Test Data Management Best Practices (Or TDM Process) 

The importance of test data management (TDM) is ever-increasing if you want to avoid data compliance risks and subsequent fines. The combination of enormous fines, such as the one British Airways faced after their data breach, and the overall growing number of data breaches indicates that we need proper data protection mechanisms.

In this article, we list six of the most important test data management best practices. The importance of these practices can’t be understated given that in the last few years a growing number of companies have been fined for failing to protect their data.

Data breaches are an important reason to have a well-defined TDM process. But managing data compliance risks are not the only benefit TDM offers. Test data management focuses on generating high-quality test data for testing your application’s functionality and detecting bugs. Test data management helps your organization with tasks such as test data selection, generation, protection, and maintenance. Speeding up the overall test process will also reduce costs in a number of different ways that we’ll explain in this article.

The 6 Test Data Management Best Practices

1. Comply With Data Regulations

We already mentioned at the beginning of this article that data regulations are an increasingly important concern for every company that manages data. GDPR and data privacy make test data management indispensable.

Compliance regulations require that all production data must be protected. This means that data leaving production must be masked or otherwise protected, because although the production data is very often safe, copies used as test data can end up with malicious third parties through negligence. Test data management is an important piece of the puzzle in protecting sensitive data.

2. Data Planning

When you test your application you should keep in mind that you can’t use all data. Plan to select relevant data that fits best for testing your application in a variety of scenarios. For example, use actual banking data for verifying a payment flow scenario.

Planning ahead makes sure testing happens in a standardized way. Standardized tests with rich data are also crucial for testing edge scenarios and ensure you’ll detect as many bugs as possible. A side effect of data planning is that it also helps reduce the overhead in managing your test data. For example, when you plan to regularly update your test data or check if the data still represents the production data model, you won’t run a bunch of tests and then realize your test data was outdated.

3. Faulty Data Generation

Test data management is not only about data extraction. It’s also about data creation. The goal is to generate data that is as close as possible to your users’ data. That way you can make sure you test for errors they might encounter when using your application.

But you should also create faulty data to test failure scenarios and verify if your application returns the correct error messages or reacts appropriately when users make mistakes or internal errors occur. If you test only success scenarios, you won’t know how your application will react when users enter faulty data.

4. Don’t Forget Data Maintenance

Once you’ve generated test data, you must remember to regularly update your test data. Your test data should always be relevant to your production data. Since your business isn’t static, you need to keep in mind that your data isn’t either. As your business progresses, so do the data models for your application.

If data models aren’t correctly represented in your test data, your test phase may be meaningless. For example, if a website’s client model changes to include authentication through social media, this needs to be reflected in the test data. Otherwise, bugs introduced when you added this feature could be completely overlooked during testing. This would provide the end-user with an unstable experience.

Having a reusable way to generate time-sensitive data will make your testing more efficient. For example, all time-sensitive data requires maintenance, such as updating timestamps.

5. Document Your Test Data Management Process

Although this best practice doesn’t exclusively apply to the testing phase, its importance cannot be overstated. Just as in other development phases, the test data management process must be well documented. Proper documentation helps other team members understand the TDM process so they can make quick changes to the process when needed.

Documenting your TDM process also ensures that you won’t have to fully rely on your test data manager for the entire TDM process. That will prove valuable in case they get sick or leave the company. Documentation is an immensely important artifact for transferring knowledge.

6. Refresh Test Data Regularly

Refreshing test data regularly can be part of your data maintenance strategy. However, it deserves its own mention because many organizations forget to refresh their test data, always testing with the same data. Always testing with the same data will leave some bugs undetected. Once you have a standardized TDM process, the best way to benefit from it is to refresh your test data regularly. Like data planning, frequently refreshing your data helps you cover a lot of edge cases and present your application to users with fewer bugs.

It’s important to understand that updating and refreshing are two different concepts. Updating refers to making sure your test data continually resembles your production data, while refreshing refers to regularly creating new data to test the application scenarios.

What’s the Importance of Test Data Management?

TDM offers several advantages, aside from avoiding astronomical fines. Its function is not merely protecting sensitive data, but it also saves you money in several different ways, as we explain here.

• High-quality data ensures you can detect bugs more easily. This happens because with proper test data management your test data always resembles your production data as closely as possible. It would be pointless to use meaningless data to test your application. That would be like testing an orange juicer with a pear.
• Good test data management also ensures a faster time to market. That’s because it allows for faster verification of test scenarios, which in turn means you can release your product more quickly.
• Proper test data management also means you’ll be ready with quality test data whenever you need to run tests. The goal is to effectively test your application without having to worry about whether the test data itself has been maintained.

Conclusion

As you’ve seen in this article, investing in a reusable, standardized test data management system can reduce your costs down the line. We explained that TDM best practices, such as masking sensitive data to protect it, can shield you from massive fines imposed by regulating bodies.

Also, we saw that generating high-quality test data speeds up your testing process and saves you time, because you’ll have fewer bugs to solve when your application is live. More importantly perhaps, it allows you to present your users with high-quality software.

Improving the overall test phase through a defined TDM process reduces the time to market for each version of your application. A final point we stressed is the importance of documentation. You don’t want to rely on one employee to know all of the information about your TDM process. If that employee leaves, you risk having a huge knowledge gap. This can cause your organization to lose its competitive advantage in the market.

Want to learn more about test data management? Check out the following related blog posts:

• Why is test data management important?
• Test Data Management (TDM): A Complete View
• Fundamentals of test data management by IBM

Post Author

This post was written by Alexander Fridman. Alexander is a veteran in the software industry with over 11 years of experience. He worked his way up the corporate ladder and has held the positions of Senior Software Developer, Team Leader, Software Architect, and CTO. Alexander is experienced in frontend development and DevOps, but he specializes in backend development.

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