Why this question matters now
At the Institute of Analytics Conference last month, a question from the audience stayed with me. It was about the value of synthetic data for organisations, especially in government analytics. I was one of the panel speakers and the question reflected something many teams are now facing. Synthetic data is getting more attention, but there is still confusion about what it is genuinely useful for and where its limits begin.
That matters because synthetic data is often described too broadly. Some people talk about it as though it solves data access problems on its own. Others treat it as a safer replacement for real data. Synthetic data can be very useful, but only when the purpose is clear. In the right setting, it can help a project move forward. In the wrong setting, it can create false confidence.
Synthetic data is artificially generated data. It is created by a model, a simulation or a set of rules. It is not taken directly from real people, transactions, or events. The aim is usually to reproduce important features of a real dataset. That might include the overall shape of the data, the balance between categories, or common patterns between variables. A variable is simply a field or column in a dataset, such as age, salary or number of transactions. The goal is to create something that behaves enough like the original to be useful, without exposing the original records themselves.
It also helps to separate synthetic data from anonymised data. Anonymised data starts as real data, then has identifying details removed or masked. Synthetic data is generated from scratch, even if a real dataset was used as a reference point. That distinction matters. People often assume synthetic means private, safe or freely usable. It does not automatically mean any of those things.
Where the value lies for organisations and students
For organisations, the value of synthetic data often appears before the main analysis even begins. In many workplaces, progress slows down because access to live data is restricted. That is often for good reason. The data may contain personal information, commercially sensitive material, or records linked to public services. A team may want to test a dashboard, check a reporting pipeline, or prototype a machine learning workflow. Even so, they may not yet be allowed to use the real records. Synthetic data can give them a workable starting point.
That starting point is more useful than it may first appear. A team can check whether columns arrive in the right format. They can see whether missing values break the system. They can test whether categories are handled properly and whether business rules behave as expected. Business rules are the logic a system follows, such as flagging a late payment or assigning a customer to a group. This early work may not be the most visible part of a project, but it often shapes whether the rest of the project runs smoothly.
This is one reason synthetic data matters in government and public sector analytics. Public bodies often work with data that is both valuable and sensitive. That may include health, education, housing, employment, or social care data. Access to those datasets is rightly handled with caution. Synthetic data does not remove that caution, but what it can do is allow some parts of the work to begin earlier. Teams can understand the structure of the data, they can test code and prepare methods and can spot obvious issues before working directly with protected records. In practice, that can make projects more efficient without lowering the standard of care around real data.
The same logic applies in universities and student projects. One of the persistent problems in analytics education is that the most useful real world datasets are often the hardest to teach with. They may be sensitive, licensed, or unsuitable to share widely. As a result, students often learn examples that are tidy but unrealistic. Synthetic data offers a middle ground. It can be designed to feel more like real analytical work while avoiding many of the barriers that come with live data. During IoA student sprint, an analytics competition that simulates real workplace conditions, synthetic data is often used to simulate realistic datasets that students will encounter in the workplace.
That has genuine educational value. A good synthetic dataset can include irregularities, missing values, uneven categories, and surprisingly realistic patterns. It can force students to think rather than follow a clean path from spreadsheet to chart. They need to decide how to clean the data, how to interpret what they see, and how to explain uncertainty. That is much closer to real analytical practice.
It also makes synthetic data useful for project work and employability. Students often need to show evidence of process, not just results. They need to demonstrate how they approached a problem, what assumptions they made, how they handled messy data, and how they communicated findings. Synthetic data supports that well. It gives students something realistic enough to analyse and present, while avoiding the problem of using data they are not allowed to share in public work or portfolios.
What synthetic data cannot do
Synthetic data can be very useful, but its limits matter, especially when the analysis is high stakes. The main issue is that a synthetic dataset can look convincing while still losing some of the features that matter most. It may preserve the general pattern of the original data, but fail to retain the details that drive meaningful insight.
For example, a synthetic dataset may reproduce average behaviour quite well. It may show how most customers spend or how most service users behave. But averages are not always what matters. In many real cases, the most important information sits in the exceptions. That might mean a rare fraud case, an unusual medical outcome, or a small vulnerable group. Synthetic generation can smooth these details out. The result can be data that looks more regular and less messy than reality actually is.
That becomes a serious issue when the analysis depends on those exceptions. If a team is doing exploratory work, meaning early stage work to understand structure and ask initial questions, synthetic data may be ideal. If a team is trying to make strong claims about how the real world behaves, much more caution is needed. The same applies in modelling. If the data does not preserve the right relationships between variables, the model may appear more reliable than it really is.
Privacy can also be misunderstood in this discussion. You will often hear the term disclosure risk. That simply means the risk that someone could work out information about a real person, household, or organisation from the dataset. Even if names have been removed, there can still be danger if the data is too detailed or too close to the original. Synthetic data can reduce this risks, but that depends on how it was generated and how closely it reflects the original records. Synthetic should not be treated as another word for safe.
The less obvious use cases
The value of synthetic data is not limited to privacy or access. It can also help teams prepare for situations that are difficult to test using historical data alone. One example is stress testing. That means checking how a system behaves under difficult or unusual conditions. Another is the creation of edge cases. These are rare or awkward examples that may not appear often in past data but still matter in practice. These uses can be helpful in fraud detection, service operations, internal training, vendor demonstrations, and product testing.
This is where synthetic data becomes more than a substitute for unavailable records. It becomes a design and rehearsal tool. Teams can explore what happens if a pattern appears more often than expected. They can test what happens if a vulnerable group behaves differently from the average. They can also see how a workflow responds under pressure. In these cases, synthetic data is useful not because it perfectly reflects reality, but because it helps teams prepare for it more intelligently.
Using it well means being clear about purpose
The best way to judge synthetic data is to ask what task it is being used for. If the goal is to learn, test, prototype, collaborate, or build systems more safely in the early stages, synthetic data can be extremely effective. If the goal is to make high confidence claims about real populations, outcomes, or behaviours - It should not be used in a vacuum, only in preparation to using a real dataset.
That is why synthetic data deserves serious attention in both organisations and education. It creates a practical middle ground between no access and full access. Used well, it can support better project design, safer experimentation, and stronger learning. Used badly, it can lead teams to believe they have tested reality when they have only tested an approximation.
