Synthetic Data and When It Helps
Synthetic data is artificially generated information that mimics real data. It can fill gaps, protect privacy and speed up testing — but it is no free lunch.
This article explains where it helps and where it misleads.
Why Use It
- Test systems without exposing real personal data.
- Create examples of rare cases that are hard to collect.
- Balance datasets that under-represent some groups.
- Share data-like material safely with partners.
The Risks
Synthetic data only reflects the patterns it was built from. If those are wrong or incomplete, a model trained on it learns a distorted view of reality and performs poorly on genuine cases.
Using It Sensibly
- Use it to supplement, not replace, real data.
- Validate models against genuine examples.
- Be transparent about where it was used.
Frequently Asked Questions
Can I train entirely on synthetic data?
Rarely advisable. It is best as a supplement, with real data used to validate that the model works in practice.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.