What each one is
Real data is collected from the world: recordings of people speaking, photos taken on actual roads, transaction logs from a working business. Synthetic data is generated by a program, a simulation or a generative model to resemble the real thing.
They aren't rivals. Most sensible datasets are a mix, and the useful question is what proportion, and for which parts of the problem.
Where real data wins
Real data carries the mess your model will face in use. People mumble, lighting changes, a customer says something no script writer would think of. Nothing generated is guaranteed to include those surprises, because nobody knew to ask for them.
It's also the only honest test. A model has to be judged on data that came from the world it will work in.
Where synthetic data wins
Start with what you can't collect: patient records, financial histories and other sensitive material that can't be gathered or shared freely. Then rare events, like a hazard, a fault or an unusual request that shows up too seldom in real collections to train on.
It helps with balance, too. If one language, accent or group is thin in your real data, generated examples can even things out while collection catches up. And it can be faster. Generating data is sometimes quicker than arranging a collection, when the need is urgent and the target is clear.
Where synthetic data goes wrong
Generated data inherits the blind spots of whatever produced it. If the generator has never encountered a situation, its output won't contain it, and you won't find out until real users hit it.
It can also look convincing and still be subtly wrong: speech with the wrong rhythm, images with lighting no camera would produce, text that sounds fluent but isn't how anyone in your market writes. Human review catches a good share of this. Research on training models repeatedly on generated data has also shown that quality can degrade over successive generations, so it works best as a supplement to real data, not a replacement.
And synthetic doesn't automatically mean private. A dataset generated from real records can still leak details about them if the process is careless.
How to test a model that uses both
- Keep a test set made only of real data, collected separately from anything used in training.
- Break results down by language, accent, age group or whatever segments matter to your users. One average hides a lot.
- Compare performance on synthetic and real test data. A large gap means the synthetic data isn't like your real world yet.
- Check that generated examples didn't leak into the test set.
Our data and model evaluation work covers this kind of testing.
A simple way to decide
Start with real data wherever you can get it, then look at where it's thin, restricted or missing. Fill those gaps with synthetic data, review it, and check the result against real examples. If a gap can be filled with real collection at a reasonable cost, do that instead.
How we approach it
We offer both under one roof: data collection from a network of 30,000+ people, and synthetic data that people review for realism and quality. That lets you mix the two without handing data between vendors. Every project starts with an NDA, before any data changes hands.
Warning signs
Be cautious of a vendor who treats synthetic data as a drop-in replacement for real data, can't explain how generated examples get checked against real ones, or claims it removes bias or privacy risk on its own.