{"id":9004111222032167,"date":"2026-05-25T09:11:57","date_gmt":"2026-05-25T13:11:57","guid":{"rendered":"https:\/\/sago.com\/?p=9004111222032167"},"modified":"2026-05-25T09:13:18","modified_gmt":"2026-05-25T13:13:18","slug":"why-synthetic-data-still-needs-human-truth","status":"publish","type":"post","link":"https:\/\/sago.com\/es\/resources\/blog\/why-synthetic-data-still-needs-human-truth\/","title":{"rendered":"Why Synthetic Data Still Needs Human Truth"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text css=\u00bb.vc_custom_1779479266012{border-left-width: 6px !important;padding-left: 20px !important;border-left-style: solid !important;border-color: #6bc073 !important;}\u00bb css_params=\u00bb\u00bb]Key Takeaways:<\/p>\n

Not all synthetic data is the same. Methods built on real human data are fundamentally different from those generated without any direct connection to the people they represent.
\nSpeed and scale are not the same as validity. Without grounding in human truth, confidence can grow faster than accuracy and that is where poor decisions get made.
\nResponsibility belongs to the researcher, not the tool. Someone always has to decide if a dataset is fit for purpose, and that judgment cannot be delegated to the model.<\/p>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][\/vc_column][\/vc_row][vc_row el_id=\u00bbanchor1″ conditional_render=\u00bb%5B%7B%22value_role%22%3A%22administrator%22%7D%5D\u00bb css_params=\u00bb\u00bb][vc_column][vc_column_text css=\u00bb\u00bb css_params=\u00bb\u00bb]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.<\/p>\n

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

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.<\/p>\n

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.<\/p>\n

The Industry Has Been Here Before<\/p>\n

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.<\/p>\n

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

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.<\/p>\n

Not All \u201cSynthetic\u201d Is Created Equal<\/p>\n

One of the biggest challenges in this conversation is that synthetic data means wildly different things depending on who you ask.<\/p>\n

Some approaches build on carefully collected human data, using modeling to explore scenarios or scale insights responsibly.<\/p>\n

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\u2019s meant to inform.<\/p>\n

Those differences matter.<\/p>\n

When synthetic data is trained on data collected for a completely different purpose, risk compounds:<\/p>\n

assumptions stack
\ncontext disappears
\noutputs feel precise but lack grounding
\nand models begin learning from other models<\/p>\n

At that point, confidence can grow faster than accuracy.<\/p>\n

Responsibility Doesn’t Disappear With Automation<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Democratization is powerful. But democratization without constraints increases risk.<\/p>\n

Where Synthetic Does Make Sense<\/p>\n

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

Early-stage discovery and scenario exploration
\nStress-testing ideas before committing to live research
\nUnderstanding how AI agents or recommendation systems behave, where humans aren\u2019t the decision-makers
\nSupplementing human data when used transparently and responsibly<\/p>\n

These use cases work best when synthetic data is treated as directional input and when it\u2019s grounded in high-quality human data.<\/p>\n

Human Insight Is Still the Baseline<\/p>\n

At its core, market research exists to answer one enduring question:<\/p>\n

Why do people think, feel, and act the way they do?<\/p>\n

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:<\/p>\n

Human data anchors insight
\nAI accelerates execution
\nSynthetic output is validated, contextualized, and transparent
\nWant to Explore the Full Pros, Cons, and Tradeoffs?<\/p>\n

This topic sparked an in\u2011depth conversation between industry leaders on what synthetic data is capable of today and what it means for the future of insights.<\/p>\n

Watch the full conversation to hear both perspectives, the real\u2011world use cases, and the open questions the industry still needs to answer.[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_id=\u00bbanchor2″ conditional_render=\u00bb%5B%7B%22value_role%22%3A%22administrator%22%7D%5D\u00bb css_params=\u00bb\u00bb][vc_column][vc_column_text css=\u00bb\u00bb]