Can Synthetic Data Replace Real Survey Data?

Picture this: on a Tuesday, a client emails asking how environmentally conscious millennials across North America would respond to three new campaign concepts — and wants an answer by Friday. Running a fresh survey would take weeks, and there's no time for that. Someone suggests generating the responses synthetically instead, and within minutes, crisp, seemingly credible answers are ready for all three concepts. But just before sending the recommendation to the client, doubt creeps in: are these numbers actually accurate? Where did they come from?

Synthetic data generation is the process of using AI to create artificial datasets that mimic real-world data. Instead of collecting fresh responses from real people, the model produces a dataset that looks and behaves like the real thing. For research teams, this usually takes the form of a simulated audience: ask how runners aged 25 to 34 in Germany would react to a new sports drink, and the model responds as if they had actually been surveyed.

But quality here depends entirely on the generation method. In the weakest case, a general-purpose language model simply guesses — ask it about your audience, and it produces an approximate portrait that sounds convincing because it draws on whatever it absorbed from the internet during training, yet may bear no real connection to your actual customers. A step up, some synthetic data is built through web scraping or behavioral prediction — closer to reality, but still an interpretation one step removed from the truth: someone who keeps browsing running shoes might simply be shopping for a gift, yet the model may tag them as an "active runner."

The most reliable approach is to ground the simulation in real survey responses — genuine answers about what people actually think and do. The question posed may be new, but the underlying evidence isn't: the model forecasts the answer by combining patterns real consumers have already voiced. That's why it helps to picture survey data as the "foundation" and synthetic data as "the building constructed on top of it" — synthetic data can model answers to new questions in seconds, but it only works well when the foundation beneath it is solid.

Used correctly, simulated data brings several concrete advantages to a research toolkit. First, speed: it delivers answers while there's still time to act on them, rather than after a decision has already been made. Second, the ability to test more ideas at once — with five different product concepts, instead of running costly full-scale research on each, a team can first use a simulated audience to identify the most promising ones, then commit real budget and time to the strongest contenders. Third, reach: where directly surveying small or scattered groups is expensive and slow, a simulation grounded in that same group's real responses can yield additional insight into it — though this is exactly where caution is needed, since the less real data exists for a group, the more carefully its simulated answers should be treated.

Even so, real surveys remain the foundation of all this work. Simulation delivers its best results not as a replacement for genuine research but as a tool that extends it — helping teams extract more value from research already conducted, and saving time and budget in the early stages of testing an idea. High-stakes or entirely new questions still call for primary research.