What is synthetic data?
Knowledge, data and integration
Synthetic data is data that is generated artificially rather than collected directly from real people, transactions, devices, or events. It is usually built to mimic the structure or statistical patterns of real data so teams can test systems, train models, share data more safely, or fill gaps where real data is scarce. It can be very useful, but it is not automatically anonymous, accurate, or fit for every purpose, so it still needs careful evaluation for utility, privacy, and bias.
What this means
A simple way to think about synthetic data is as a realistic stand in. Instead of handing a team a live customer database, a hospital record set, or a month's worth of production logs, you generate new records that look and behave enough like the real thing to be useful. The rows, images, text, or events are invented, but the aim is that the patterns in them still reflect something important about the real world. That makes synthetic data different from ordinary "dummy data", which often has the right format but none of the useful structure.
Synthetic data comes in several forms. It can be fully synthetic, where the released records are entirely model generated, or partially synthetic, where some fields are replaced while other parts stay tied to original records. It can also be used as augmentation, where real data remains the main asset and synthetic examples are added to improve coverage of rare cases or edge conditions. In practice, leaders do not need to memorise these labels, but they do need to know that "synthetic" is not one thing. The way it is made affects how trustworthy it is.
This topic often gets blurred with training data, but they are not the same. Synthetic data describes where data comes from and how it was made. Training data describes the role data plays when a model learns. Some synthetic data is used for training. Some is used for testing, validation, or software development. Some is only used for safer sharing between teams. The distinction matters because a dataset can be synthetic without ever touching model training, and a training set can contain little or no synthetic data.
It is also important to separate synthetic data from anonymisation. The ICO explicitly notes that synthetic data may or may not be anonymous. If the synthetic set is too close to the real source data, or if unusual individuals can still be inferred from it, it may still create privacy risk. So the right mental model is not "fake means safe". It is "generated data may reduce some risks, but the residual risk depends on method, context, and testing".
Why it matters
Synthetic data matters because the things organisations most want to do with data are often the things real data makes hardest. Live customer records, patient data, and production logs carry privacy obligations, access restrictions, and real consequences if they leak. Synthetic stand ins let teams build, test, and share more freely while keeping the sensitive original out of harm's way.
It is also a practical answer to scarcity. When a team needs to test a rare scenario, train a model on cases that hardly ever occur, or work before enough real data exists, generating realistic examples can unblock work that would otherwise stall. Used well, it speeds up development and widens who can safely work with data.
The reason it deserves careful handling is that synthetic data is easy to misread as risk free. It is not automatically anonymous, accurate, or representative, and a model trained on data that quietly differs from reality can fail in ways that are hard to see. The value is real, but it depends on understanding what the synthetic data does and does not faithfully reproduce.
How it works
The process starts with a purpose. Before anyone generates a single synthetic record, the organisation needs to decide what the dataset is for. Is the aim to let engineers test an application without exposing live customer data? To train a fraud model where genuine fraud cases are rare? To share data with a supplier? To benchmark an AI system? The required level of realism changes depending on the job. A dataset that is good enough for software testing may be far from good enough for training or policy analysis. NIST's guidance on utility makes this point clearly. There is no one size fits all measure of synthetic data quality.
Once the purpose is clear, teams choose a generation method. For tabular data, that might mean statistical synthesis, probabilistic modelling, or a specialised synthetic data generator that tries to reproduce relationships between variables. For images or sensor data, it may be simulation, rendering, or a generative model. For text, it may mean using rules, templates, or language models to create realistic but invented records. Some organisations combine real seed data with domain rules. Others create synthetic examples almost entirely from simulation. The method must match the domain. A realistic call centre transcript, a medical claims table, and a warehouse sensor stream do not fail in the same way.
If real data is used as the basis for generation, the source data still needs proper governance. The ICO notes that you will generally need to process some real data to estimate realistic parameters, and that this upstream processing must comply with data protection law if those records relate to identifiable people. This matters because many teams focus only on the released synthetic dataset and forget the legal and operational controls around the source set and the generation pipeline.
After generation comes the hard part, evaluation. There are at least three things to assess. First, fidelity: does the synthetic data preserve the statistical properties, correlations, and structure that matter for the task? Second, utility: does it support the analysis, testing, or model performance you actually need? Third, privacy: how easy would it be to infer something about real people from the released dataset? NIST has built tools and benchmark work specifically around this privacy utility trade off, which is a sign of how central that question is.
Utility checks should be task based as well as statistical. A synthetic customer table that matches average age and income distributions might still be useless if it breaks the relationships that matter for churn modelling. A synthetic image set might look convincing to a person but teach a vision model the wrong cues. Strong teams therefore test synthetic data against the real task, not only against summary charts. That often means keeping a tightly controlled real holdout set and asking whether models trained or developed with synthetic data still perform acceptably on real cases.
Privacy checks matter just as much. NIST describes synthetic datasets as having the same schema and attempting to maintain properties of the original data, and in differentially private synthetic data that is paired with a formal privacy guarantee. But outside those stronger guarantees, the residual risk can be hard to judge. The ICO warns that unusual people in the source data can still be inferred if similar unusual records survive in the synthetic release. That means acceptable privacy risk depends on context, attacker capability, and how much realism the project requires.
This is why synthetic data usually works best when the objective is narrow and explicit. "We need safe but realistic data to test the end to end workflow in our product" is a strong use case. "We want one synthetic dataset that can replace all access to production data for every purpose" is usually not. The broader the use case, the harder it is to preserve enough of the right structure while still reducing disclosure risk.
Differential privacy deserves a brief mention because it is often paired with synthetic data in serious discussions. Differentially private synthetic data attempts to provide a provable privacy guarantee for individuals in the source dataset while still preserving useful aggregate structure. That can be powerful, but it comes with its own trade offs in accuracy and complexity. It is not the default mode of synthetic data generation, and leaders should not assume every vendor using the phrase "synthetic data" is providing that level of protection.
Examples
A bank may want to let engineers test a new onboarding flow without granting broad access to live customer accounts. A synthetic dataset can provide realistic combinations of transactions, addresses, risk flags, and account states so the software behaves as it would in production, while reducing the need to spread real personal data into multiple development environments. That is a classic engineering use case.
A manufacturer building a defect detection model may have thousands of examples of normal products and very few examples of rare faults. Synthetic augmentation can help create additional defect images or sensor traces so the model sees more of the minority class during training and testing. The point is not to invent reality, but to give the model more exposure to cases the historical record barely contains.
A healthcare or public sector organisation may also use synthetic data to let analysts prototype pipelines, documentation, or access controls before the live data approval process is complete. In those settings, the synthetic set is not standing in for final validation. It is reducing wasted time while the real governance process continues in parallel.
A customer support team might build synthetic conversations to test a triage assistant or summarisation workflow before using real transcripts at scale. The benefit is speed and safer early stage iteration. The risk is that synthetic conversations can be too clean and too predictable, which may hide the messy language and ambiguity that live customers bring. That is why synthetic data often helps most in early development and targeted augmentation, rather than as the only source of truth.
Common misunderstandings
One common misunderstanding is that synthetic data is just "fake data". In practice, useful synthetic data is engineered to preserve specific patterns that matter for an intended use. That is very different from simple placeholder values. If the structure is not preserved, the dataset may be easy to share but not useful.
Another is that synthetic data is automatically anonymous. It is not. The ICO states this directly. Depending on how the data was generated and what can still be inferred, synthetic data may still carry privacy risk and may still need to be treated with care.
A third misunderstanding is that synthetic data can replace real data everywhere. Sometimes it can replace real data for a specific development or sharing task. Often it cannot replace the need for real world validation. The safe view is that synthetic data should earn trust use case by use case.
A fourth is that more realism is always better. In fact, privacy and usefulness are often in tension. Too much realism can increase disclosure risk. Too little realism can destroy utility. The right balance depends on the task.
Risks and boundaries
Synthetic data can reproduce the weaknesses of the real data it was based on. If the source data is biased, incomplete, or historically distorted, the synthetic version can preserve those distortions or even make them harder to spot. It can also smooth away rare cases that matter commercially or ethically. In short, synthetic data can reduce access risk while still preserving quality risk.
There is also a practical boundary around proof. A vendor may claim a synthetic dataset is "privacy safe" or "production grade", but without a clear description of how utility and disclosure risk were assessed, that claim should be treated as incomplete. Strong evidence usually includes task specific validation, disclosure testing, known limitations, and documentation of what the synthetic data should not be used for.
Finally, synthetic data does not remove legal or professional judgement. If real personal data was used to create it, the source processing still matters. If the synthetic set supports a regulated or high stakes decision, the final system still needs testing on real conditions that reflect deployment. This article is a practical explainer, not legal, privacy, or statistical advice.
What to do next
Start with one narrow use case where synthetic data has a clear job. Good first candidates are product testing, partner sandboxes, model augmentation for rare events, or safe analyst prototyping. Avoid broad mandates like "replace production data with synthetic data everywhere".
Then ask four simple questions. What decision will this dataset support. What properties must be preserved. What privacy risk remains after generation. How will we test usefulness on real world conditions. If the team cannot answer those questions clearly, the project is not ready.
Keep the real source data tightly controlled, document the generation method, and require an evaluation pack that covers fidelity, utility, and disclosure risk. If the use case touches sensitive people data or regulated processes, involve privacy, security, and domain owners early. Synthetic data is often most valuable when it shortens careful work, not when it tries to bypass it.
FAQs
Is synthetic data the same as anonymised data?
No. Synthetic data may reduce identifiability, but the ICO is clear that it may or may not be anonymous depending on how it was created and what can still be inferred from it.
Can synthetic data be used to train AI models?
Yes. OECD guidance notes that synthetic data can be used to train AI when real data is scarce or confidential, but model quality and bias still need to be checked against real use.
Is synthetic data only useful for large enterprises?
No. Smaller organisations can use it for safer development, demos, testing, and narrow augmentation use cases. The key issue is not company size but whether the synthetic set is good enough for its stated job.
How do you tell if a synthetic dataset is any good?
By testing its task utility, not just how realistic it looks. Strong practice checks statistical fidelity, task performance, and disclosure risk together.
Does differential privacy come with synthetic data by default?
No. Differential privacy is a specific mathematical privacy framework. Some synthetic datasets use it, many do not.
When should we avoid relying on synthetic data?
Be cautious where the final decision is high stakes, the real world edge cases matter most, or the synthetic set cannot be validated against controlled real data.
Sources
Glossary (Information Commissioner's Office). Primary. Definition of synthetic data and the point that it may or may not be anonymous.
How should we assess security and data minimisation in AI? (Information Commissioner's Office). Primary. Limits of synthetic data, source data processing duties, and inference or re identification risk.
Differentially Private Synthetic Data (NIST). Primary. Explanation that synthetic data aims to preserve structure and properties, and when differential privacy adds a formal privacy guarantee.
Guidelines for Evaluating Differential Privacy Guarantees (NIST). Primary. Privacy evaluation concepts, synthetic data characteristics, and the need to weigh privacy and utility.
SDNist v2 Deidentified Data Report Tool (NIST). Primary. Evidence that synthetic and deidentified data should be evaluated with explicit metrics rather than assumed to be safe or useful.
HLG-MOS Synthetic Data Test Drive Guide (NIST). Primary. Utility and privacy evaluation workflow for synthetic data.