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, 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.
According to the State of Data Compliance and Security Report, respondents use synthetic data for:
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:
| Use Case | Synthetic Data Generation | Data Masking | Explanation |
|---|---|---|---|
| Testing unique/new scenarios where no data exists | ✅ | ❌ | Synthetic data is purpose-built for unique/new scenarios. |
| Scenario testing to unblock development and speed dev/test cycles | 🤝 | 🤝 | Pairing synthetic and masked data will speed scenario testing. |
| End-to-end testing that requires consistent relationships across systems | 🤝 | 🤝 | Synthetic data plus masking with referential integrity support the full software development lifecycle. |
| Production-like copies for realistic late-stage testing | ❌ | ✅ | Masking sensitive fields will help you keep production-like structure and values. |
| Sharing data broadly with reduced exposure risk | ✅ | ✅ | Using synthetic and/or masked data will reduce risk, and synthetic ensures data stays in a customer environment. |
| Governed pipelines where synthesis and masking must follow policy | 🤝 | 🤝 | Synthetic data generation and masking applied per policy will help companies maintain unified governance. |
| Ephemeral cloud data environments | 🤝 | 🤝 | Both synthetic data and masked data can be space-efficient when delivered ephemerally. |
| Debugging a specific production incident that requires exact reproduction | ❌ | ✅ | Synthetic data generation cannot replicate exact scenarios or related data. |
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 are Your Peers Using Synthetic Data?
According to our State of Synthetic Data mini-report, 36% of respondents use synthetic data in small scale and experimentation mode, and that’s just one insight from the 280 global leaders surveyed. See how these organizations are utilizing synthetic data in their software, data analytics, and testing environments.
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.
Synthetic Data Generation FAQs
The realism of synthetic data depends on how it’s generated. If you simply request a set of data from, say, ChatGPT, there’s no guarantee that the data will make sense. For example, it could generate an order shipped date that occurs before the order placed date. If you work with an effective synthetic data solution, it should maintain both realism and referential integrity.
Yes, using both synthetic data and masking can mitigate data privacy risk and support test data management efficiencies. You may choose to mask your production data for testing and then realize you don’t have enough data for your use case. Synthetic data can fill those gaps.
Yes, many synthetic data generation solutions use AI to create new data. As mentioned above, if an AI is not built with the purpose of generating high-quality data, it may not provide realistic or useful data for testing or DevOps use cases.
How Perforce Delphix Can Support Your Synthetic Data Strategy
Accelerate Innovation with AI-Powered Test Data Management
Perforce Delphix, a Customers’ Choice in the Gartner® Peer Insights™ 2025 Voice of the Customer Report for test data management, will enable you to have faster, higher-quality application releases. Its capabilities give you cost-efficient test data delivery in any environment, so you can eliminate bottlenecks in your DevOps processes. Customers of Delphix have experienced 58% faster time to develop an application, according to a recent IDC study*.
Reduce Data Privacy Risk with Synthetic Data and Masking
Get trusted data for faster, safer innovation. Leveraging both synthetic data and static data masking with Delphix will ensure a well-rounded approach to compliance, minimizing the amount of production data in non-production environments. By automating masking for DevOps pipelines and provisioning synthetic data, you’re reducing regulatory and security risk with ease. Thanks to Delphix, 77.2% more data and data environments were masked and protected*.
See Delphix Synthetic Data in Action
Learn how Delphix synthetic data could fit into your overall test data management strategy. Request a no-pressure demo today.
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*IDC Business Value White Paper, sponsored by Delphix, by Perforce, The Business Value of Delphix, #US52560824, December 2024