Synthetic Data Explained: What It Is and When It Helps

Synthetic data is information created by a program or a model rather than collected from real people, sensors or transactions, and it is designed to look and behave like the real thing. Teams use it to test software without touching customer records, to fill gaps where real examples are rare, and to train or evaluate AI systems. This guide explains how synthetic data is made, what it is good for, where it quietly fails and how to use it sensibly when you work with an AI assistant.

The topic sounds technical, but the core idea is simple: a stand-in for real data that is safe to share and cheap to produce. The hard part is making sure the stand-in is faithful enough to be useful and different enough to be safe. Both goals pull in opposite directions, and most of what follows is about that tension.

What synthetic data is, in plain words

Imagine you need a thousand customer records to test a new invoicing screen. You could export real customers, which raises privacy problems, or you could invent a thousand records by hand, which takes days and looks unrealistic. Synthetic data is the third route: generate records that have plausible names, addresses, order histories and mistakes, without any of them belonging to a real person.

The same idea applies far beyond tables. Synthetic images show rare situations for a vision system, synthetic text imitates support conversations, and synthetic time series imitate sensor readings. The general overview of synthetic data lists these families and their history.

It helps to separate three things people often mix up:

• Synthetic data is generated from scratch or from a model of real data.
• Anonymised data is real data with identifying details removed or masked.
• Augmented data is real data modified slightly, such as a rotated photo or a paraphrased sentence, to create more training examples.

Only the first is entirely invented. The other two still start from real people, which is why their privacy risks are different. For a practical look at the anonymised route, see our guide to anonymising text before using AI.

How synthetic data is made

There are four common approaches, from simple to sophisticated.

Method	How it works	Good for	Main limit
Rules and templates	You define fields and formats; a script fills them with random values	Software testing, demos, forms	Looks uniform; misses real-world oddities
Statistical sampling	Learn distributions and correlations from real data, then sample new rows	Analytics, dashboards, prototyping	Only captures patterns you measured
Simulation	Run a model of a process, such as traffic, a factory or a game engine, and record the output	Robotics, driving, rare events	Only as realistic as the simulator
Generative models	A trained model produces new text, images or tables in the style of its training data	Language tasks, images, complex tables	Can copy or distort the originals; needs checking

An AI assistant sits in the last row for text and in the first row when it writes a generator script. Both are useful, and the second is often underrated: asking an assistant to write a small program that produces test data gives you something repeatable, which a one-off pasted list does not.

Why teams use synthetic data

Privacy and sharing

Real customer records are risky to copy into test environments, spreadsheets shared with contractors or demos for sales. Synthetic records avoid that risk when they are generated well. This is the most common reason software teams adopt them.

Rare cases

Fraud, equipment failure and unusual medical presentations are rare in real data, so a model trained on real examples sees too few of them. Simulation or targeted generation can create more examples of the rare class so a system learns to recognise it.

Speed and cost

Collecting and labelling real data is slow. A generated dataset can exist in minutes, which helps with prototypes, classroom exercises and early product tests.

Testing edge cases on purpose

Real data rarely contains the awkward cases you most want to test: a name with an apostrophe, a date on a leap day, an address with no street number, a very long product description. You can ask for them deliberately.

Synthetic data explained through examples

Concrete examples make the idea easier to hold. These are everyday uses where an assistant helps.

Test data in general has a long history in software; the article on test data gives background.

A test database for a small shop

A developer asks Mio for a script that creates 500 customers, 2,000 orders and a realistic share of refunds, with a few deliberately broken rows such as missing postcodes. The script runs on the developer’s machine, writes a file and can be rerun with a different random seed. Because Mio can run Python or Node code in a sandbox on plans that include Code mode, it can also generate a sample and show you a summary before you take the script away.

Practice material for teaching

A teacher needs a spreadsheet of fictional survey answers to teach pivot tables. Real survey data would raise consent questions. A generated sheet with invented respondents, a plausible spread of ages and a few blank cells solves the problem in one request.

Example conversations for a support team

A support lead wants training examples of angry but polite customers asking about delivery delays. The assistant writes thirty short exchanges. They are useful for training new agents, with the caveat that they reflect an assistant’s idea of how customers write, not how yours actually do.

Mock documents for a demo

Sales teams often need an example contract, invoice or report to show a product. Invented documents with fictional companies avoid showing a real client’s details.

Where synthetic data goes wrong

Synthetic data is a copy of a model of the world, and every step can distort. The risks below are the ones that cause real damage.

It copies the biases of its source

If a generator learns from data that underrepresents some groups, the output underrepresents them too, and may amplify the gap. Our article on AI bias explains how this happens and why more data of the same kind does not fix it.

It looks right but is too clean

Real data has typos, duplicates, missing values and strange outliers. Synthetic data tends to be smoother. A model trained or tested only on the smooth version can look excellent in the lab and fail with real input.

It can leak the originals

A generative model trained on real records can reproduce some of them nearly word for word, especially rare ones. Synthetic does not automatically mean private. If the output is meant to be safe to share, test whether any record matches a real one too closely.

Models trained on models drift

If a system is trained mostly on text produced by other systems, errors and stylistic habits can compound over generations, and variety shrinks. Researchers describe this as a loss of diversity. Mixing in real, verified data and measuring on real examples guards against it.

It can be mistaken for evidence

Synthetic data shows what a model of the world predicts, not what happened. A chart built from generated records is an illustration, not a finding. Label it clearly so no one quotes it as real.

How to check that synthetic data is good enough

1. Define the purpose. Testing a form needs different realism from training a classifier.
2. Compare distributions. If real data has 8 per cent refunds and your synthetic set has 30, say so and fix it.
3. Check relationships. Prices should rise with size, dates should fall in order, postcodes should match cities.
4. Look for near-copies. Search for records that match real ones closely, if any real data was involved.
5. Test on a real sample. If you train on synthetic data, always evaluate on a small set of verified real examples.
6. Document it. Record how the data was generated, with which parameters and seed, so others know what it can and cannot show.

Our guide on AI hallucinations is relevant here: a language model asked to produce “realistic facts” will happily invent statistics, so never treat generated figures as sourced.

Working with an assistant to generate data

Good prompts for test data share a pattern. State the schema, the volume, the realism you need, the edge cases to include and the output format.

For example: “Write a Python script that generates 1,000 fictional customers with these columns: id, full name, email, country, signup date, plan. Names should reflect a mix of countries. Include about 2 per cent invalid emails and 1 per cent missing countries. Use a fixed random seed and write a CSV.” Then ask for a short summary of the output so you can check proportions before using it.

Three habits help:

• Ask for the generator, not the data. A script is repeatable and auditable. A pasted block of 1,000 rows is neither.
• Mark fictional values clearly. Use reserved domains such as example.com and obviously fake phone ranges so a test email never reaches a real person.
• Never paste real records to “make more like these”. You would be sending the real data to the assistant. Describe the shape of the data instead.

Synthetic data and the rules

Whether synthetic data counts as personal data depends on how it was made. Data generated purely from rules and random values describes no one. Data produced by a model trained on real people may still reveal information about them, and regulators treat re-identification risk seriously. If you work in a regulated field such as health or finance, speak to your data protection officer before assuming generated data is outside the rules. Our guide to data protection impact assessments for AI tools shows how to think about that risk systematically.

Cost and plan notes

Describing a dataset and getting a generator script is an everyday Code or Chat task. Ask Mio’s pricing page lists a coding answer at 5 to 10 points and a normal chat reply at 1 point, and the free plan includes 600 points a month in Chat and Write. Code mode and the code execution sandbox, which lets Mio run a generator and show you a summary, are part of the Coding and Business plans.

Frequently Asked Questions

Is synthetic data the same as anonymised data?

No. Anonymised data starts from real records and removes or masks identifying details, so some risk of re-identification can remain. Synthetic data is generated rather than copied, so no row belongs to a real person if it was made from rules alone. Data produced by a model trained on real people sits in between and needs testing for leaks.

Can I train an AI model only on synthetic data?

You can, and it works for narrow tasks where the generator is accurate. For general-purpose systems it usually causes problems: variety shrinks and errors compound. A safer approach mixes synthetic examples with real, verified ones and always measures performance on a held-out set of real examples before relying on the result.

Is synthetic data safe to share publicly?

Only if you have checked. Data made from random rules is safe in practice. Data produced by a model trained on real records can reproduce some of them, so test for near-matches and consult whoever owns data protection in your organisation before publishing anything derived from real people.

How do I generate synthetic test data with an AI assistant?

Describe the columns, the number of rows, the realism you want and the edge cases to include, then ask for a script that writes the file with a fixed random seed. Run it yourself, check the proportions, and rerun it when the schema changes. Avoid pasting real records into the chat as examples.

Why does synthetic data look too perfect?

Generators tend to produce smooth, consistent values because they model averages. Real data contains typos, duplicates, missing fields and odd outliers. You can ask for those flaws explicitly, such as two per cent invalid emails or a few duplicated orders, so that tests exercise how your system behaves with untidy input.

Does synthetic data remove bias?

No. A generator reproduces the patterns of what it learned from, including gaps and skews. It can be used to rebalance a dataset deliberately, for example by creating more examples of an underrepresented group, but that is a design decision you must make and then verify, not an automatic effect.

The Bottom Line

Synthetic data is a practical tool for testing software, teaching and covering rare cases, and a risky shortcut when treated as proof or as automatically private. Ask for a generator script rather than a pasted block, include the awkward cases on purpose, and always check the result against a small sample of real data. You can try the approach on the free plan, and see what Code mode adds on the Ask Mio pricing page.