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.