Synthetic data for research: how is it used?
8 min read
Synthetic data for research is still underexplored and, at the same time, surrounded by doubts. Can it be trusted? Is it safe? Does the quality really keep up?
The truth is that this topic has been gaining ground precisely because it addresses real daily challenges for researchers. And understanding how it works can open new possibilities, with more agility and fewer barriers. Shall we talk about it?
What is synthetic data for research?
Synthetic data for market research is artificially generated information that aims to reproduce the characteristics of real data, maintaining patterns and behaviors without exposing sensitive data.
According to the World Economic Forum, “it is artificially generated data to mimic the statistical properties, structure, and distribution of real-world data. It can fill data gaps, protect privacy, and enable the testing of new scenarios, providing a scalable and cost-effective alternative when real-world data is limited or sensitive.”
This means that this data allows testing scenarios, validating hypotheses, and working with information more securely and agilely, especially when access to real data is limited or involves privacy issues.
How is synthetic data generated?
If the purpose of synthetic data is to reproduce the behavior of real data, its generation starts from a simple principle: learn from existing data and, from that, create new information that follows the same patterns without copying real records.
It all starts with a reference base
This can be formed by anonymized real data or by reliable bases that accurately represent the scenario to be studied. It is this base that shows how data behaves in practice, revealing patterns, relationships between variables, and response distributions.
Artificial intelligence models
From there, artificial intelligence and statistical models come into play. They analyze the data structure, understanding how different profiles behave, which responses usually appear together, and how variations occur. With this learning, they start generating new data that follows the same logic.
The most important point is that this data is not copies. They are new combinations, created based on learned patterns. This allows preserving data behavior without exposing real information, which is especially relevant when it comes to privacy.
Generating data
There are different ways to do this generation. Simpler statistical models work with probabilities and distributions, while machine learning techniques can capture more complex relationships. Generative AI, on the other hand, allows simulating more advanced scenarios, such as open-ended responses and interactions, bringing synthetic data even closer to reality.
Data validation
After generation, there is still an essential step: validation. It is necessary to ensure that the data makes sense, maintains the expected patterns, and does not introduce distortions into the analysis. Without this, its use can compromise research results.
As you can see, the quality of synthetic data is directly linked to the quality of the base used at the beginning. The more consistent and representative this base is, the more reliable the generated data will be. This is how synthetic data can function as a reflection of reality, without directly depending on it.
What are the benefits of using synthetic data in research?
They do not replace real data, but they expand what you can do with it. Below are the main practical benefits:
More agility in the process
With synthetic data, you don't have to wait for all the collection to start analyzing. You can test paths, validate hypotheses, and make decisions faster.
Cost reduction
Collecting real data can be expensive, especially for specific audiences. Synthetic data helps reduce this dependence, optimizing investment without stopping the research.
Greater data protection
As they do not represent real people, synthetic data reduces privacy-related risks and helps work within legal requirements with more peace of mind.
Possibility to test scenarios
Want to validate an idea before launching it? Synthetic data allows simulating situations, testing variables, and understanding impacts without real risk.
Access to difficult audiences
When the audience is rare, dispersed, or difficult to recruit, synthetic data helps approximate this scenario and enable analysis.
More control over the research
You can adjust variables, create scenarios, and explore hypotheses with more freedom, something that is not always possible with real data.
Support in sample construction
They help complement and balance samples, especially when there is low incidence or underrepresented groups.
Scalability
It is possible to generate large volumes of data in a short time, which facilitates more robust analyses and broader tests.
Improvement in planning quality
Even before collection, synthetic data helps to better structure the research, identify flaws, and avoid rework.
What is researchers' opinion on synthetic data?
The adoption of synthetic data still divides opinions in the market, and this shows that the topic is in a transition phase. A survey by Rival Technologies indicates a balanced scenario, but with some caution:
- 27% of researchers are excited about the use of synthetic data
- 30% are indifferent
- 43% are not yet excited
This snapshot reveals an important point: despite the potential, there are still relevant doubts about quality, reliability, and practical application. Many professionals recognize the benefits, such as scalability and data protection, but still question to what extent synthetic data can replace or complement real data without losing accuracy.
On the other hand, the group that shows interest usually sees synthetic data as a strategic tool, especially for quick tests, simulations, and initial research phases.
How to use synthetic data in research?
If before synthetic data seemed distant, today it is starting to become part of the routine of researchers. And it is not to replace real data, but to help, complement, and, mainly, unlock stages that usually hinder the process.
Below are some practical ways to use it daily:
When your sample doesn't close
You know when you need a very specific audience and they simply don't appear in the necessary quantity?
Synthetic data comes in precisely there. It helps complete the sampling with simulated profiles that follow the same pattern as real data, enough for you to move forward more securely.
When the research is not well representing the audience
Not all groups always appear as they should in the sample. And this can distort the result. With synthetic data, you can better balance these groups and make the analysis closer to the reality you want to understand.
Before going to the field
Instead of discovering problems only after the research has run, you can test beforehand. With synthetic data, you can simulate responses, validate the questionnaire, and adjust what is necessary. It's a simple way to avoid rework.
When the data is too sensitive
Some research runs into privacy issues. And rightly so. In this case, synthetic data allows working with patterns and behaviors without exposing anyone. You continue analyzing, but with more security.
To test scenarios without risk
Want to understand how a change can impact behavior? Or test an idea before launching? Synthetic data helps simulate these scenarios. You test, learn, and only then decide the next step.
To explore very specific niches
The more niche the audience, the more difficult (and expensive) the collection becomes. Today there are solutions that allow generating synthetic respondents with a high level of precision, helping to analyze these segments in more depth.
In support of qualitative research
Even in more exploratory research, synthetic data has a place. You can simulate interviews, interactions, and even complete journeys. It doesn't replace real conversation, but it helps test paths and gain repertoire beforehand.
With automated research agents
There are already systems that ask questions, adapt the conversation, and explore responses dynamically. These agents function as scalable support, especially when you need to explore many scenarios at once.
Creating personas to test ideas
Another practical way is to create synthetic personas. They simulate real profiles and help you understand how different audiences would react to a product, campaign, or experience.
To better organize the research
Not everything is analysis. Planning also counts a lot. The data helps structure the study, test flows, and validate hypotheses before putting everything into practice.
How to use synthetic data safely?
To ensure they truly bring value, without compromising quality or privacy, some good practices make all the difference. Check it out:
Start with a reliable base
Everything starts with the base you use to train or guide the generation. If the source data is biased, incomplete, or unrepresentative, synthetic data tends to reproduce these same problems. Security is also about quality.
Avoid any possibility of re-identification
Even being artificial, synthetic data needs to ensure that it is not possible to track or reconstruct information from real people. This involves validating whether there are no very specific patterns or unique combinations that could indirectly expose someone.
Validate before use
Just because the data has been generated doesn't mean it's ready for analysis. Review consistency, coherence, and variable behavior. They need to make sense within the research context, otherwise, they can lead to wrong conclusions.
Use as a complement, not a total substitute
Synthetics work best when used alongside real data. They help test, simulate, and expand analyses, but critical decisions should still consider data collected directly from people.
Synthetic or human data?
They accelerate tests, allow simulating scenarios, and reduce barriers when access to real data is limited. In many research stages, they are valuable support for gaining agility and better organizing the path.
But, in the end, when the goal is to understand behavior, opinion, and decision, human data is still the main reference. They bring context, nuance, and what cannot be predicted with models alone. It is in contact with real people that research is confirmed.
And this doesn't have to be complicated. Today, there are simpler ways to access qualified respondents, with segmentation and scale. Respondent panels help precisely connect your research with the right audience, quickly and reliably.
If the idea is to move from simulation and validate with those who really matter, it's worth checking out PainelTap and starting your research with real data, without complications. Contact our team!