Synthetic data is artificially generated data that wasn't produced by real-world events, usually created by algorithms to mimic the patterns of real data. It's used for testing and training AI without exposing real people's details. The catch: it works best alongside some real data, not instead of it.
Every so often a vendor tells you their AI was trained on synthetic data, and you nod along without quite knowing what it means. I've found it's a simple idea with a few important catches. If you run a business and you're thinking about testing software, training a model or sharing data safely, it's worth understanding.
Synthetic data in plain words
Wikipedia defines synthetic data as artificially generated data that wasn't produced by real-world events. It's typically created using algorithms. Think of a flight simulator: nobody's flying, but the readings and scenarios look and behave like the real thing.
In business terms, it's a made-up spreadsheet of customers, orders or messages that follows the same patterns as your real records but doesn't describe any real person.
How synthetic data gets made
Wikipedia describes a few routes. The most common idea is statistical: take real data, build a model or equation that fits it, then sample brand-new records from that model. Another route uses generative adversarial networks, where one AI system produces examples that another tries to spot as fake, and the results can then be used for training.
For language models there's a third route. Wikipedia mentions a method called Self-Instruct, where a small seed set of human-written instructions is used to have a model write a much larger set of instruction-following examples. In other words, an AI helps produce the practice material for another AI.
Where synthetic data is used
- Testing systems. Wikipedia says it's used to train and test fraud and intrusion detection on realistic scenarios, including attacks that don't appear in authentic data.
- Training AI models. A model can be trained on a synthetic dataset with the aim of working well on real data afterwards.
- Research baselines. Scientists use it to create baselines for studies and testing.
- Where real data is scarce. Wikipedia points to defence work, where complex systems need data that's hard to collect.
For a small business, the practical version is simpler. If you're setting up a new CRM, a booking system or a reporting dashboard, dummy customers are safer to experiment with than your real client list.
Why people choose synthetic data
Wikipedia lists a few benefits. Once the synthetic environment is ready, it's fast to produce as much data as you need. The labels can be perfectly accurate, which matters because labelling real data by hand is slow and sometimes impossible. And the setup can be adjusted to improve the model and its training.
Privacy is the one that gets most attention. Wikipedia says synthetic data is used to protect confidentiality because it holds no personal information and can't be traced back to any individual. It also notes the idea goes back to Donald Rubin, who proposed fully synthetic data in 1993 as a way of releasing census records without exposing real household records.
The limits of synthetic data
Here's the part that gets skipped in sales pitches. Wikipedia says that moving from synthetic to real data, known as transfer learning, remains a nontrivial problem, and that synthetic data hasn't become ubiquitous yet. It also reports research showing that adding a small amount of real data significantly improves the results.
There are regulatory questions too. Wikipedia says regulators are at various stages of recognising and integrating AI-generated synthetic data. It also notes that, in drug development, no drug or medical device has been approved using solely or predominantly synthetic data.
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So the safe reading is this: synthetic data is a useful supplement, not a magic replacement for the real thing. If a generator copies too closely from your real records, the privacy promise is weaker, so it's reasonable to ask a vendor how they check that.
Synthetic data versus real data in a small business
| Real data | Synthetic data | |
|---|---|---|
| Where it comes from | Actual customers, sales and messages | Generated by an algorithm or model |
| Privacy handling | Needs care and clear permission | Holds no personal information, according to Wikipedia |
| Good for | Decisions about your real customers | Testing, practice runs and training |
| Main weakness | Can be limited or messy | May not match real life closely enough |
My rule of thumb: use synthetic data to rehearse, and real data to decide.
Sensible habits when synthetic data comes up
- Ask where it came from. Which real data was the generator built from, and was it allowed to be used that way?
- Ask what was checked. A good vendor can explain how they test that the data behaves like the real thing.
- Keep some real examples. Research cited by Wikipedia says a small amount of real data helps a lot.
- Don't paste real client details into a public AI tool to make a fake version. Use a made-up sample or follow your tool's own data controls page.
If you're weighing up which AI tools are worth using in your business, my roundup of the best AI tools for small business owners is a good place to start. And if you'd like a hand working out where data like this fits, that's part of what I do as an AI consultant.
Frequently asked questions
What is synthetic data?
It's artificially generated data that wasn't produced by real-world events. It's usually created by algorithms and made to follow the patterns of real data.
Why is synthetic data used?
It can be produced quickly in large amounts, can come with accurate labels, and is used for testing, training AI models and protecting confidentiality.
Is synthetic data the same as fake data?
It's closely related. Good synthetic data is built to behave like real data statistically, whereas random dummy values may not.
Does synthetic data protect privacy?
Wikipedia says it holds no personal information and can't be traced to an individual, but it's wise to ask how a vendor checks that the generator hasn't copied real records.
Can synthetic data replace real data?
Not fully. Wikipedia says moving from synthetic to real data is still a nontrivial problem, and that a little real data significantly improves results.
How does a small business use synthetic data?
Mostly for testing: trying out new software, dashboards or workflows with made-up customers before using real records.
