Synthetic Data Generation: Methodology, Use Cases, and What You Need to Know

Many companies have integrated synthetic data into their overall test data management process. Our 2025 Perforce Delphix State of Data Compliance and Security Report found that 63% of surveyed global enterprises use it to protect sensitive data in non-production environments.

Whether you’re currently or contemplating using synthetic data generation (also known as artificial data generation), it’s important to understand its best practices, benefits, and risk considerations. Here’s what you need to know.

What is Synthetic Data?

Synthetic data is artificial data, made to resemble real production data. It’s often made using statistical methods or generated by AI.

Synthetic data differs from data masking because it’s completely new. While some forms of masking will replace real data with fictious data, synthetic data generation creates entirely new data from scratch.

Use Cases for Synthetic Data Generation

While synthetic data is often useful, it does not fit every scenario. Here’s a look at a few use cases for synthetic data generation:

Benefits of Synthetic Data Generation

While synthetic data is often useful, it does not fit every scenario. Here’s a look at a few use cases for synthetic data generation:

• Customization: You can tailor synthetic data to your exact testing needs, matching required formats, distributions, and relationships while intentionally dialing up rare conditions. Teams can generate domain-specific datasets (including new-feature scenarios and edge cases) on demand, without being constrained by what production happens to contain.
• Efficiency: Synthetic data generation gets teams data they need when they need it, speeding up application and new feature development. This eliminates waiting and test data delivery bottlenecks.
• Increased Data Privacy: Data breach risk and consequences are both reduced with synthetic data, as it’s not attributable to actual people. Synthetic data helps minimize sensitive data sprawl and the amount of production data present in non-production environments.
• Richer Data: Real data can be scarce, and missing test results can lead to false positives or negatives. Synthetic data generation can help fill gaps for edge and corner cases, preventing any impact on release quality.

Synthetic Data Generation Concerns

Even with all the benefits that synthetic data offers, it’s important to note what challenges and concerns to look out for. If you’re leveraging synthetic data generation, be sure to:

• Use synthetic data purposefully and only for select use cases. Real, masked data is better suited for functional testing and debugging.
• Evaluate the quality and realism of your synthetic data. Check that the data does not have errors or nonsensical information, such as bad formats or ranges like ZIP codes with letters or negative account balances when they’re not allowed.
• Pair your synthetic data with data masking. To get the most out of your test data management strategy, use both masked and synthetic data. Doing so will reduce your data security risk and increase the amount of data at your disposal. 

How to Generate Synthetic Data  

There are many ways to generate synthetic data, including:

• Generative AI Generation: Artificial intelligence uses algorithms trained on data samples to create new, synthetic data.  
• Rules-Based Generation: Defined logic, constraints, or business rules, established by the user, helps generate synthetic data.
• Random Data Generation: This method generates data in a way that mimics a data structure but may not reflect real-world data.
• Entity Cloning: Different from the others, this method makes exact copies of an existing entity.

However, with the boom in AI, it has become a go-to method for generating synthetic data. Delphix, for example, uses this method. We use AI to generate customized, high-fidelity synthetic data, which you can use to ensure data security and optimize your teams’ test data management strategy at enterprise speed.