Any change in data, whether it’s via data cleansing, migration, or transformation, will require quality assurance processes to ensure the data remains productive and accurate. Data testing can be the key to achieving data certainty and is a key step in launching new software that performs.
The data you choose to use within your testing processes should be very carefully considered, as it’s crucial that you minimize the risk of unauthorized access, particularly when it comes to sensitive data. IDS discover more about test data management and how to protect your data without compromising on your organization’s testing abilities.
What happens if you use real data in test environments?
The risk of using real data in testing is huge, especially in regulated industries such as banking and healthcare. This can pose a challenge, as production data is always going to be the best choice for your test environments, but it can leave your data open to cybercrime and GDPR regulations.
If data was lost or it fell into the wrong hands during testing, your organization could be liable for penalties and fines. So, it’s important that you find a process by which you can protect your sensitive data, which is often done by creating test environments with false or masked datasets.
Having a robust test data strategy
It’s in the best interest of your business to protect its data as well as adhering to regulations. This can also help your organization to avoid any surprises or issues once your system or application goes live.
Test data management can increase confidence in your quality engineering and testing practices, knowing that you aren’t dealing with sensitive data. Consider finding test data that are as close to production as possible, to achieve a realistic testing environment with much less risk.
Data obfuscation and data synthesis
You can achieve effective test data in two ways, data obfuscation and data synthesis.
Data obfuscation is the process of taking real data and changing it, so that any sensitive data is not revealed. This can provide you with test data that is as close as possible to the original, which can result in more accurate testing. With that in mind, this method can carry some risks.
However, generally speaking, data obfuscation will mean that even if there was a data breach, the real data is less likely to become compromised. There are different methods of obfuscation, including data masking, encryption, and tokenization. Data masking is often the most popular method, which substitutes realistic but false data to obscure the real meaning of the data itself.
Data synthesis, on the other hand, is the process of using fabricated data. This is usually nothing like the real data which poses much less of a risk than obfuscation. However, it also means your test data could be even less accurate.
Synthetic data can be helpful in a number of scenarios, particularly when training new AI systems. For instance, fraud detection systems within banking or finance can be trained using data synthesis to avoid exposing real financial data.
Finding the right tools to help you achieve test data management could be the key to a safe testing environment.
