Choosing Your Test Data

Synthetic v.s. anonymized data: what’s the difference? And which is better for your software testing needs?

In the software development lifecycle, testing with reliable and secure data is essential for ensuring quality and compliance. While actual production data may seem like the most natural choice, using it directly when testing comes with significant risks. Production data often contains sensitive information—such as customer details or transaction records—which could lead to privacy breaches and regulatory violations if accidentally exposed. There's also always a risk that actual production data can cause inconsistencies or conflicts when applied across different testing scenarios, potentially impacting test results and reducing repeatability.

So what are the alternatives? Well, in short, synthetic and anonymized data are two alternatives that are considered safer than production data. Synthetic data is entirely generated. Therefore, it's never linked to any real users, enabling you to create large datasets tailored to specific scenarios without taking any privacy risks. Anonymized data, on the other hand, is based on real production data but with sensitive elements masked. That way, you can perform realistic testing that still respects data protection regulations.

In this article, we’ll dive into the differences between synthetic and anonymized data and examine when to use each type to maximize test reliability, scalability, and security across all phases of your project.

THE IMPORTANCE OF CHOOSING THE RIGHT TYPE OF DATA

Test data is essential in every phase of the software development and testing process. It enables you to simulate realistic scenarios, identify errors, and ensure that your software is of high enough quality. But what are the distinguishing characteristics of synthetic and anonymized data? And how do you know when to use what type of data? Let's take a closer look.

Synthetic data

Synthetic data is artificially generated and mimics real data without using actual production data from your users.

Its main advantage is that you can tailor it to specific test scenarios and generate it in large quantities without taking any data privacy or security risks. It's also easy to control synthetic data and ensure consistency across tests, which, in turn, supports repeatable and reliable test results.

However, synthetic data comes with some risks of its own. Since it's artificially created, it may not capture all real-world complexities, potentially missing critical scenarios or dependencies that would have been easily spotted with production data. This can lead to test results that don't fully represent real-life situations. Additionally, if the process that generates the data is flawed or biased, the data might misrepresent certain behaviors, which would then also impact test accuracy.

In other words, for synthetic data to be useful, you need to take great care of your data generation process and make sure it's unbiased and correct. Otherwise, you risk compromising the reliability of your data, which can lead to unreliable test results and hinder your overall software quality.

Anonymized data

Anonymized data is real production data with sensitive elements masked to protect personally identifiable information (PII).

Anonymized data has the advantage of being based on data quality, provided the test data is correct. Often, however, we know that our data quality is not good enough to be used as a reliable test data source. This can lead to side effects such as false positives or false negatives if we do not have control over our test data.

And, of course, using anonymized data also comes with certain risks of its own. If the anonymization process isn't thorough enough, there's a risk that sensitive information could be re-identified, especially when combined with other datasets. 

Additionally, anonymized data may still contain patterns or dependencies that might limit its flexibility when you apply it to other contexts. That means that it can be difficult to use in diverse testing scenarios. Due to these risks, using anonymized production data requires several extra steps and processes to ensure that the data remains compliant with privacy regulations throughout your testing. That in and of itself can be quite resource-intensive. There's also always a risk that anonymized data can carry inherent biases from the original dataset, potentially skewing test outcomes if not carefully identified and managed.

WHEN TO USE SYNTHETIC DATA OR ANONYMIZED DATA  

The type of data that's most suitable for you might vary depending on what test phase you're currently in. Choosing the right type of test data for each testing phase will help improve your overall test quality and ensure compliance with security and data protection requirements. In other words, you need to choose the type of data that best meets the needs of each test phase.

Early test phases

Synthetic data is frequently used to verify individual functions or modules where realism is less important.
Why? Synthetic data can be generated to cover specific test cases, allowing for consistent and repeatable tests to quickly identify error sources.

System integration tests

Both synthetic and anonymized data are often used for system integration tests.
Why? Anonymized data reflects real data relationships and dependencies, while synthetic data is helpful when anonymized data is unavailable or insufficient.

End-to-end tests

Anonymized data (preferred) or synthetic data when real data access is limited.
Why? End-to-end tests require realistic data for simulating complete business processes across multiple systems. So, if there's enough of it, anonymized data based on actual data structures provides a great foundation for this type of testing.

Load and performance tests

Synthetic data is ideal, as these tests require large volumes of data.
Why? Synthetic data can be generated in large quantities without violating data protection regulations, allowing flexibility and quick data volume adjustments.

HOW SYSTEM VERIFICATION CAN HELP

At System Verification, we’re well-acquainted with the specific challenges energy companies face in managing sensitive data throughout their testing processes. For instance, energy providers need to mask customer-specific data such as personal identification numbers, installation IDs, and facility IDs to comply with GDPR. Additionally, certain internal company information may be highly sensitive and protected by NDAs, requiring stringent handling to ensure it remains confidential, even within development teams.

Our experts can assist you in designing secure test data pipelines that meet these strict privacy requirements. We work with you to implement processes that generate synthetic or anonymized data at key points in your testing workflow, ensuring data integrity without compromising sensitive information. By tailoring solutions to your unique requirements, we ensure the use of suitable data that aligns with your testing needs.