Glossary
Synthetic data
Synthetic data is training material generated rather than collected, used where real examples are too scarce, too sensitive or too expensive to label in the quantity a project needs.
In plain terms
Made-up examples, produced on purpose. If you need a thousand pictures of a rare fault and have eleven, or a thousand customer records you are not allowed to use, somebody generates material resembling the real thing and trains on that. It works better than it sounds and it inherits every blind spot of whatever produced it.
Why it matters
The scarce-example problem is a frequent reason a promising project stalls, and this is the standard answer to it. Rare faults, unusual claims and edge cases are by definition the ones you have least of and the ones you most need a system to handle. It also gives a route around material you cannot lawfully use for training, which for regulated organisations is often the difference between a project existing and not.
How it works
Material is produced by a model, by a simulation, or by transforming real examples into plausible variations, and which of those you use depends on the medium. Images tend to come from transformations and simulations; text more often from a model asked to produce examples in the style of the real ones.
It works best filling gaps rather than replacing the collection entirely. A modest quantity of real examples anchors the exercise, and generated variations extend the coverage into cases you have too few of. Systems trained wholly on generated material are the ones most likely to be excellent in tests and disappointing in use.
It inherits the limitations of whatever produced it, which is the central caution. Generated from a model, it carries that model's gaps and habits. Generated from a simulation, it carries every simplification the simulation makes. The system then learns those alongside the intended lesson and nobody notices until real material arrives.
The privacy claim needs care rather than acceptance. Material generated from real records can retain enough of them to be re-identifiable, so synthetic and anonymous are not the same word. Where the point of the exercise was avoiding an obligation, that has to be established properly rather than assumed from the label.
The two things it is asked to do
Seen in the wild
Ask an assistant to produce fifty realistic examples of a support enquiry you rarely receive, which is the text version of this at its simplest.
ChatGPTGenerate variations of an awkward document to test an extraction step, where the point is coverage of the difficult shapes rather than realism.
ClaudeBuild a step that produces test cases automatically, so a system gets exercised on the rare situations rather than only the common ones.
n8n
Common misconceptions
People assume
It is automatically private.
In fact
Material generated from real records can carry enough of them to be traced back, so synthetic and anonymous are different claims. Where avoiding an obligation was the purpose, that needs establishing properly, with advice where the obligation is a legal one, rather than being inferred from the word.
People assume
It can replace collecting real examples.
In fact
It extends a real collection far better than it substitutes for one. A system trained wholly on generated material learns whatever produced that material, including its blind spots, and the results are frequently excellent in testing and poor in use. Real examples anchor the exercise, even a modest number of them.
Telling them apart
Synthetic data vs Training data
Synthetic data
Generated deliberately for a purpose, in the quantity you need, with the gaps of whatever produced it.
Collected from the world, uneven, and carrying whatever the world happened to contain.
One has the gaps of its generator and the other has the gaps of reality. Neither is neutral, and knowing which kind you are holding tells you which gaps to look for.
Questions
- When is it the right answer?
- When real examples are genuinely scarce, when the cases you need most are the rarest, or when the material you have cannot lawfully be used for training. Those three cover most legitimate uses. Generating material because collecting it is tedious is the version that disappoints.
- How do we know it is any good?
- Hold back real examples, train on the generated material, and test against the real ones you kept. That is the only measurement that means anything, and it is worth doing before rather than after the project depends on the answer. Testing generated material against generated material tells you nothing.
- Does it satisfy our privacy obligations?
- Take that to whoever advises you on the obligation, and bring two specifics: what the generator was trained on, and whether any single real record can be matched to an output. Those are the two things such an assessment turns on, and neither is settled by the word synthetic appearing in a product description.
Key takeaways
- It is generated training material, used where real examples are scarce, sensitive or too costly to label.
- It works best extending a real collection rather than replacing one.
- It inherits the gaps of whatever produced it, and the system learns those alongside the intended lesson.
- Synthetic and anonymous are different claims, and the second needs establishing.
- The only meaningful test is against held-back real examples.
Last checked July 2026