Synthetic data: definition, value and limits
Definition
Synthetic data is artificially generated data that mirrors the statistical properties of real data without directly representing real people or events. It is produced through models or rules and serves as a substitute or supplement when real data is scarce, sensitive or imbalanced. In practice it usually supplements real data rather than replacing it entirely.
Why the Term Matters
Missing or sensitive data is a common reason an AI initiative never starts. Synthetic data can close that gap, for instance to test a model before production data is even available. As a neutral advisor we care about the term because it goes beyond a pure data protection question: it helps decide whether a use case is feasible and at what cost. Framing this early avoids expensive detours.
When It Is Relevant
- Real data exists but cannot be used directly for data protection reasons.
- Rare cases are missing from the dataset and need to be created for testing.
- A prototype should be built before access to production data is settled.
- Several teams need realistic test data without touching the originals.
When Not
When enough clean, usable real data exists, synthetic generation is usually needless effort. And where the real distribution is poorly understood, synthetic data often just carries those gaps forward. Then an honest review of the data situation helps first, not an extra generation step.
Industries That Use It
- Banks and insurers: create realistic test data for models without carrying real customer data into test and development environments.
- Medical technology: supplement rare conditions for testing where real cases are scarce and heavily protected.
- Automotive suppliers: generate sensor data for rare operating states that seldom occur in the field.
- E-commerce: mirror order and usage patterns to test recommendations before real histories exist.
Typical Use Cases
- A data-protection-safe test dataset for development, derived from production data.
- Filling underrepresented classes so a model learns rare cases too.
- An early prototype that shows feasibility before production access is settled.
- Load tests with large volumes that are realistic but not personal.
- Data sharing with external partners without handing over real customer data.
Decision Guide
| Signal | Synthetic data fits | Alternative |
|---|---|---|
| Real data sensitive, access hard | yes, as a de-risked substitute | check anonymising real data |
| Rare cases missing | supplement deliberately | collect more real data |
| Distribution barely understood | rather not | clarify the data situation first |
| Enough real data available | rarely needed | work with real data directly |
Example
An insurer wants to test a model but may not put customer data into the development environment. In a potential analysis we frame which fields are sensitive and recommend a synthetic test dataset for the early phase, while production access is clarified in parallel. This describes the method, not an asserted client metric.
Service Connection
Whether synthetic data makes sense for your initiative is something we clarify in the AI readiness check or the AI potential analysis. For personal data, EU AI Act consulting belongs with it.
Sources
This framing describes common practice in handling training and test data. Statements on data protection and the EU AI Act are general orientation and not legal advice; they must be checked case by case against the currently applicable legal texts.