AI Term of the Week: Synthetic Data
What it means: Synthetic data is artificially generated information that mimics the statistical properties and patterns of real-world data, but doesn't contain any actual observations from reality.
Think of it like creating practice patients for medical students. Instead of using real patient records (with privacy concerns), you generate fictional patients with realistic symptoms, demographics, and medical histories that doctors can learn from without risking anyone's privacy.
Why it matters now: Three converging forces make synthetic data critical:
First, privacy regulations like GDPR make it increasingly difficult to use real customer data for training AI models. Synthetic data lets you develop and test AI systems without touching sensitive information.
Second, AI models are data-hungry, but high-quality labeled data is expensive and time-consuming to collect. Synthetic data generation can produce millions of training examples at a fraction of the cost.
Third, synthetic data solves the "edge case" problem. Self-driving cars need to train on rare scenarios like encountering a moose on the highway, but waiting to capture these events naturally would take years. Synthetic data lets you create these scenarios on demand.
Real-world applications:
Financial services: Banks generate synthetic transaction data to train fraud detection models without exposing real customer financial behavior. This lets them share data with third-party AI developers while maintaining privacy.
Healthcare: Pharmaceutical companies create synthetic patient populations to test clinical trial designs before recruiting real patients, saving millions in trial costs and accelerating drug development.
Autonomous vehicles: Waymo and Tesla generate synthetic driving scenarios—everything from children running into streets to unusual weather conditions—to train their AI systems on situations they haven't yet encountered in real driving.
Retail: Companies like Walmart create synthetic shopping baskets and customer journeys to optimize store layouts and inventory management without analyzing individual customer behavior.
However, synthetic data quality depends entirely on how well it captures real-world complexity. If your synthetic data is too simple or based on flawed assumptions, your AI will learn the wrong patterns. The art lies in generating data that's realistic enough to be useful but artificial enough to protect privacy and create the scenarios you need.
A useful rule: synthetic data works best when you understand your domain deeply enough to know what "realistic" means. It's a tool for experienced practitioners, not a shortcut around understanding your problem.
Possibly the biggest challenge is knowing when synthetic data helps versus when it creates problems. In the 20-minute conversation below, Alexius Rona (CTO at Invisible Technologies) breaks down the practical questions companies face: What's the right mix of synthetic and human data? How do you avoid reinforcing hallucinations? And when should you be transparent about using it?