AI glossary
Synthetic Data
Synthetic data is artificially generated data, created by a computer rather than collected from real-world events, designed to have statistical properties similar to real data so it can be used in place of, or alongside, real data for training or testing a model.
Teams increasingly turn to synthetic data generation to solve specific bottlenecks in the data lifecycle. Instead of waiting for physical sensors to record rare events or paying for expensive labeling services, engineers can produce datasets on demand. This approach accelerates the development of machine learning models by providing large, clean, and perfectly labeled datasets when real-world data is insufficient.
Why teams generate synthetic data
Organizations generate synthetic data primarily when real data is too scarce, too expensive, or too slow to collect. In many domains, capturing enough real-world examples to train an accurate model takes years. Synthetic data solves this by allowing teams to scale their datasets instantly.
Another common driver is data imbalance. Models often struggle to recognize rare events because the training set is dominated by common occurrences. By generating synthetic examples of these rare events, teams can balance their datasets and improve model accuracy for edge cases.
Privacy is also a major factor. When dealing with sensitive information, such as health records or financial transactions, sharing real data poses a risk. Synthetic data provides a way to share or work with data that resembles a sensitive real dataset while reducing direct exposure of real individuals’ information. This utility supports broader data-protection goals, including those outlined in the GDPR.
How it’s generated
There are three primary methods for producing synthetic data, each suited to different technical requirements and available resources.
Simulation
This method generates data from a physics- or rules-based model of the real world. By coding the laws of physics or business rules into a software environment, developers can create realistic scenarios. For example, a flight simulator generates data based on aerodynamic principles rather than recording actual flights. This approach is highly controllable and ideal for domains where physical laws are well-understood.
Generative models
Teams often train generative models on existing real data to produce new, similar synthetic examples. These models learn the underlying distribution of the real dataset and then generate new records that follow the same patterns. This method is particularly effective for complex data types like images, text, or audio, where capturing the exact statistical nuance is more important than strict physical accuracy.
Statistical and rule-based methods
For simpler use cases, straightforward statistical methods or rule-based systems create new records with a desired distribution. These approaches are computationally cheap and easy to implement. They are useful when the goal is to test database performance or validate query logic without needing high-fidelity realism.
Real-world example: self-driving simulation
Self-driving car programs represent one of the most widely cited applications of synthetic data. Developing autonomous vehicles requires training models to recognize millions of driving scenarios. Relying solely on real-world test drives is inefficient because rare events, such as a pedestrian stepping into the road during a sudden downpour, occur infrequently.
To address this, companies run large numbers of simulated driving scenarios in software. They generate synthetic sensor and driving data that mimics the output of LiDAR, cameras, and radar. This process is far cheaper, faster, and safer than waiting for these situations to occur naturally during real-world testing. Engineers can simulate thousands of near-collisions in hours, allowing the model to learn from a diverse range of edge cases without the physical risk and logistical cost of real-world collection.
Synthetic data and privacy
The relationship between synthetic data and privacy is often misunderstood. Because well-designed synthetic data does not correspond to any single real person’s actual record, it offers a layer of abstraction. If you take a synthetic dataset and look at one record, you cannot identify the specific individual from whom that data was derived.
This makes synthetic data an attractive tool for sharing datasets with third-party vendors or researchers. It connects to broader data-protection goals because it reduces the risk of re-identification. However, it is not a perfect shield. If the generation process retains too much detail from the original data, it may be possible to infer information about the source population. Therefore, while it aids in compliance with regulations like GDPR, it should be viewed as a risk-reduction strategy rather than a complete guarantee of anonymity.
Limitations
Synthetic data is only as good as the process or model used to generate it. If that process fails to capture some real-world pattern or relationship, models trained on the synthetic data can inherit those same gaps. This is often referred to as the “reality gap.” A model might perform perfectly on synthetic test data but fail when deployed in the real world because the simulation missed a subtle variable, such as a specific lighting condition or a unique user behavior pattern.
Furthermore, synthetic data can amplify existing biases. If the real data used to train the generative model contains biases, the synthetic data will reflect those same biases. For instance, if a dataset used to generate synthetic employee performance records underrepresents a certain demographic, the synthetic data will continue to skew in that direction. Teams must actively audit for bias in the generation pipeline.
Finally, for genuinely novel or rare situations that the generating process itself doesn’t know about, synthetic data cannot substitute for eventually validating a model against real-world data. It is a powerful tool for initial training and data augmentation, but it does not eliminate the need for real-world validation. Techniques like federated learning can sometimes complement synthetic data by allowing models to learn from distributed real data without moving it, but they do not replace the need for understanding the limits of synthetic generation.
FAQ
Is synthetic data better than real data?
Neither is universally better. Real data is the ground truth of the physical world. Synthetic data is better when real data is too costly, slow, or scarce to obtain at scale. They are often used together, with synthetic data handling the bulk of training and real data validating the final model.
Can synthetic data be used for machine learning?
Yes, synthetic data is widely used in machine learning for training models. It is particularly useful for data augmentation to broaden what a model has seen and for testing edge cases that are rare in real-world datasets.
Does synthetic data eliminate privacy risks?
It reduces them significantly but does not eliminate them. Well-designed synthetic data does not map to a single real person, but sophisticated analysis can sometimes infer information about the source population. It is a tool for privacy enhancement, not a total guarantee.