COMPEL Glossary / synthetic-data
Synthetic Data
Synthetic data is artificially generated data that mimics the statistical properties of real data but does not contain actual individual records.
What this means in practice
It is created using algorithms that learn the patterns and distributions in real datasets and produce new data points that share those characteristics without revealing any specific real-world information. Synthetic data is valuable for AI training when real data is scarce (insufficient examples for rare events), sensitive (personal health or financial information), or biased (underrepresentation of certain groups). It can augment training datasets, enable privacy-preserving development, and help address fairness concerns. However, synthetic data governance is important: the generation process must be validated to ensure statistical fidelity, and synthetic data should be clearly labeled to prevent confusion with real data in governance processes.
Why it matters
Synthetic data addresses critical AI development constraints by enabling training when real data is scarce, sensitive, or biased. It can augment datasets, enable privacy-preserving development, and help address fairness concerns by supplementing underrepresented groups. However, synthetic data governance is essential to ensure statistical fidelity and prevent confusion with real data. Organizations that master synthetic data generation gain a significant competitive advantage in regulated industries.
How COMPEL uses it
Synthetic data generation is assessed within the Technology pillar as a privacy-preserving technique during the Model stage's data architecture design. During Calibrate, data gaps that synthetic data could address are identified. The Governance pillar requires validation of synthetic data fidelity and clear labeling to distinguish synthetic from real records. The Evaluate stage verifies that models trained on synthetic data perform appropriately on real-world inputs.
Related Terms
Other glossary terms mentioned in this entry's definition and context.