Using Synthetic Test Data vs Test Data Masking for Performance Testing

When it comes to delivering high-quality software applications, robust test data is essential. It forms the backbone of effective testing and enables your application to perform as expected under varying conditions.  

However, working with sensitive data often brings risks related to compliance and security. Mishandling sensitive information like credit card numbers, healthcare records, or personal addresses can lead to serious regulatory repercussions and loss of brand trust.   

This is why strategies that utilize synthetic test data and test data masking have emerged as critical approaches for quality assurance (QA) teams. While both help overcome security hurdles, they serve distinct purposes and work best in specific scenarios.  

This blog provides an overview of synthetic test data and test data masking, the differences between the two, and how they can complement each other to streamline your performance testing.   

What is Synthetic Test Data?

Synthetic test data is artificially generated data that mimics real-world data in structure and behavior. It does not contain any actual sensitive or personal information. For example, you can use synthetic test data to replicate address patterns, credit card numbers, social security numbers, or geolocation data without any real-world associations.

The primary use of synthetic test data is for when specific data patterns or datasets are required to test an application's capabilities effectively. Examples of this include: 

• Testing name recognition systems with diverse names from multiple countries.   

• Simulating user behaviors based on geolocation data to test map-based applications.   

• Creating unique test cases involving random, yet valid, credit card numbers for payment gateway testing.   

• Crafting expected but non-existent scenarios to test specific edge cases.   

The major advantage of synthetic test data is its flexibility. Developers can create customized data patterns that meet the exact needs of the test environment, which improves the precision and coverage of their tests. Since the data is artificially generated, it inherently avoids compliance risks related to sensitive information.

What is Test Data Masking?

Test data masking anonymizes or obfuscates sensitive, real-world data to protect it from unauthorized access. It replaces original data with non-identifiable data while retaining the usability for testing. Examples include replacing customer names with pseudonyms, scrambling phone numbers, or generating fake healthcare identifiers.

Test data masking is especially useful in production environments where companies need to extract datasets from live systems for testing purposes. Examples of these scenarios include:

• Finance teams testing systems with real transaction records that must be anonymized to meet PCI DSS compliance.   

• Healthcare organizations using patient data for application testing while adhering to HIPAA regulations.   

• Enterprises needing to test CRM systems with real data while ensuring GDPR compliance.   

The benefit of test data masking lies in its ability to preserve data integrity. Since masked data is derived from live environments, it closely mirrors real-world data patterns — making validations and system behavior testing highly realistic.

Synthetic Test Data vs Test Data Masking

When considering whether to use synthetic test data or test data masking, it would be helpful to see how they compare side-by-side. 

When Should You Use Each?

• Synthetic Test Data is ideal when you need complete flexibility and customization in your testing environment. For instance, when testing extreme edge cases or creating data for entirely new features.   

• Test Data Masking is preferred when working with legacy systems or performing regulatory-compliant testing on production datasets.  

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Synthetic Test Data and Test Data Masking for Performance Testing

When it comes to performance testing, combining masked and synthetic data can also optimize results. Below, you can find how to effectively use both to unlock powerful performance insights.   

Steps for Performance Testing with Test Data Masking and Synthetic Test Data

1. Diversify Data Sources: Use masked production data to simulate real-world scenarios while adding synthetic data for edge cases and stress testing.   

2. Scale with Synthetic Data: Generate large data sets to test how the application performs under heavy traffic or high volumes of transactions.   

3. Protect Production Integrity: Ensure masked data complies with privacy requirements to avoid the risks of exposing sensitive information during testing.   

By using both strategies, teams can generate robust test data that is secure, comprehensive, and scalable — leading to better application resilience and performance.

Explore BlazeMeter Synthetic Test Data and Delphix Data Masking  

If you are looking to optimize your test data approach, using the right tools based on your resources is the key.   

• BlazeMeter Synthetic Test Data offers an intuitive platform to generate artificial datasets tailored to your specific testing requirements. With advanced configurability, BlazeMeter empowers QA teams to create synthetic data that mimics real-world conditions—without the risk.   

• Delphix Data Masking brings world-class capabilities in safeguarding sensitive production data. Whether it’s GDPR, HIPAA, or PCI compliance, Delphix ensures your team can securely utilize production data for testing by anonymizing it in real time, minimizing risk without compromising utility. 

Bottom Line

Test data management does not have to be a challenge. Understanding when to use synthetic test data and when to rely on test data masking can significantly boost your testing strategy. Whether testing new systems or safeguarding sensitive data, these approaches provide the flexibility and security needed to drive high-quality software development.   

Want to elevate your test data processes and achieve compliance peace of mind? BlazeMeter and Delphix have the tools to help you transform how you approach test data.