Synthetic Data
Artificially generated data that mimics real-world data for AI training and testing.
Definition
Synthetic data is artificially generated information that mimics the statistical properties and patterns of real-world data without containing actual real data. Created through algorithms, simulations, or AI models, synthetic data serves as a substitute for real data in situations where authentic data is unavailable, insufficient, sensitive, or problematic to use directly.
Synthetic data generation approaches include rule-based generation (creating data according to defined rules and distributions), simulation (generating data from modeled systems and processes), generative AI models (using techniques like GANs or VAEs to create realistic data), and agent-based modeling (simulating entities whose interactions produce realistic data patterns).
Quality synthetic data preserves the statistical properties, relationships, and patterns in real data while eliminating identifiable information and potentially addressing biases or gaps in original datasets. The data should be useful for the intended purpose—training ML models, software testing, analytics—while not exposing sensitive information.
Use cases span many domains: training AI models when real data is insufficient or unavailable, software testing and development, analytics and research, financial modeling and stress testing, and healthcare and other sensitive sectors where privacy prevents real data use.
Why It Matters
Synthetic data addresses the fundamental tension between data needs and data constraints. AI development requires vast training data, but privacy regulations, data protection requirements, and data scarcity often limit access to real data. Synthetic data provides an alternative that can satisfy data needs while respecting constraints.
For privacy-sensitive domains like healthcare and finance, synthetic data may be the only viable option for certain applications. Patient data, financial records, and other sensitive information cannot be freely shared, but synthetic equivalents can enable research, development, and testing that would otherwise be impossible.
Synthetic data can also address quality issues in real data. It can be generated to fill gaps, balance underrepresented categories, remove biases, or create edge cases that rarely occur naturally. These improvements can make training data more suitable for producing fair, robust AI systems.
The economics of synthetic data are attractive. Generating synthetic data is often cheaper and faster than collecting, cleaning, and labeling real data. As synthetic data quality improves, it increasingly offers a practical alternative to expensive data collection.
Examples in Practice
A healthcare AI company cannot access sufficient patient imaging data due to privacy restrictions. They train generative models on available data, then generate synthetic medical images for AI training. The synthetic data enables model development without accessing protected patient information.
A financial services firm uses synthetic transaction data to test fraud detection systems. The synthetic data includes realistic patterns of both legitimate transactions and various fraud types, enabling thorough testing without exposing actual customer data.
An autonomous vehicle company supplements limited real-world driving data with synthetic data from simulation. The simulations generate diverse scenarios including rare edge cases that might take years to encounter naturally, improving system robustness.
A software development team uses synthetic customer data for development and testing environments. The synthetic data has realistic properties for testing purposes without containing any actual customer information, enabling development without data protection concerns.