We build synthetic data generation tools, so we have an obvious interest in you using synthetic data. That's exactly why it's worth being direct about where it helps and where it doesn't, rather than overselling a category that, used wrong, will underdeliver and leave teams skeptical.
Synthetic data solves three specific problems well. It handles almost nothing else better than real data does. Knowing the difference upfront saves a lot of wasted effort.
Where Synthetic Data Works: The Three Problems It Solves
1. Privacy constraints that block access to real data
This is the clearest use case and the one that drives most of the interest in synthetic data. Your team needs tabular records for model training. The records contain fields covered by HIPAA, GDPR data minimization, financial privacy rules, or internal access controls that would require months of legal review to navigate. The real data is off-limits or practically inaccessible.
Synthetic data generated from the real distribution can unblock the training pipeline without the real records ever leaving the secured environment. The statistical structure crosses the boundary; the records don't. For development, feature engineering, and architecture experiments, a high-fidelity synthetic dataset will get you most of the way to what you'd learn from real data, without the compliance overhead.
The key word is high-fidelity. Not all synthetic data is equivalent. If fidelity is poor, you're not getting a privacy-preserving substitute for real data, you're getting a different dataset that happens to have the same column names. Measuring fidelity before training is essential.
2. Class imbalance that makes minority class patterns hard to learn
When 97% of your data is majority class and 3% is minority, a model trained without oversampling will often learn to predict the majority class almost exclusively. Standard techniques like SMOTE generate synthetic minority class samples, but do so in feature space only, without preserving the joint distribution and schema constraints of the original data.
Probabilistic synthesis of additional minority class rows, drawing from a generative model fit on the real minority class distribution, produces samples that preserve inter-column relationships and satisfy data constraints. For datasets where the minority class has meaningful feature interactions (fraud, rare disease, anomalous events), this approach typically outperforms SMOTE-based oversampling on downstream model metrics.
This use case doesn't require privacy protection as a motivation. Even teams with full access to real data use synthetic oversampling to address class imbalance. The privacy benefit is a bonus if the underlying data is sensitive.
3. Data scarcity in development and testing environments
Production data is often unavailable in dev/test for reasons that have nothing to do with privacy: environment separation, cost of data replication, latency of data pipelines, or the simple fact that a new product doesn't have historical data yet. Engineers writing integration tests against a payments service need realistic transaction records. A new team building a churn model needs training data before they have a year of customer history.
Synthetic data generated from a known distribution fills this gap. It doesn't need to be perfect for dev/test purposes. It needs to be realistic enough that code behaves consistently with production behavior: column types are correct, value ranges are plausible, foreign key relationships hold, edge cases are represented. Schema-preserving synthetic generation handles all of these.
Where Synthetic Data Doesn't Help
Production model evaluation
Your final model evaluation before production deployment must use real held-out data. Full stop. Evaluating on synthetic test data tells you how well the model learned the synthetic distribution, not how well it generalizes to real production data. These can be significantly different, especially for models sensitive to distribution shift.
We're not saying synthetic evaluation is useless for early-phase iteration. Running TSTR (train on synthetic, test on real) benchmarks during development is exactly the right way to track whether your synthetic data quality is sufficient for the training task. But the final accuracy number that determines whether you deploy should come from real data.
Replacing real data when you have access to real data
If your team has clean, accessible real training data with no compliance constraints, training on that real data will produce a better model than training on synthetic data derived from it. Synthesis introduces a generation step that adds some error, however small. You only pay that accuracy cost if you have a reason to.
The motivation for synthetic data is always removing a constraint: privacy restriction, class imbalance, data scarcity. If none of those constraints apply, using synthetic data as a substitute for real data you have access to is solving a problem that doesn't exist.
Capturing distributional properties that don't exist in the source data
Synthetic data generates new records that resemble existing records. It cannot generate data representing scenarios that don't appear in the source distribution at all. If your real data has no examples of a particular fraud pattern because it hasn't appeared in production yet, synthesis won't produce examples of it. If your real data has no records from customers above age 75 because your product doesn't have that user segment, synthesis won't produce those either.
For edge cases and out-of-distribution scenarios, you need domain expert input, not synthesis. This is a fundamental property of distribution-matching synthesis, not a fixable limitation of any particular implementation.
The Setup That Gets Teams in Trouble
The most common failure pattern we see: a team needs real data for a compliance-sensitive ML project, gets access to synthetic data instead, skips fidelity measurement, trains a model, discovers at evaluation time that the model performs poorly on real data, and concludes that synthetic data doesn't work.
The root problem in this scenario is skipping the fidelity measurement step. Synthetic data quality is not guaranteed; it depends on how well the generative model captures the source distribution, which in turn depends on the complexity of that distribution and the size of the dataset. Generating synthetic data and training on it without first measuring fidelity is the same error as training a model without checking your data for quality issues.
A concrete fidelity measurement takes ten minutes to run before training. Jensen-Shannon divergence per column, Spearman correlation matrix comparison, a basic TSTR spot-check on a small subset. If those pass your threshold, you have reasonable confidence that full training will produce useful results. If they don't, you know to fix the generation step first.
Practical Guidance for Teams Evaluating Synthetic Data
If you're evaluating synthetic data for the first time, run one complete cycle on a subset: real data in, fidelity metrics out, simple model trained on synthetic, evaluated on real. Don't start with your most complex dataset or your most critical model. Start with something where you already know what good results look like so you can calibrate how much the synthetic training degrades performance.
If fidelity is consistently high (0.85 or above on composite metrics) and TSTR accuracy delta is within your tolerance, you have a working pipeline. If fidelity is inconsistent, investigate which columns are causing the problem. High-cardinality categoricals, columns with complex multi-modal distributions, or tight constraint dependencies are common culprits. These are fixable with better generation configuration, not fundamental reasons to abandon the approach.
The teams that get the most out of synthetic data treat it as a tool with a specific profile of strengths, not a general substitute for real data. Used in that narrower scope, it solves problems that are otherwise genuinely hard to solve, and it solves them well.