Synthetic data has become a common tool in enterprise IT, particularly when teams encounter privacy, security or regulatory barriers. In my experience, synthetic data shows up when a development team needs access to user data but can’t get it. It offers a way to keep projects moving by generating risk-reduced data sets when the real thing is off-limits.
It’s important to understand where synthetic data delivers real value and where it creates new risks or challenges. Getting this right is key for any organization trying to balance innovation with responsibility.
The benefits of synthetic data
Synthetic data offers clear advantages when real-world data is locked away behind privacy rules, compliance restrictions or contractual delays. For teams under pressure to test, develop or validate systems, synthetic data can fill critical gaps and keep work on track.
One of the most common benefits I’ve seen is in early-stage development. Teams can use synthetic data sets to prototype features, test performance or check integrations without waiting for sensitive production data. This can prevent long delays, especially if legal teams are still negotiating access rights or nondisclosure agreements.
Synthetic data also plays a key role in industries subject to heavy regulation. In healthcare, for example, it allows developers to train models without handling protected health information. For example, when working with medical images, teams often need anonymized versions to ensure that no patient-identifiable details are exposed. In that case, synthetic data still allows meaningful testing and model training. In finance, it supports testing systems without exposing customer transactions or account details.
Synthetic data enables the generation of large, diverse datasets, which can be challenging to achieve using operational systems alone. This expanded scale is especially valuable when training or stress-testing machine learning models, where having more varied data typically improves performance and reliability.
Finally, synthetic data reduces privacy risks when shared across teams or partners. Even when real data can’t leave a secure environment, synthetic versions can be passed around more freely, supporting collaboration across departments or with external vendors.
The challenges of synthetic data
While synthetic data offers real benefits, it also comes with limitations that enterprise teams need to understand.
One challenge is that synthetic data often lacks the subtle complexity and edge cases found in real-world data sets. This becomes even more pronounced with agentic AI systems, which are designed to make autonomous decisions and adapt over time. When these systems are trained too heavily on synthetic data, they can experience model breakdowns, generate flawed outputs or start reinforcing artificial patterns that don’t hold up in real-world conditions.
There’s also the risk of over-reliance. Some teams assume that synthetic data can fully replace real data, but that’s rarely true. Synthetic data sets are most effective when used alongside real-world inputs, not as a complete substitute.
Another concern is the risk of privacy leakage, particularly when working with synthetic data sets that retain some statistical traces of the original source. If outliers or unique identifiers aren’t properly handled, it becomes possible to trace synthetic records back to real individuals or transactions, reintroducing the very risks synthetic data is meant to avoid.
Finally, creating high-quality synthetic data is not simple. It requires thoughtful design, careful validation and ongoing monitoring. Poorly generated synthetic data can introduce hidden biases, distortions or gaps that degrade the quality of any models or systems trained on it.
Best practices for using synthetic data
To get the most out of synthetic data without introducing risks, enterprise teams should follow a few key principles.
First, synthetic data should complement real-world data, not replace it. While synthetic data sets are useful for prototyping, early testing or overcoming access delays, they should be paired with real data for validation and final model training. This balance helps ensure models remain grounded in real-world complexity and don’t fall into synthetic feedback loops.
Second, be rigorous about privacy. Even partially synthetic data can retain traces of the original source, especially when outliers or rare events are present. Teams should apply strong de-identification practices, removing or smoothing out unique records that could be linked back to individuals or sensitive transactions.
Third, maintaining synthetic data quality requires continuous attention, since generating it is never just a one-time task. It requires careful design, regular validation and ongoing checks to make sure it continues to meet the needs of the system it supports. This includes watching for hidden biases, gaps or distortions that can quietly erode model performance.
Finally, manage the original source data with care. Synthetic data sets are often generated from sensitive real-world data. Once the synthetic version is created, teams should securely delete or isolate the original data sets to reduce exposure risk. Leaving sensitive source data lying around increases the chances of accidental leaks or misuse.
What enterprise leaders should remember
Synthetic data has earned a place in the enterprise toolkit, offering a practical way to navigate privacy, compliance and access challenges. But like any tool, its value depends on how carefully it is applied.
Enterprise IT leaders need to approach synthetic data with clear eyes, recognizing both its potential and its limits. When it is paired with real-world validation, strong privacy practices and thoughtful oversight, synthetic data can help organizations push innovation forward while respecting the boundaries that protect sensitive information.
