What synthetic data is
Synthetic data is created rather than collected: text written by a language model, images rendered in a simulator, or tables generated to match the statistics of a real dataset. It is used to fill gaps, balance rare classes and share data without exposing real people.
Where synthetic data falls short
- No new knowledge. A generator can’t invent the details of how a real engineer, clinician or support team solved a problem it has never seen.
- Model collapse. Research has shown that models trained mostly on generated output lose diversity over generations.
- Hidden errors. Plausible but wrong examples are hard to spot at scale.
Why real records stay valuable
Developers use real business records as seeds for synthetic pipelines, as filters to reject unrealistic examples, and as test sets that show whether a model works in practice. The more synthetic data the industry produces, the more a verified real record is worth. See what data AI companies buy.
What this means for your business
If you hold years of genuine records of skilled work, synthetic data makes them scarcer, not obsolete. Calto Data’s free two-minute check tells you whether they fit what buyers ask for, and this guide covers what drives price.
Frequently asked questions
Is synthetic data cheaper than real data?
To produce, yes. But it still needs real data to generate and check it, and models trained only on synthetic data tend to underperform on real tasks.
Can synthetic data solve privacy problems?
It can reduce them, but poorly made synthetic data can still leak details of the real records it was based on.
Will synthetic data make my company’s data worthless?
Unlikely. It raises the value of real, verified records that capture how work is actually done.