Synthetic data is one of the fastest-growing topics in ML — and one of the most misunderstood. The hype says synthetic data will replace real data entirely. The skeptics say it's snake oil. The truth, as usual, is more nuanced and depends entirely on your use case.
This article gives you a clear framework for when to use synthetic data, when real data is irreplaceable, and how to combine both for the best results.
What Is Synthetic Data?
Synthetic data is data that was artificially generated rather than collected from real-world events. It comes in two main flavors:
- Statistical synthetic data — Generated by learning the statistical distribution of real data and sampling from it. Tools like CTGAN, TVAE, and Gretel.ai work this way for tabular data. The generated rows have similar statistical properties to real data but are not real records.
- Simulation-based synthetic data — Generated by running simulations (physics engines, traffic simulators, financial market simulators). Common in autonomous vehicles, robotics, and quantitative finance.
In both cases, the data contains no actual real-world records — making it intrinsically privacy-safe.
Side-by-Side Comparison
Synthetic Data — Advantages
- No real PII — safe to share and sell
- Generate at arbitrary scale
- Oversample rare events (fraud, failures)
- No data collection or labeling cost
- Generate scenarios that haven't happened yet
- Clear licensing — you own it
Synthetic Data — Limitations
- Requires a real seed dataset to learn from
- May not capture complex real-world patterns
- Can amplify biases in the seed data
- Rarely matches real data for unstructured types (images, audio)
- Needs validation against real data
- Model collapse risk if used recursively
When Synthetic Data Works Well
Tabular classification (fraud, churn, credit risk)
This is where synthetic data shines. Tools like CTGAN can learn joint distributions across dozens of features and generate thousands of realistic synthetic transactions. The key requirements: a real seed dataset of at least 5,000 rows to learn from, and validation that synthetic data doesn't leak patterns that don't exist in real data.
LabelSets' proprietary datasets are in this category — our E-Commerce Fraud Detection and SaaS Customer Churn datasets are synthetic but built to match real statistical distributions, with realistic class imbalance and feature correlations.
Rare event oversampling
Even when you have real data, rare events (fraud, equipment failures, disease cases) are underrepresented. Generating synthetic examples of the minority class — through SMOTE, CTGAN, or other methods — is standard practice and generally works well.
Data augmentation for computer vision
Geometric transforms (flip, rotate, crop, color jitter) are a form of synthetic data generation and are universally used in vision training. More advanced augmentation (Mixup, CutMix, diffusion-based augmentation) can generate genuinely novel training examples from real images.
Simulation for autonomous systems
Autonomous vehicles, drones, and robots are trained almost entirely on simulated data — it's the only way to safely generate edge cases (accidents, sensor failures, rare road conditions). NVIDIA DRIVE Sim, Waymo's simulation stack, and game engines like Unreal are central to AV model training.
When Real Data Is Irreplaceable
LLM pre-training
Language models are pre-trained on real human text because the goal is to model actual human language. Synthetic text generated by earlier LLMs creates "model collapse" — cascading degradation as each generation trains on increasingly distorted data. Real human-written text cannot be replaced for pre-training.
High-stakes medical and legal tasks
When the model needs to learn real clinical or legal reasoning patterns, synthetic data created without domain expert oversight introduces subtle errors that degrade trust in exactly the cases that matter most. Use real expert-annotated data for fine-tuning in these domains.
Distribution shift detection
If your model needs to detect when reality has changed (market regime shifts, fraud pattern changes, equipment wear), training on synthetic data can mask the signal. You need real data representing the actual distribution your model will face.
| Task | Synthetic? | Notes |
|---|---|---|
| Tabular fraud/churn/credit | Yes ✓ | Works well with proper validation |
| Rare class oversampling | Yes ✓ | Standard practice (SMOTE, CTGAN) |
| Computer vision augmentation | Partially | Transforms yes; full generation limited |
| Autonomous vehicle simulation | Yes ✓ | Physics simulation is industry standard |
| LLM fine-tuning (domain) | Partially | Synthetic instruction pairs OK; avoid for pre-training |
| LLM pre-training | No ✗ | Real human text required |
| Medical imaging diagnosis | Partially | Augmentation OK; replace real labels carefully |
| Distribution shift detection | No ✗ | Real data required |
The Best Approach: Hybrid Training
The most robust production models combine real and synthetic data:
- Start with real data — even a small seed dataset (5,000–50,000 rows) is enough to characterize the real distribution
- Augment with synthetic data — use SMOTE or CTGAN to expand the minority class and balance your dataset
- Validate against held-out real data — your test set must always be real data, never synthetic
- Monitor for distribution drift — if real data patterns shift, your synthetic augmentation needs to update too
LabelSets carries high-quality proprietary synthetic datasets for fraud detection, churn prediction, credit risk, and more — all built from realistic statistical distributions with proper class imbalance and temporal structure. Browse all datasets → — preview any dataset for free before buying.
Frequently Asked Questions
What is synthetic data in machine learning?
Synthetic data is artificially generated data that statistically mirrors real data without containing actual real-world records. It's created using generative models (CTGAN, GANs), simulation, or algorithmic generation — and contains no real personal information.
Is synthetic data as good as real data for ML training?
It depends on the use case. For tabular classification tasks (fraud, churn), high-quality synthetic data trained on real distributions can match real data performance. For computer vision, NLP pre-training, and distribution drift detection, real data is generally irreplaceable.
What are the advantages of synthetic data?
Privacy-safe (no real PII), scalable, can oversample rare events, avoids legal uncertainty around data provenance, and can model scenarios that haven't happened yet. For tabular use cases, it's often faster and cheaper than collecting real labeled data.