For an artificial intelligence system to work well, data is often no less important than the algorithm. Data helps a model recognize patterns, distinguish between different cases, and adjust how it makes predictions. Yet in many fields, real data is not easy to collect. Medical data may contain private information, images from factories are often controlled by businesses, and rare situations hardly ever appear in sufficient numbers in ordinary datasets.
Against this backdrop, synthetic data is increasingly being discussed as a way to supplement AI data sources. This is data created by computer models or simulation processes, rather than recorded directly from a person, device, or specific event in real life. Synthetic data can take the form of text, images, audio, video, tabular data, or records describing behavior.
What is noteworthy is that synthetic data does not simply mean fake data. Its value lies in recreating certain characteristics and relationships needed from the real world to serve a specific purpose. However, precisely because it is generated from models and initial assumptions, synthetic data always needs to be evaluated before being used to train, test, or operate AI systems.
How Is Synthetic Data Created?
There are many ways to create synthetic data. At a basic level, developers can establish rules for generating new records. For example, a system can generate traffic scenarios based on speed, the distance between vehicles, lighting conditions, and the state of the road surface. This approach is suitable when the rules that need to be simulated are relatively clear.
For more complex problems, data can be generated by machine-learning models. A model is trained on real data to learn how the data is distributed, and then generates new samples that have a similar form but do not necessarily match the original records. In image processing, this process can create different scenes, objects, or lighting conditions. In tabular data, it can generate simulated records that reproduce relationships between information fields.
Another approach is to simulate an environment. Developers recreate a digital space with rules governing physics, movement, and interaction, and then allow an AI system to observe many situations within that environment. This method is particularly useful when collecting real data is costly, dangerous, or too time-consuming. Even so, a simulated environment is meaningful only when it adequately represents the important variations of the outside world.
Why Are Businesses and Developers Interested?
The first benefit of synthetic data is its ability to supplement cases that are difficult to find in real data. An object-recognition system, for instance, may need to see an object from many angles, in low-light conditions, or partially obscured. If it relies only on naturally collected data, these situations may appear unevenly. Simulated data makes it possible to deliberately create additional cases needed to test the model’s capabilities.
Synthetic data can also help protect privacy. Instead of directly sharing records or images linked to individuals, an organization can create a new dataset that retains certain statistical characteristics needed for research. This does not mean that all synthetic data is automatically safe. If the data-generation process reveals information from the original records, or if someone can infer an individual’s identity from a combination of details, privacy risks still remain.
Controllability is another advantage. With real data, collectors often have to wait for events to occur and then clean and label the data. With synthetic data, they can specify certain desired conditions, create many variations, and repeat the process at a lower cost in some cases. This data is also useful during the initial testing stage, when a development team needs to evaluate an idea before investing in a large-scale data-collection system.
However, synthetic data should not be understood as always being cheaper or faster than real data. Building a data-generation model, designing a simulated environment, determining reasonable rules, and checking quality all require time, expertise, and resources. For problems where the simulation does not accurately reflect reality, the cost of correcting mistakes may exceed the initial benefits.
The Greatest Limitation Lies in the Gap with Reality
The fundamental risk of synthetic data is that a data-generation model can create only what it has been designed or trained to represent. If the initial data lacks a certain group of cases, the new data may continue to overlook that group. If the simulation rules are overly simplified, an AI system trained on that data may perform well in a testing environment but fail when it encounters a real-world situation.
This gap is often broadly referred to as the gap between simulation and reality. An image created under perfect lighting conditions does not guarantee that a model will recognize objects well in rain, fog, or an obstructed environment. An artificial dataset may preserve average relationships while overlooking outliers that are highly important in decisions involving people.
Surface-level quality is also not enough to evaluate data. A data sample that appears reasonable to an observer may still contain relationships that are incorrect from a business-process perspective. For example, synthetic customer data may create information fields that are technically consistent but assign behaviors that do not fit real-world processes. Without someone knowledgeable about the field reviewing it, this error can easily pass through automated evaluation steps.
Another issue is the repetition of bias. When synthetic data is generated from real data that is already biased, the data-generation model may learn and reproduce that bias. If synthetic data is then used to create more new data, the initial biases may become more pronounced or harder to detect. Therefore, completely replacing real data with synthetic data is not a safe default choice.
When Should Real and Synthetic Data Be Combined?
In most cases, a reasonable approach is to combine the two types of data rather than treating them as mutually exclusive options. Real data helps keep a system grounded in actual conditions, while synthetic data helps expand the range of situations and fill gaps. The combination ratio should not be determined solely by a fixed number; it should be based on the objective, the field, and the level of risk associated with the application.
During development, synthetic data can be used to create prototypes, test boundary conditions, and identify errors in a model’s logic. The system should then be evaluated using real data that is independent of the data used for training. If performance is good on simulated data but drops sharply on real-world data, that is a sign that the model or the data-generation process needs to be reconsidered.
For systems that affect people’s rights and interests, evaluation requires even greater caution. It is not enough to measure average accuracy; the development team should examine results across different groups of people, operating conditions, and types of situations. Rare cases with serious consequences should also be tested separately, rather than being hidden by overall results.
Principles for Responsible Use
First, organizations need to clearly record how the data was generated, what assumptions it is based on, and what purpose it is intended to serve. A dataset may be suitable for testing software but unsuitable for training a system that makes important decisions. Documentation helps other teams understand the data’s limitations and avoid using it beyond its original scope.
Next, synthetic data needs to be checked for diversity, consistency, and representativeness. Technical specialists should work with people who understand the relevant business processes to identify unreasonable data patterns. When data involves privacy, organizations should also assess the risk of re-identification rather than relying solely on the label “synthetic.”
Finally, data should not be treated as an asset created once and used forever. Real-world environments change, user behavior changes, and new cases may arise. Good governance requires monitoring model performance after deployment, recording unexpected errors, and updating how data is generated and evaluated when necessary.
Conclusion
Synthetic data offers a flexible approach to AI development, especially when real data is scarce, sensitive, or difficult to collect. It can help create additional testing scenarios, support research, and reduce the direct use of certain types of personal data. But synthetic data is not a perfect copy of the real world, nor does it automatically eliminate bias or privacy risks.
The value of synthetic data depends on its objective, the method used to create it, and the quality of its validation. The most reliable approach is usually to treat it as a supplement to real data, combined with independent evaluation and human oversight. By understanding both the capabilities and limitations of this tool, organizations can make effective use of AI without mistaking plausibility on a screen for accuracy in the real world.
