Synthetic data sounds like a way around every data problem. No privacy review, no waiting for production extracts, as many rows as you want.
In practice it is a tool with clear strengths and a specific failure mode. It can only contain patterns that the generator, or the person configuring it, already understood. Anything the real world does that you did not anticipate is missing, and that absence is invisible.
Where synthetic data earns its place
Software testing. Test environments need realistic records: names, addresses, account structures, transaction histories. Generated data lets teams build and test without copying production data into places it should not be.
Edge cases on demand. Real data may contain a handful of examples of an unusual document layout, a rare failure code or an extreme value. Generating more of them helps test that the system handles them at all.
Load and performance testing. Volume matters more than nuance here. Synthetic data scales easily.
Early development. Before access to real data is approved, synthetic samples let engineers build pipelines, schemas and interfaces.
Evaluating AI behaviour. Generated prompts, documents and adversarial inputs can extend an evaluation set, especially for safety and robustness checks.
Where it misleads
Training a model only on synthetic data. A model learns the generator’s view of the world. If the generator smooths away messy correlations, the model never sees them and fails on real inputs.
Measuring accuracy. Strong results on synthetic test sets often say more about how easy the generator made the task than about production performance.
Rare-event modelling. Fraud, defects and failures are rare because they are strange. Generating them from what you already know reinforces existing assumptions instead of revealing new patterns.
Bias and fairness work. If the source data under-represents a group, a generator trained on it will too, sometimes more so. Synthetic data does not fix bias by default.
Model collapse. Repeatedly training models on output from other models can narrow variety over generations. Keep real data in the loop.
Privacy is not automatic
Synthetic data is often presented as privacy-safe. That depends on how it was made.
Generators trained on real records can memorise and reproduce them, especially rare ones. Combining synthetic attributes can still make a real person identifiable when outliers carry through.
Treat privacy as something to test:
- Check for near-duplicates of real records in the output
- Pay attention to outliers and small groups
- Consider formal techniques such as differential privacy where the risk justifies it
- Involve your privacy team in deciding when synthetic data counts as non-personal
Validate against reality
Every synthetic dataset needs a comparison against real data, even if that comparison happens in a controlled environment.
Useful checks:
- Distributions of key fields and their correlations
- Business rules: totals that should reconcile, dates that should be in order
- Downstream task performance: train on synthetic, test on real, and compare with train on real, test on real
- Expert review of samples by people who know the domain
If a model trained on synthetic data performs well only on synthetic tests, you have learned something about the generator, not about the model.
A practical mix
The approach that tends to hold up in production:
- Use synthetic data freely for development, functional testing and load
- Use it to augment, not replace, real data for model training
- Keep a held-out set of real data as the final judge of quality
- Label synthetic records clearly in every dataset and pipeline
- Version the generator and its configuration like any other code
Where to start
- List the places you use or plan to use synthetic data and the purpose of each
- Separate testing uses from training and evaluation uses
- Define the real-data checks each synthetic dataset must pass
- Run a privacy review on any generator trained on personal data
- Make sure release decisions rest on real-world evaluation
Incubics helps teams design synthetic data pipelines that speed up delivery without hiding what production will actually throw at the system.