Synthetic Data

What is synthetic data?

Synthetic data is artificially generated information that looks and behaves like real data but contains no actual real-world records. It is created by algorithms—often AI models trained on real data—to serve as a substitute or supplement for real data in training, testing, and development.

The Analogy: A Flight Simulator

A pilot does not need to crash a real plane to learn how to handle turbulence. A flight simulator generates realistic scenarios—storms, engine failures, crosswind landings—that let pilots practice safely. Synthetic data works the same way for AI: it creates realistic but artificial examples that let models learn patterns without exposing them to sensitive or scarce real-world data.

Why synthetic data matters

Real-world data is often scarce, expensive, privacy-sensitive, or legally restricted. Synthetic data addresses these constraints:

• Privacy: Healthcare records, financial transactions, and personal data are governed by regulations like GDPR and HIPAA. Synthetic data preserves the statistical patterns models need while containing zero actual patient or customer records [1].
• Scarcity: Rare events—fraud patterns, equipment failures, medical edge cases—may not appear in sufficient quantities in real datasets. Synthetic data can generate thousands of realistic examples of these rare scenarios on demand.
• Cost: Collecting, labeling, and cleaning real data is often the most expensive part of an AI project. Synthetic data reduces dependence on manual data collection pipelines.
• Bias mitigation: Real data often reflects historical biases. Synthetic data can be deliberately balanced to ensure models see representative examples across demographics, geographies, and edge cases.

How synthetic data is generated

Modern synthetic data generation uses several families of techniques [2]:

• GANs (Generative Adversarial Networks): Two neural networks—a generator and a discriminator—compete against each other. The generator creates fake data; the discriminator tries to distinguish it from real data. Over time, the generator produces increasingly realistic synthetic samples. CTGAN and TVAE are widely used for tabular data.
• Diffusion models: The same architecture behind image generators like Stable Diffusion, adapted for structured data. Models like TabDDPM learn to reverse a noise-adding process, producing high-fidelity synthetic records. Diffusion models offer greater training stability than GANs for heterogeneous tabular features [2].
• LLM-based generation: Large language models treat data rows as text sequences and generate synthetic records that capture semantic relationships between columns. Approaches like GReaT and DP-LLMTGen leverage the reasoning capabilities of LLMs for data synthesis.
• Statistical methods: Simpler approaches like Bayesian networks, copulas, and SMOTE (Synthetic Minority Over-sampling Technique) generate data by modeling the statistical distributions in the original dataset. These are faster but produce less realistic results for complex data.

Privacy-preserving synthesis

A key concern with synthetic data is whether it inadvertently memorizes and reproduces real records from its training set. Differential privacy (DP) addresses this by adding calibrated noise during training, providing mathematical guarantees that no single real record can be reconstructed from the synthetic output. Techniques like DPGAN and PATE-GAN integrate differential privacy directly into the generation process [2].

The distinction between “private synthetic data” (with formal DP guarantees) and “vanilla synthetic data” (no formal guarantees) matters. Without differential privacy, synthetic data may still leak information about real individuals, particularly for rare or unique records.

Use cases across industries

Synthetic data is deployed across healthcare, finance, and software development:

• Healthcare: Generating synthetic patient records for clinical research, drug development, and model training without exposing real patient data. Companies like MOSTLY AI and Tonic specialize in HIPAA-compliant synthetic data for healthcare.
• Finance: Creating synthetic transaction data for fraud detection model training, stress testing, and regulatory compliance. Banks use synthetic data to share realistic datasets with third-party developers without exposing customer information.
• Software testing: Generating realistic test data for development and CI/CD pipelines, replacing manually curated test databases that are expensive to maintain and often stale.
• Autonomous vehicles: Simulating rare driving scenarios (pedestrian crossings in unusual conditions, unusual road obstacles) that may not appear in real driving data at sufficient scale.

Key vendors and tools

The synthetic data market includes specialized platforms [1]:

• MOSTLY AI: High-fidelity synthetic data platform focused on healthcare and finance, with strong privacy and compliance orientation [4].
• Gretel: Developer-first synthetic data APIs supporting tabular, time-series, and image data with differential privacy capabilities [5].
• Tonic: Automated synthetic data for testing, development, and production environments, strong in financial services and HIPAA-regulated data.
• SDV (Synthetic Data Vault): Open-source library for generating synthetic tabular, relational, and time-series data, widely used in research.

Limitations

Synthetic data is not a universal solution:

• Fidelity vs. privacy tradeoff: The more realistic synthetic data is, the higher the risk of memorizing real records. Differential privacy reduces this risk but can degrade data quality.
• Validation difficulty: Evaluating whether synthetic data accurately captures the relationships present in real data is itself a complex task. Poor synthetic data can introduce subtle biases that are harder to detect than missing data.
• Does not replace real data entirely: Models still need real validation and test data to measure actual performance. Synthetic data cannot fully substitute for real-world evaluation.
• Garbage in, garbage out: If the real data used to train the generator is biased or low quality, the synthetic data will reproduce those problems.

Reader Questions

Is synthetic data the same as fake data?

No. Random fake data (like made-up names or addresses) has no statistical relationship to real data. Synthetic data is designed to preserve the patterns, distributions, and correlations found in real datasets while containing zero actual real-world records. The goal is realism, not randomness.

Can synthetic data replace real data for AI training?

Partially. Gartner predicts that by 2030, synthetic data will surpass real data as the majority of training data used in AI models [3]. However, real data remains essential for validation and evaluation—you cannot measure how well a model performs on real-world inputs using only synthetic test data.

Does synthetic data protect privacy?

It depends on how it is generated. Synthetic data created with formal differential privacy guarantees provides mathematical protection against reconstructing real records. Without differential privacy, there is a risk that the generator memorizes and reproduces rare real records. Always verify whether a synthetic data vendor offers formal privacy guarantees or only informal ones.

What is the difference between synthetic data and data augmentation?

Data augmentation modifies existing real data (rotating an image, rephrasing a sentence) to create variations. Synthetic data generates entirely new records from scratch based on learned patterns. Augmentation works with existing data; synthesis creates new data. Both increase dataset size, but synthetic data can generate data for categories where you have no examples at all.