What is synthetic data?

Since collecting information from people or real events can be slow, pricey, or raise privacy issues, developers let an algorithm create as much "sample" data as they need. They then use this made-up data to test software and train AI models without risking anyone's personal information.

Before putting synthetic data to work, it helps to know the flavors you can create. Synthetic datasets differ both in the medium they imitate and in how much of the final record is fabricated versus copied from the real world. Let's elaborate on these two ways.

By format

• Text data – computer-generated sentences or documents for training chatbots and other NLP tools.
• Tabular data – spreadsheet-style rows and columns for testing databases or analytics models.
• Multimedia – fake images, video or audio for computer-vision jobs like teaching a camera to spot defects.

By how much is "made-up"

• Fully synthetic data – 100 % fabricated. The algorithm studies real patterns, then creates brand-new records that never existed. Banks, for instance, can invent fraudulent-transaction logs to train an anti-fraud model.
• Partially synthetic data – most of the data is real, but sensitive fields (names, IDs, addresses) are swapped for artificial values, so privacy is protected. Hospitals use this to share research data without exposing patients.
• Hybrid data – a blend of the two: some untouched real records mixed with fully synthetic ones, shuffled, so no entry can be traced back to a person. Retailers might use this to analyse buying trends without revealing any single customer.

Real data is gathered from actual events (blood-test results, card purchases, sensor logs, clickstreams). It reflects the messiness, gaps, and privacy risks of the real world.

Artificial data is any information a computer invents instead of observing. It can be completely random (dummy phone numbers for a UI demo), procedurally simulated (weather readings from a physics model), or statistically engineered to resemble a target distribution.

Synthetic data is artificial data with a specific goal: mimic the statistical patterns of a real data set closely enough that an analytics or machine learning workflow can treat it as a drop-in stand-in. Algorithms study the original sample, capture its correlations and ranges, then generate new records that look "real" but contain no link back to actual individuals.

AI creates synthetic data by training a generative model on a small sample of real data, then sampling that model to produce unlimited new records.

Here are 4 main techniques for training:

1. GANs (Generative Adversarial Networks) – Think of two AIs in a contest. One makes fake data, the other tries to spot the fake. As they compete, the "faker" gets so good that its output looks real.
2. VAEs (Variational Autoencoders) – The model shrinks each record into a short code, then rebuilds it. By perfecting this shrink-and-expand trick, it learns the data's patterns and can spit out new, similar records.
3. Diffusion models – Start with static-like noise and teach the model to clear it away step by step until a clean image (or other data) appears. Running that process creates sharp, realistic samples.
4. Transformer models – Large language models predict one word (or code token) at a time. Keep letting them predict, and they generate convincing text, tables, JSON, or even full code files.

Synthetic data is already powering real-world AI across several industries:

Media and voice assistants
Graphics and audio generators create fake images, video, and speech so systems like Amazon Alexa can learn new languages without endless human recordings.

Natural language tools
Chatbots, machine translation engines, and sentiment-analysis models all practice on computer-made text. The best example is ChatGPT.

Tabular analytics
Finance and research teams spin up artificial rows and columns to stress-test dashboards or train models when real tables are too small or sensitive.

Unstructured vision data
Self-driving programs at companies such as Google's Waymo rely on simulated streets, weather, and traffic to teach cars how to react to rare road events.

Synthetic transaction streams
Fintech and banking platforms like American Express and PayPal generate privacy-safe payment datasets to train fraud-detection and credit-scoring models without risking real customer information.

Synthetic sensor logs
In manufacturing and insurance, firms such as BMW and AXA blend simulated LiDAR, camera feeds or event-driven claims data with live inputs to perfect defect detection, predictive maintenance, and risk-pricing models in a safe, scalable way.

Synthetic data lets teams shape datasets to precise business needs, save time, protect privacy, and enrich model training:

• Made-to-order datasets – You can dial in any size, class balance, or rare scenario your project needs.
• Speed and cost savings – Data is generated (and already labeled) in hours, not weeks, with no expensive field collection.
• Privacy by design – No real people or copyrighted records are included, easing compliance and data-sharing worries.
• Richer training material – Synthetic samples let you fill gaps, balance under-represented groups, and inject edge cases, so models learn more robustly.