Synthetic Data: Innovating and Testing Without Putting Real Data at Risk

Every innovation team has run into the same obstacle at some point: developing or testing a new data service requires a realistic dataset, but the organization's real data is subject to access restrictions, confidentiality agreements, or simply can't leave a controlled environment. The usual result is a bottleneck that delays entire projects while permissions are negotiated or one-off anonymizations are awaited.

Synthetic data solves this problem from another angle: instead of protecting real data by reducing its detail, it generates an artificial dataset that reproduces the relevant statistical properties without corresponding to any real person, transaction, or facility. For innovation teams, data scientists, and developers, this completely changes the speed at which they can experiment.

In this article, we explain what synthetic data is, in which specific use cases it adds the most value within a data space, what generation techniques exist, and what its real limits are in terms of fidelity and privacy.

What Synthetic Data Is and How It Differs From Anonymized Data

Anonymized data starts from a real record and transforms it to reduce the risk of re-identification, but it remains linked, even if indirectly, to a real case. Synthetic data, on the other hand, is generated from scratch using statistical models or learning algorithms that learn the patterns, distributions, and correlations of an original dataset, and then produce new records that don't correspond to any real case, but that behave statistically in a similar way.

This difference matters for data teams because it changes the starting point of the risk analysis: when the generation process is well designed, synthetic data doesn't contain personal information because it doesn't describe any specific person, which makes it much easier to use in early development stages or in contexts where the real data couldn't even be shared in anonymized form.

Use Cases for Synthetic Data in a Data Space

Testing and development environments

The most common case is also the most practical. A development team building a new data app for the data space needs a dataset with realistic volume and variety to test its processing logic, but has no reason to work with real data during this phase. Synthetic data makes it possible to set up the testing environment from day one, without waiting for authorization to access production data.

Consider a technology company in Castile and León developing a quality-control data app for municipal management datasets. Before having access to any city council's real data, the team can generate a synthetic dataset with the same field structure, the same expected types of errors, and comparable volumes, and thus validate its business rule logic with complete freedom.

Commercial demonstrations

When an organization wants to show a potential client how a data service works, it isn't always appropriate or possible to use real data from other clients or from the organization itself. Synthetic data makes it possible to build realistic, convincing demonstrations without needing to anonymize on the fly or sign additional confidentiality agreements just for a sales demo.

A data analytics consultancy that wants to present its capabilities to a regional administration can generate a synthetic dataset of public management indicators, with the same shape and complexity as real data, to illustrate its dashboards without compromising any third-party information.

Training machine learning models

Data scientists training machine learning models often need larger data volumes than what's available, or need to cover scenarios that are rare in the real dataset. Synthetic data makes it possible to expand the training set by reproducing the same statistical distributions, speeding up model development without waiting to accumulate more real historical data.

Balancing datasets

A recurring problem in machine learning projects is class imbalance: for example, a facility-maintenance dataset where serious incident cases are very scarce compared to normal cases. Generating synthetic records for the minority class, while respecting its statistical patterns, makes it possible to balance the training dataset and improve the resulting model's performance, without inventing real cases that never happened or exposing the few real cases that do exist.

Synthetic Data Generation Techniques

Different technical approaches exist for generating synthetic data, and the choice depends on the type of data and the level of fidelity needed.

• Parametric statistical models: fit a known distribution (normal, Poisson, etc.) to the original data and generate new values from that distribution. They're fast and easy to audit, though they may not capture complex relationships between variables.
• Generative learning-based models: learn more complex patterns and correlations across multiple variables from the original dataset, then generate synthetic records that preserve those relationships with greater fidelity.
• Perturbation-based sampling: starts from real records and applies controlled variations, generating new plausible values from the existing ones. Requires special care to avoid ending up with records that are too close to the originals.
• Domain rule–based generation: combines expert sector knowledge with random generation within valid ranges, useful when there isn't a sufficiently large original dataset to learn patterns from.

In a modern data space, synthetic data generation is offered as just another data app, deployable in a Docker container, which can take a real dataset available through the connector as a reference and produce a synthetic version ready for use in testing or demonstrations, without the original data ever leaving its control perimeter.

Fidelity and Privacy Limits You Should Know

Synthetic data isn't a magic solution, and it's worth being cautious about its limits.

Fidelity limits

The simpler the generation technique, the easier it is to audit, but the lower the statistical fidelity to the original dataset. A more sophisticated generative model can capture richer relationships between variables, but it's also harder to interpret and validate. Before considering a synthetic dataset valid for training a production model, it's worth comparing its main distributions and correlations against those of the original dataset using statistical tests.

Privacy limits

A less obvious but real risk is that, if the generation technique isn't well calibrated, a generative model can memorize specific records from the original dataset and reproduce them almost verbatim among the synthetic data — especially when the source dataset is small or contains very singular outliers. That's why, especially with sensitive data, it's advisable to combine synthetic generation with additional checks to ensure no generated record is excessively close to a known real case, and to consider whether the process should be complemented with differential privacy techniques during the generation phase.

When synthetic data isn't enough

For certain use cases, such as analyzing a very specific phenomenon that depends on subtle real-world correlations, synthetic data may not be sufficient, and it will be necessary to rely on real anonymized or pseudonymized data under the corresponding usage agreement. Synthetic data is a powerful complement for accelerating development and experimentation, not a universal substitute for real data at every phase of a project.

Accelerate Your Innovation Without Exposing Real Data

If your team needs to move forward with developing data services, training models, or preparing demonstrations without waiting to resolve every real-data access permission, synthetic data within a data space is the fastest and safest way to do it. If your organization is considering taking this step, look for a technology partner with experience in data spaces who can help you generate your own synthetic datasets from your real sources.