What synthetic data is, and where it actually helps ML teams

Synthetic data is often described as a replacement for real data. It works better as a tool for specific gaps in a training set: records you cannot share, cases you rarely see, and conditions you have never observed. This article covers what synthetic data is, how it is generated, where it pays off for ML teams, and where it does not.

A working definition

Synthetic data is data produced by a model or a process rather than collected directly from the world. It is designed to reproduce the statistical properties of a reference dataset, or of a domain in general, closely enough to be useful for a downstream task. For machine learning, that task is usually training, validating, or stress-testing a model.

The definition says nothing about any single synthetic row being "real". A synthetic customer record is not a disguised copy of a real customer. It is a sample from a learned distribution. That distinction matters for privacy, and it matters for how you judge quality: you evaluate synthetic data as a population, not record by record.

How it is generated

The generation method depends on the modality and on how much structure you need to preserve.

Tabular data. Common approaches include copula and Bayesian-network models, which capture marginal distributions and dependencies explicitly, and deep generative models such as conditional GANs, variational autoencoders, and diffusion models, which handle mixed data types and non-linear relationships. Multi-table data adds the need to preserve keys, cardinalities, and referential integrity.
Text and LLM training data. Large language models are prompted or fine-tuned to produce instructions, dialogues, documents, or labelled examples. The hard parts are diversity, factual consistency, and avoiding a narrow house style that the downstream model then learns.
Images and vision. Options range from rendered 3D scenes with exact ground-truth labels to diffusion-based generation and augmentation of real images. Rendering gives perfect annotations; learned generators give more realistic texture and variation.
Time series. Generators must preserve autocorrelation, seasonality, regime changes, and cross-series dependencies, not just the distribution of individual values.

Rule-based and simulation-based generation also counts. A physics simulator, an agent-based model of a payment network, or a scripted scenario engine can produce data that no learned model has seen, because it is driven by explicit rules rather than by a reference sample.

Where synthetic data actually helps

Access and sharing

Many ML projects stall before any modelling starts because the data cannot leave a controlled environment. Synthetic data trained on the sensitive source can let engineers build pipelines, prototype features, and run early experiments without broad access to the original records. It is also useful for sharing realistic data with vendors, contractors, or other internal teams. This benefit only holds if privacy is actually tested; generation alone is not a guarantee.

Class imbalance and rare events

Fraud, equipment failure, rare diagnoses, and safety-critical edge cases are underrepresented by nature. Conditional generation lets you produce more examples of the minority class or of specific feature combinations, so the model sees enough of them to learn a decision boundary. This works when the rare class has enough real examples to learn from; it does not create signal that is absent.

Coverage of conditions you have not observed

Simulation and controlled generation can produce scenarios outside the historical record: new product configurations, unusual sensor conditions, or input formats a model will meet after deployment. This is especially useful for test sets, where the goal is to probe behaviour rather than to match the training distribution.

Labelling cost

Generated data can come with labels attached. Rendered images carry exact segmentation masks. Generated text can be produced for a specified intent or class. That can reduce annotation work, provided the labels are checked for consistency with how real data would be labelled.

Testing and QA

Not every use is training. Synthetic data is well suited to populating staging environments, load testing, regression tests for data pipelines, and fixed evaluation suites that can be shared and versioned without exposing production records.

Synthetic data is most valuable when it fills a specific, named gap in your training set. It is least valuable when it is asked to stand in for data you do not understand.

Where it does not help

Knowing when not to use synthetic data is as important as knowing how to generate it.

When the real data is small and noisy. A generator trained on a few hundred rows will mostly reproduce those rows or smooth over their structure. You gain volume, not information.
When the signal lives in the tails. Generative models tend to under-represent extreme values and rare interactions unless explicitly conditioned or evaluated on them. If your model's value depends on those tails, check them directly.
When the reference data is biased. A generator learns the biases of its source. Rebalancing can correct some of this, but only for attributes you measure and control.
As a substitute for a real test set. Final evaluation should use held-out real data wherever possible. A model that performs well on synthetic data may simply be good at the generator's quirks.
For regulatory claims you have not tested. "Synthetic" does not automatically mean "anonymous". Memorization and re-identification risk must be measured, not assumed.

Fidelity, utility, and privacy

Every synthetic dataset involves a trade-off between three properties:

Fidelity: how closely the synthetic data matches the statistical properties of the reference data, including marginals, correlations, and higher-order structure.
Utility: how well the data serves the downstream task, usually measured by training a model on it and testing on real data.
Privacy: how little the synthetic data reveals about individual records in the source.

These pull against each other. Pushing fidelity to the limit risks copying training records. Adding strong privacy protections, such as differential privacy during training, usually reduces fidelity on fine-grained structure. The right balance depends on the use case: a dataset shared externally needs a different setting than one used to augment a model inside the same secure environment.

Mixing real and synthetic data

In practice, most teams get the best results by combining real and synthetic data rather than replacing one with the other. Common patterns include:

Training on real data augmented with synthetic examples of under-represented classes or conditions.
Pre-training or warm-starting on a larger synthetic set, then fine-tuning on real data.
Using synthetic data for development and pipeline work, then training the production model on real data inside a controlled environment.

The mixing ratio is a hyperparameter. Treat it like one: sweep it, evaluate each setting on a held-out real test set, and watch for the point at which adding more synthetic data stops helping or starts hurting.

A practical starting point

Before generating anything, write down the gap you are trying to close and how you will know it is closed. That usually means naming a target metric on real held-out data, the subgroups or conditions you care about, and the privacy threshold the data must meet. With those in place, synthetic data becomes an experiment you can measure rather than a bet.

If you are weighing synthetic data for a specific model or dataset, our product overview describes how SETC handles generation, control, and evaluation. If you would rather test it against your own data, you can book a demo.