeholder ownership. Data science can own utility, data engineering can own fidelity diagnostics, and privacy/compliance can own leakage checks—making synthetic data reviews operational rather than ad hoc.