During the development of an AI system, the model often receives more attention than the data behind it. However, the quality, scope, and management of the data are the factors that determine whether the system operates reliably. When real-world data is difficult to collect, contains sensitive information, or is not sufficiently representative of rare situations, synthetic data is becoming an approach of increasing interest.
Synthetic data is data generated by algorithms or simulation models rather than collected directly from a specific event or individual. It can consist of images of nonexistent objects, simulated transaction records, scripted conversations, simulated sensor data, or reconstructed edge cases for testing. The goal is not to create perfect copies of the world, but to provide additional appropriately structured material for training, evaluating, and improving AI systems.
Why real-world data is not always sufficient
Real-world data has a clear advantage because it reflects situations that have actually occurred. Even so, collecting and using this type of data often involves many limitations. A medical dataset may contain private information, transaction data may be linked to identities and financial habits, while image data from public environments may raise questions about consent and purpose of use. Therefore, not all data that can be collected can be immediately introduced into the training process.
Another issue is that important situations often do not appear frequently enough in historical data. A system that supports fault detection on a production line needs to recognize abnormal conditions as well, but serious faults may occur only rarely. A traffic-support tool needs to be tested under a wide range of weather, lighting, and traffic-density conditions, while waiting for those situations to arise in real life is both time-consuming and difficult to control.
Synthetic data allows development teams to proactively create missing cases. They can adjust certain variables, increase the frequency of rare situations, or construct controlled environments to observe how a model responds. This is why synthetic data is often viewed as a supplement to real-world data, rather than necessarily a complete replacement.
How synthetic data is generated
There are many ways to generate synthetic data, depending on the type of data and the project’s objectives. For structured data such as transaction tables, operational records, or sensor information, a system can learn the statistical relationships among fields and then create new records with similar characteristics. These records do not necessarily match any real individual or transaction, but they still preserve the relationships needed for testing.
In the fields of images and audio, data can be generated from simulated environments, transformation techniques, or generative models. For example, a team can build multiple versions with different lighting, viewpoints, backgrounds, and weather conditions to test a system’s recognition capabilities. For language processing, synthetic data can consist of conversations based on predetermined roles, purposes, and levels of ambiguity.
Another approach is to combine real data with simulated data. Real data provides context and characteristics close to everyday life, while synthetic data helps expand areas that are still lacking. This combination can be useful when real data is too scarce, too heavily skewed toward one user group, or does not yet cover unusual cases. Regardless of the method used, development teams still need to clearly document how the data was generated and what purposes it is intended to serve.
The benefits go beyond saving data
The most obvious benefit of synthetic data is reduced dependence on collecting sensitive data. In some projects, data can be generated for the initial development phase without bringing all personal information into the testing environment. This does not automatically eliminate every privacy risk, but it can help reduce the amount of real data that must be copied, shared, or stored during product development.
Synthetic data also makes testing more intentional. Instead of evaluating a model mainly with existing samples, a development team can create a test set focused on anticipated weaknesses. They can ask questions such as how the system responds when an image is obstructed, when a question is phrased unusually, when a signal is noisy, or when multiple adverse conditions occur at the same time.
This degree of control is also valuable when reproducing errors. If an error occurs randomly in a real-world environment, recreating the exact circumstances can be very difficult. A well-designed simulated environment makes it possible to change each factor and observe the causes of the model’s failure. As a result, synthetic data can support not only training but also diagnosis, version comparison, and verification of bug fixes.
Risks of using machine-generated data
Synthetic data is not inherently trustworthy simply because it does not contain a real person’s record. If the model that generates the data learns from a biased source, its output may reproduce or amplify that bias. When the original data overrepresents one group and underrepresents another, synthetic data generated from it may create an impression of richness while still lacking important perspectives.
Surface-level quality can also be misleading. An image that looks natural or an answer that seems coherent does not necessarily reflect the actual distribution of real-world data. In structured data, the relationships among different fields may be generated incorrectly. In language data, the wording may be grammatically correct but fail to reflect how people actually express themselves in a particular context. Without verification, development teams can easily train models on attractive-looking samples that have little operational value.
Another major risk is a self-reinforcing loop. When machine-generated data continues to be used to train systems that generate new data, small biases can accumulate over multiple cycles. Rare patterns or less common ways of phrasing things may gradually disappear, while familiar patterns are repeated too frequently. Therefore, synthetic data needs to be treated as a source with a specific provenance, not as an infinite source that can be used without supervision.
Evaluating synthetic data should begin with the objective
There is no single criterion for determining whether synthetic data is good or bad. A dataset suitable for robustness testing may not be suitable for training a model in a real-world environment. Before generating data, the team needs to clearly define the objective: whether it wants to expand the range of cases, protect privacy, test a specific error, or simulate an operational process.
For training purposes, it is necessary to consider whether synthetic data helps the model learn the required characteristics. For evaluation purposes, what matters is that the test set be sufficiently independent of the data used to develop the model. If the same data-generation process is used for both training and evaluation without controls, the results may be more optimistic than the system’s actual capabilities.
Evaluation should combine multiple perspectives. Teams can compare statistical characteristics between synthetic data and reference data, check for the presence of important groups or cases, and ask subject-matter experts to review representative samples. In sensitive fields, they also need to assess the risk that synthetic data could reveal information from its source or inadvertently recreate an almost identical original record.
Transparency about provenance is a condition for responsible use
Each synthetic dataset should be accompanied by information about the reference data source, the generation method, the variables that were adjusted, the scope of use, and known limitations. This documentation helps subsequent users understand the conditions under which the data was generated, what questions it can be used to answer, and what it should not be used for. Without provenance information, identifying the cause of an incorrect result becomes more difficult.
It is also necessary to distinguish between reducing privacy risks and guaranteeing absolute privacy. Synthetic data can limit the direct sharing of personal data, but the effectiveness of the protection depends on the generation method and the extent of testing. A dataset generated from a source that is too small or too specific may still contain clues that allow others to infer information about the original data.
Within an organization, management responsibility should not belong solely to the technical team. Legal, information security, subject-matter experts, and product owners should all participate to an appropriate extent. They can raise questions about the purpose of use, retention periods, access rights, criteria for removing data, and how to provide notice when a system has been built in part from synthetic data.
The practical outlook for synthetic data
Synthetic data will likely develop alongside real-world data rather than completely replacing it. Real data helps maintain a connection with the real world, while synthetic data makes it possible to expand scenarios, accelerate testing, and enable research in environments that are difficult to access. This relationship requires a continuous process: generating data, testing it, comparing it with reality, recording errors, and then making adjustments.
The most important thing is not to generate as much data as possible. Its value lies in whether the data serves the exact question the system needs to address. A small dataset with a clear purpose, independently evaluated and fully documented, can be more useful than a large data repository generated without adequate controls.
As AI is increasingly introduced into important processes, the ability to explain the provenance and limitations of data will become part of product quality. Synthetic data can help teams build safer and more flexible systems, but only when it is treated as a tool that requires verification. The convenience of generating data cannot replace the responsibility to understand the data, measure its impact, and know when it should not be used.
