The power of Test Data Management (TDM)

What is Test Data?

When we talk about test data, we refer to the data you create or import for your testing activities. It is the data you need to make sure your tests are as realistic as possible, from the initial parameters to the results you finally analyse. It allows testers to simulate scenarios and evaluate the response of an IT system. By an IT system, we mean an organised collection of software and hardware that achieves specific goals.

What is Test Data Management?

Of course, you need to organise all this data properly and you want to ensure quality. That's where Test Data Management (TDM) comes in. It ensures that your IT system, from applications to databases, meets all relevant data requirements.

Also read about test varieties.

3 key pillars

In Test Data Management (TDM), there are three essential pillars to watch out for:

• Data knowledge: Know your databases. Understand where data is stored and how it is structured, so you know exactly what test data you need.
• Data access: Manage access to production systems. Make sure you have access when you need it, but protect sensitive information at the same time.
• Data management: Keep your test datasets up to date. Regular updates and quality checks are crucial to ensure the reliability of your tests.

It is also important that your TDM strategy is in line with the needs of your entire organisation and specific projects. New project requirements can affect your TDM process, so make sure you are always up-to-date and flexible to changes.

The different data requirements

Test Data Management (TDM) is therefore all about creating, managing and maintaining test data effectively and efficiently. With this, you guarantee realistic tests without compromising the security, privacy and integrity of the data. But which data requirements should your test data meet?

• Relevance and completeness: Make sure your test data covers all scenarios and use cases, from the most common to the most unusual.
• Timeliness: Ever tested with outdated data and later realised you wasted time? Your test data should be current and reflect recent changes.
• Privacy and compliance: The AVG is leading here. Protect personal data by anonymising or masking real production data.
• Consistency: Nothing is more frustrating than inconsistencies in data. Make sure relationships between datasets remain intact, even after changes.
• Diversity: Ever been surprised by an unexpected input from a user? Your test data should represent a wide range of scenarios.
• Traceability: If you've ever struggled to analyse a defect, you know how important it is to have a clear link between test data and specific test cases.
• Manageability: Large datasets can be overwhelming. Focus on smaller, manageable datasets that still cover all necessary scenarios.
• Sustainability: Reuse saves time. Make sure you can reuse datasets in different testing phases.
• Accessibility: Ever spent a long time searching for specific test data? Ensure easy access, but at the same time protect against unauthorised viewing.
• Quality control: Just as you double-check your code, you should also check your test data. Guarantee that it meets the highest standards.

Test Data Management tools at your service

To make things easier for yourself, you can use Test Data Management tools. Tools such as DATPROF, IBM, EPI-USE, Broadcom, Informatica and Delphix help you with your TDM tasks. These help you create smaller data sets and generate data synthetically, allowing you to test faster and rely less on production data.

Also read about DevOps teams to test and implement software faster

How do you generate test data?

Generating test data was once simple: you copied production data. However, the AVG has changed the rules of the game. This legislation emphasises the protection of personal data, making direct copying risky. Instead, you can now opt for techniques like data masking, subsetting or synthetic data generation to stay both AVG-compliant and keep your tests realistic and reliable.

Subsetting

Subsetting is the process of selecting a representative subset of data from a larger production database. You can do this to reduce the size of the test database without compromising its integrity or usability. So instead of working with a giant database, you work with a smaller, manageable subset that still reflects the variability and characteristics of the full dataset.

Synthetic data generation

Synthetic data generation involves creating new test data that is not derived from real production data. The important thing here is that this data has the same structures and patterns as real data, but without the real data values. This is important when you need to ensure data privacy or when real data is simply not available for certain test scenarios.

Data masking

In data masking, you change the data so that it is no longer traceable to individuals, but the original structure is preserved. This allows you to test with realistic data without violating privacy laws. There are many techniques available, from simple randomisation to more complex methods, depending on what you need.

Conclusion

Managing test data effectively and efficiently is indispensable for quality assurance in IT projects. A well-thought-out Test Data Management strategy not only helps you ensure realistic and reliable testing, but also ensures compliance with privacy regulations such as the AVG. Want to delve deeper into this topic and take your quality control to the next level? Then take a look at our e-learning e-learning TMap for Cross-functional teams. In it, you will gain further insights and tools for your TDM strategy.

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