Synthetic data vs. 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 production data may seem like the most realistic 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 exposed. Furthermore, using real production data can introduce inconsistencies or conflicts when applied across different testing scenarios, potentially impacting test results and reducing repeatability.
To address these challenges, synthetic or anonymized data offer safer alternatives. Synthetic data is completely generated and never linked to real users, enabling you to create large datasets tailored to specific scenarios without compromising privacy. Anonymized data, in contrast, is based on real production data with sensitive elements masked, allowing for realistic testing that still respects data protection regulations.
In this article, we’ll dive into the differences between synthetic and anonymized data, examining when to use each type to maximize test reliability, scalability, and security across all phases of your project.
WHAT IS SYNTHETIC AND ANONYMIZED 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 quality. But what are the distinguishing characteristics of synthetic and anonymized data?
Synthetic data
Synthetic data is artificially generated data that mimics real data without using actual production data.
Its main advantage is that it can be tailored to specific test scenarios and generated in large quantities without risking data privacy or security. It is easy to control synthetic data and ensure it remains consistent across tests, which supports repeatable and reliable test results.
However, synthetic data has some risks. Since it’s artificially created, there’s a chance it may not capture all real-world complexities, potentially missing critical scenarios or dependencies found in actual data. This can lead to test results that don’t fully represent real usage patterns. Additionally, if the generation process is flawed or biased, synthetic data could misrepresent certain behaviors, impacting test accuracy.
You have to care for your process, and it should be correct. Otherwise, you risk compromising the reliability of your data, which can lead to unreliable test results and hinder 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.
However, using anonymized data also come with certain risks. If the anonymization process is not thorough enough, there is a chance that sensitive information could be re-identified, especially when combined with other datasets. Additionally, anonymized data may still contain patterns or dependencies that can limit flexibility, making it challenging to adapt for diverse testing scenarios. It also requires extra steps and processes to ensure that the data remains compliant with privacy regulations, which can be resource-intensive. Lastly, anonymized data can carry inherent biases from the original dataset, potentially skewing test outcomes if not carefully managed.
WHEN TO USE SYNTHETIC DATA OR ANONYMIZED DATA
Depending on your testing phase, you may need to use different types of test data. Choosing the right type of test data for each testing phase improves test quality and ensures compliance with security and data protection requirements. Select 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 here.
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 anonymized data based on actual data structures provides a strong foundation.
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.
