Why Synthetic Data Still Needs Human Truth

AI has permanently changed the way teams work. We can move faster, scale further and explore questions that were previously out of reach. Synthetic data is often positioned as the next logical step.

The market research industry has navigated methodological shifts before. The move to synthetic data deserves the same scrutiny we gave every one of them.

The promise is compelling: research insights at speed and scale, without the friction of fieldwork, hard-to-reach quotas, or incentive costs. Synthetic data, proponents argue, can deliver all of this.

In some contexts, those claims hold up. In others, they’re dangerously overstated. Understanding the difference and when to use synthetic data is now one of the most important judgment calls in the insights industry.

The Industry Has Been Here Before

When research moved from telephone to online, the industry did not simply accept digital methods as equivalent to what came before. It tested, benchmarked, and established proof points that showed online data could be trusted, and where it could not.

That rigor mattered. It protected the validity of insights that businesses were using to make real decisions. Synthetic data deserves exactly the same scrutiny.

The difference today is pace. AI has dramatically accelerated what research teams can do: writing better questions, moderating at scale and identifying patterns faster. That is genuinely valuable. But speed is not the same as validity, and the two are increasingly conflated.

Not All “Synthetic” Is Created Equal

One of the biggest challenges in this conversation is that synthetic data means wildly different things depending on who you ask.

Some approaches build on carefully collected human data, using modeling to explore scenarios or scale insights responsibly.

Others generate entirely new datasets using secondary, legacy, or previously repurposed information, with no direct connection to the people the data is meant to represent or the decision it’s meant to inform.

Those differences matter.

When synthetic data is trained on data collected for a completely different purpose, risk compounds:

assumptions stack
context disappears
outputs feel precise but lack grounding
and models begin learning from other models

At that point, confidence can grow faster than accuracy.

Responsibility Doesn’t Disappear With Automation

No matter how advanced the model, someone is responsible for deciding that a synthetic dataset is fit for purpose. That decision cannot be delegated to the tool itself.

Making that call well requires understanding how the data was generated, what assumptions were embedded, and what the data cannot answer. It also means validating outputs against real-world truth points. That is true whether the researcher is a seasoned methodologist or a first-time user of an insights platform.

When research capabilities become accessible to people without formal training, including marketers, product managers, and strategists, the tools do not automatically come with the judgment to use them responsibly.

Democratization is powerful. But democratization without constraints increases risk.

Where Synthetic Does Make Sense

None of this means synthetic data has no place. In fact, there are clear scenarios where it can add real value:

Early-stage discovery and scenario exploration
Stress-testing ideas before committing to live research
Understanding how AI agents or recommendation systems behave, where humans aren’t the decision-makers
Supplementing human data when used transparently and responsibly

These use cases work best when synthetic data is treated as directional input and when it’s grounded in high-quality human data.

Human Insight Is Still the Baseline

At its core, market research exists to answer one enduring question:

Why do people think, feel, and act the way they do?

AI can scale the work. Synthetic data can extend it in thoughtful ways. But neither replaces human emotion, lived experience, or context. The most responsible path forward is building systems where:

Human data anchors insight
AI accelerates execution
Synthetic output is validated, contextualized, and transparent
Want to Explore the Full Pros, Cons, and Tradeoffs?

This topic sparked an in‑depth conversation between industry leaders on what synthetic data is capable of today and what it means for the future of insights.

Watch the full conversation to hear both perspectives, the real‑world use cases, and the open questions the industry still needs to answer.