Synthetic data explained
Synthetic data is artificially created information designed to represent useful properties of a real or imagined system. Instead of collecting another customer transaction or patient encounter, a team generates a new example using rules, statistical models, simulation or machine learning. The objective is not necessarily to reproduce every detail. It is to preserve the characteristics needed for a specific task, such as testing software, developing a forecasting model or exploring an operational scenario.
This distinction matters because realistic appearance does not establish usefulness. A table can look convincing while containing impossible relationships, and a statistically plausible dataset can still expose information about its training records. A credible project therefore starts with a purpose and measurable acceptance criteria rather than a request for a large quantity of artificial rows.
How it differs from anonymised data
Masking changes selected values in existing records, while anonymisation aims to prevent people from being identifiable through information that remains. Synthetic generation creates new records, although those records may still reflect patterns learned from personal information. These approaches address different requirements and should not be treated as interchangeable labels. Replacing names alone, for example, does not prevent identification through unusual combinations of location, age and activity.
The ICO specifically cautions that synthetic data is not automatically anonymous. Membership inference, attribute inference and linkage attacks can remain relevant. Processing the original information also needs an appropriate legal basis and governance. Legal and security specialists should assess the actual release context, including who receives the data and what other information they could access.
Choosing a generation approach
Rules and simulation work well when domain constraints are explicit, such as allowable transaction states or equipment operating limits. Statistical methods can represent distributions and dependencies in structured tables. Generative machine learning can model more complicated patterns in text, images or mixed datasets. The right choice depends on structure, available examples, explainability requirements and the computational resources the organisation can sustain.
Complexity is not a quality guarantee. A simple generator with well-tested constraints may outperform a sophisticated model for software testing. Conversely, independently sampling each column usually loses relationships that matter for prediction. Our synthetic data generation guide explains how to turn these choices into a repeatable workflow.
Where organisations use it
Development teams can create realistic test environments without routinely copying production records into laptops or external sandboxes. Data scientists can investigate whether carefully designed augmentation improves performance for underrepresented conditions. Researchers can explore collaboration datasets where direct sharing is difficult. Simulation teams can create hypothetical demand, failure or disruption scenarios that historical observations do not adequately cover.
Each application needs its own evaluation. A dataset suitable for checking whether an application handles missing values is not necessarily suitable for estimating clinical risk. Equally, scenario data deliberately representing a severe disruption should not be presented as a forecast of how often that disruption will occur. The intended use should travel with the dataset.
Evaluating utility and privacy together
Utility assessment should include schema validity, marginal distributions, relationships between fields and performance on the downstream task. Keep an untouched real evaluation set whenever lawful and feasible. A model trained on synthetic examples should be evaluated against real conditions, not only against other generated examples. Check subgroup behaviour as well as aggregate scores, because improvements in a large group can conceal deterioration elsewhere.
Privacy assessment needs an explicit threat model. Compare generated records with training examples, test credible attacks and document residual risks. Differential privacy can provide formal guarantees when correctly implemented and accounted for, but it introduces a trade-off with utility. It does not justify saying that disclosure is mathematically impossible in every circumstance.
Putting governance around the dataset
Record the source versions, permissions, transformations, generation settings and evaluation results. Assign a dataset owner and define where outputs may be used. Retention and access restrictions may still be appropriate even after privacy testing. If generation is repeated, establish whether new releases increase cumulative exposure and whether the previous approval remains valid.
Operational controls should include reproducible runs, versioned validation reports and a process for withdrawing unsuitable releases. Source quality also matters: synthetic generation can preserve historical bias and measurement errors. Reliable data integration is therefore part of synthetic data quality, not a separate administrative concern.
Questions to ask before sharing
Ask whether a recipient could combine the release with outside information, whether unusual records have been examined and whether the permitted use is clear. Review the trained generator as well as its exported dataset: model access can create a different exposure from a static release. Controls should reflect that difference instead of treating every delivery method as equivalent.
Also consider what happens if recipients discover an error. Provide a contact, version identifier and withdrawal procedure. A useful release note explains limitations in language its users understand, including tasks for which the data has not been validated. This reduces the chance that a convenient testing dataset is later reused as evidence for an unrelated operational or clinical decision.
A practical starting point
Begin with one bounded use case, one accountable team and a baseline dataset. Define what success means before selecting a tool: perhaps fewer production copies, faster test provisioning or acceptable predictive performance under agreed privacy constraints. Compare several methods on the same criteria and reject outputs that fail hard business rules, regardless of how impressive their visualisations appear.
For healthcare, read the synthetic healthcare data use case before designing a release process. To discuss requirements rather than assume a particular deployment or guarantee, explore ALLFLOW's GenAI Synthetic Data Engine. A useful pilot produces evidence, limitations and an adoption decision, not simply a larger dataset.