Independent, auditable validation methodology mapped to Gartner's five criteria for synthetic data quality. Published methodology – not just claimed accuracy.
Most synthetic data vendors claim accuracy figures without publishing their methodology. This creates trust gaps that undermine analytical confidence.
"12x accuracy" headlines with no published methodology or validation criteria. How was this measured? Against what benchmark? These questions remain unanswered.
Vendors grading their own work using proprietary, undisclosed scoring systems. No independent audit trail means no accountability.
Data generated without transparency into how statistical properties are preserved, validated, or verified against source distributions.
Accuracy claimed but never demonstrated against real-world outcomes. Synthetic data should predict as well as original data – this must be proven, not assumed.
Each dimension is scored 0–100. The composite PRISM Quality Score is a weighted average. Above 80 indicates excellent quality; 60–80 is good; below 60 requires parameter adjustment or additional source data.
Measures how closely the synthetic variable distributions match the original source data. Checks that the generated data reproduces the statistical shape of every variable – not just the mean.
Measures whether the synthetic data preserves the diversity and range of the original. A high Richness score means the projection captures the full spread of responses – not a compressed or smoothed version.
Checks whether relationships between variables are maintained. Synthetic data with high Integrity is internally coherent – the logical patterns between variables that exist in the real data are preserved in the projection or imputation.
Measures the statistical confidence and analytical utility of the synthetic data. A high Strength score means the output provides genuine analytical power – not just structural similarity to the source.
Tests whether the synthetic data would produce similar analytical results if used for modelling or segmentation. This is the most practically important dimension – it answers the question: can I actually use this data for analysis?
Validated, auditable, governed. PRISM methodology – because claimed accuracy without published methodology is not validation.
Every synthetic projection receives a PRISM quality grade based on composite validation metrics.
Industry claims vs. the PRISM standard for synthetic data validation.
| Industry Claim | PRISM Standard |
|---|---|
| "12x accuracy" – no methodology published | Published validation criteria, auditable trail |
| Self-reported accuracy metrics | Mapped to Gartner's 5 validation requirements |
| Black-box projection process | Representativeness + distributional fidelity checks |
| Accuracy claimed, not demonstrated | Predictive validity tested against real outcomes |
| Single headline accuracy figure | Five-dimension quality assessment (PRISM) |
How the validation framework operates across the synthetic data lifecycle.
Every Knowsis synthetic projection includes full PRISM validation documentation – not just an accuracy claim, but an auditable methodology.
