What is synthetic data and why does it matter to those who lead

There's a phrase I often hear in product and engineering meetings: "we have the data, but we can't use it." There is information about a client who cannot leave the protected environment. It is based on the fact that you cannot feed a model due to a contract. There is a rare scenario that almost does not appear in history. The result is a team standing still, waiting for a release that takes time or never comes.

Synthetic data attacks exactly this point. Instead of working with a person's real record, you generate an artificial record that behaves like the real thing without being the real thing. For those who lead data, AI or technology, it is worth understanding the concept well before treating it as a silver bullet or a fad.

What actually is synthetic data

Synthetic data is information created artificially to reproduce the statistical properties of a real data set. The central idea is not to copy records, but to copy behavior: distributions, correlations between variables, frequency patterns, common cases and edge cases.

Think about a financial transaction basis. The real data has the customer's CPF, the value, the date, the establishment. A synthetic version of this base has customers that don't exist, transactions that never happened, but that, in aggregate, are distributed like the true set: the average ticket matches, seasonality appears, the proportion of fraud remains similar.

There is a spectrum of techniques. On the simpler side, you generate data by rules: define ranges, formats, and probabilities, and produce records that respect those rules. On the more sophisticated side, you train a generative model on the real base and ask it to produce new samples that follow the same learned structure. Between one extreme and the other there are several approaches, each with its cost and fidelity.

The point that interests the manager is not which algorithm is at the bottom. It's the property it delivers: usable data that carries the pattern without carrying the person.

How they differ from the real data

The most obvious difference is the origin. The real data was observed: someone clicked, purchased, filled out a form, was served. The synthetic data was generated by a process that attempts to mimic this observation.

This gives rise to three practical differences that matter for the decision.

The first is the relationship with identifiable people. Real data, even "anonymized", often still allows re-identification when crossed with other sources. Well-constructed synthetic data does not correspond to any specific individual, which changes the conversation with legal, compliance and the data protection authority.

The second is control. With real data, you are hostage to what the world has produced: if a scenario is rare, it is rare in its base. With synthetic data, you can ask for more examples of a specific case, balance an underrepresented class, or simulate a situation that hasn't happened yet.

The third is fidelity, and here lies care. Real data is true by definition, it happened. Synthetic data is an approximation. It is only as good as the process that generated it and as limited as the patterns it was able to capture. What the generating model did not see, it does not reproduce. What he saw skewed, he reproduces skewed.

Why this became a central theme

Two movements pushed synthetic data from the margins to the center of the technical agenda.

The first is privacy. With the LGPD in Brazil and equivalent regimes abroad, the cost of handling personal data has risen. Each copy from a production base to a test environment is an exposure. Each access by a data scientist to sensitive information is a risk to be justified. Synthetic data offers a way to work without moving real personal data back and forth, and I cover this use in depth in Synthetic Data as a Privacy Tool.

The second is scarcity. AI models are data hungry, and there is not always enough volume, with the right quality and labeling, for the problem you want to solve. Regulated sectors, rare cases, specific languages and contexts, all of this comes up against a lack of good data. Synthetic data helps fill this gap, with caveats worth knowing before trusting.

For the public sector, the combination is even more sensitive. Agencies deal with health, assistance, security and tax data, all of which are highly protected, and at the same time they need to innovate, test systems and train models. Synthetic data opens up space for experimentation without turning each pilot project into a risk of citizen information leakage.

Where they deliver value in practice

It is worth separating the uses, because they have different maturities and risks.

Training and improving models is the most talked about use. When real or labeled examples are missing, synthetic data complements the base and can improve performance. This is also where a specific risk lies, that of the model learning from its own imitation and degrading.

Testing systems and generating revenue for QA is perhaps the most underrated use and the fastest payback. Instead of copying the production base for approval, you generate a realistic mass that exercises the same code paths without exposing a single real customer.

Simulating scenarios is the most strategic use. You can generate a spike in demand, a new fraud, a system failure, and see how your product reacts before the situation actually happens.

Protecting privacy cuts across all of the above. It's less of an isolated use case and more of a property you gain when you replace real data with synthetic data at the steps where personal data doesn't need to be.

What a leader needs to demand

Synthetic data is not magic and does not dispense with governance. Before adopting, align three things with the team.

Define the generation objective. Data for model training, for system testing and for business analysis have different fidelity requirements. Generating well for one purpose does not guarantee that it will serve another.

Demand validation against reality. Synthetic that has not been compared with the true basis is faith, not data. The team needs to measure how close the synthetic distribution is to the real one and how much it leaks information from the original.

Treat privacy as a requirement, not a bonus. Poorly generated data can reproduce real records almost intact. The promise of privacy is only valid if it is tested.

Synthetic data is a powerful tool when it comes with method: clear objective, serious validation and surrounding governance. Treated like this, they stop being a fad and become a real lever to move projects that are currently blocked due to a lack of usable data.