To use synthetic test data or to use test data masking — that is the question. But the answer may not be what you expect.
Before we dive into that: what’s happening in today’s business landscape that’s prompting the question around synthetic vs. masking?
Delivering high-quality applications at lightning speed is expected. Fast time-to-market is at odds with security and compliance requirements. The traditional paradigm of “speed, cost, or quality; pick two” has held companies back for years. It usually requires application teams to accelerate untested products to market, or worse, allow sensitive data to be used by non-privileged users for testing.
To solve this problem, the key lies in striking the right balance between realism and security in your testing environments. This is where two essential techniques come into play: test data masking and synthetic test data generation.
What Is Synthetic Test Data?
Synthetic test data is a class of artificially generated data unrelated to real-world events. It can be referred to as “fake test data'' or “simulated test data.''
Synthetic values are useful when no real data exists that matches schemas or when compliance regulations restrict production data access. Unlike masked or anonymized data, synthetic test data is not a transformation of production data. It is entirely artificial.
Another benefit of synthetic test data is that it mitigates security risks by using fake but equally effective data. Not only does it protect sensitive information, but it also greatly expands test coverage to allow for testing against broader sets of data that can often be complex — names, addresses, geolocation, credit card numbers, and beyond.
You can generate large volumes of synthetic test data quickly and easily, which can help accelerate development speed.
What Is Test Data Masking?
Test data masking is the process of replacing sensitive data with fictitious values that are realistic. Other ways you can refer to test data masking include static data masking, deidentification, scrambling, and PII (personal identifiable information) data masking.
Masked values are useful because they provide realistic data with referential integrity based on the actual values that are masked.
With masking, you can protect sensitive data which is critical for compliance with data privacy regulations and industry standards.
Test Data Masking vs. Synthetic Test Data: What’s the Difference?
The main difference between test data masking and synthetic test data is that masked data is a demographically accurate yet fictitious replacement for a real value while synthetic data is entirely artificial and may not capture the nuances and patterns of real data.
When to Use Test Data Masking and Synthetic Test Data
Test data masking and synthetic test data both have a time and place.
Use Test Data Masking to Shield Sensitive Data and Preserve Integrity
Imagine testing environments that can be kept up-to-date with production and mirror the richness and complexity of your production data — without ever exposing actual sensitive information. That's the power of test data masking.
Why is this important? Using real production data poses significant security risks. Using obfuscated encrypted data results in nonsensical values that aren't indicative of real-world functionality or performance.
By masking production data for testing, you’ll create a safe and compliant copy where sensitive values are replaced with fictitious, yet realistic, alternatives.
The best use cases for masking are for:
Data privacy: Customer data, financial information, and other confidential details are protected from unauthorized access.
Compliance: Meet stringent industry regulations (like GDPR, HIPAA, PCI DSS) without sacrificing testing accuracy.
Realistic testing: Evaluate application performance, functionality, and error handling against datasets that closely resemble real-world usage patterns.
Non-production network safety: Ensure that sensitive data is never present in non-production environments — without sacrificing speed or realism.
The 2024 State of Data Compliance and Security Report
66% of organizations we surveyed are using static data masking to protect non-production data. Discover insights from 250 global leaders around sensitive data, compliance, masking, AI, and more.
Use Synthetic Test Data to Tailor Data for Precise Testing
While masked production data excels in replicating real-world scenarios, synthetic data generation provides a powerful alternative.
The best use cases for synthetic data are when:
Real data is scarce or sensitive: Early in development, when testing new features, or exploring edge cases, real data might be limited or too risky to use.
Specific data characteristics are required: You need to generate data that precisely aligns with specific test cases, including unusual values, boundary conditions, or error scenarios.
Chaos testing: In testing, you not only want realistic data, but unrealistic data as well to ensure errors are caught and fixed before moving to production.
API testing: Create a wide range of inputs and expected outputs to thoroughly validate API functionality and error handling.
By generating synthetic test data, QA teams can achieve targeted testing. They can focus on specific application aspects or functionalities with data tailored to the task at hand.
Testing no longer must wait for a major release to be completed. It can be done at the feature level earlier in the development lifecycle even before real data is available. This flexibility also extends to unusual or niche scenario testing. QA teams can find potential issues that might be difficult to replicate with real data alone.
The Winning Formula: Test Data Masking + Synthetic Test Data Generation
So, what’s the secret to success for the world’s largest enterprises? A combined approach — masked data and synthetic data generation — will help you achieve the best results.
Masking production data for testing safeguards sensitive information in non-production environments, while synthetic test data generation fills in gaps and allows targeted testing of specific scenarios.
Synthetic test data for software testing can complement masking. And vice versa. Here at Perforce, we’re proud to offer solutions that help you do both.
Static (Test) Data Masking with Delphix
The Delphix platform provides powerful masking for production data in testing. These capabilities create secure, compliant copies of production databases, ensuring realistic, yet risk-free testing environments. Discover more >> What Is Delphix?
With Delphix, you’ll gain:
Data privacy and protection: Sensitive data will be masked, ensuring customer or patient privacy.
Compliance: Fulfill regulatory compliance requirements for masked data without sacrificing speed.
Realistic testing: Gain confidence that your test data is realistic and improve the quality of testing.
Non-production network safety: No sensitive data will ever reach non-production environments.
Synthetic Test Data Generation with BlazeMeter
BlazeMeter excels at automated, continuous testing and includes algorithm or AI-driven synthetic test data generation tools. This allows QA teams to build and execute comprehensive test suites for even the most complex applications.
With BlazeMeter, you’ll gain:
Data you can use now: Use synthetic data early in development, when testing new features, or when exploring edge cases.
Data for every testing requirement: Generate data that aligns with specific test data, regardless of what real production data you have available.
Chaos testing: Embrace chaos testing. Leverage AI and built-in rules to create negative counterparts of the expected “happy path” data. Use mock services to support unexpected scenarios.
API testing: Create API tests in minutes, as well as monitor APIs from development to production.
Delphix + BlazeMeter: Better Together
By embracing both test data masking and synthetic test data generation, your company can strike the right balance between realism and security in test environments. You’ll no longer be stuck in the traditional paradigm of “speed, cost, or quality; pick two”. Your company can confidently accelerate software development lifecycles while upholding the highest standards of data security and compliance.
That’s why Delphix and BlazeMeter are better together. Contact us to learn more.