Synthetic data: Synthetic data is artificially generated data, often produced by an AI model, that mimics the statistical patterns of real data and is used to train, test, or demonstrate systems without exposing real records.
Real data is scarce, messy, expensive to label, and often too sensitive to share. Synthetic data fills the gap. A generator, which may be a language model, an image model, or a simpler statistical process, produces records that look like the real thing: fake patients with realistic vitals, fake transactions with realistic timing, fake support tickets in realistic language.
AI labs use synthetic data heavily. Models generate practice problems, worked solutions, and conversations that are then filtered and used to train the next model, especially in areas where human-written examples are rare. The risk is that errors and quirks compound. Researchers have documented "model collapse," where models trained mostly on the output of other models gradually lose diversity and accuracy, which is why labs mix synthetic data with real data and filter it hard.
For most businesses, synthetic data is useful for testing and training in the everyday sense. You can generate a realistic sample dataset to demo a dashboard, test a workflow, or train new staff without touching customer records. Just do not confuse it with real evidence: a synthetic dataset will not reveal the strange edge cases your real customers produce.
Example at work
A logistics team is building a new claims-tracking sheet and wants to test it before rollout. Rather than copying live shipments, an analyst asks an assistant to generate 200 fictional freight claims with plausible lanes, weights, damage types, and dates. The team debugs the formulas on the fake data, then switches to real records inside the approved system.
Why it matters
Synthetic data lets you build and test with AI tools without leaking real information, which solves a lot of privacy problems at once. Knowing its limits keeps you from drawing conclusions from data that was, by design, made up.
Related terms
- Training dataTraining data is the collection of examples an AI model learns from, such as web pages, books, code, images, or labeled records. What is in that data, and what is missing, shapes everything the model knows, how it writes, and which mistakes it makes.
- Data privacyData privacy, in the context of AI, is the set of rules and practices that govern what information you put into an AI system, who can see it, how long the vendor keeps it, and whether it is used to train future models.
- Personally identifiable information (PII)Personally identifiable information (PII) is any data that can identify a specific person on its own or in combination with other data, such as a name, address, Social Security number, email, phone number, photo, or account ID.
- Generative AIGenerative AI is a class of AI models that create new content, including text, images, code, audio, and video, in response to a prompt. Chat assistants like ChatGPT, Claude, and Gemini and image tools like Midjourney are generative AI.
- Fine-tuningFine-tuning is extra training that adjusts an existing AI model's weights using a smaller, targeted set of examples so it performs better on a specific task, style, or domain. It changes the model itself, unlike prompting, which only changes the instructions.