Test Data Management: A Guide to The What, Why, and How


Despite TDM being one of the many challenges that threaten software companies, there’s hope in the form of AI-assisted tools.

The software development field changes so fast it’s hard for companies to stay afloat. In this highly competitive scenario, having a proper testing strategy in place is essential. And a sound testing strategy comprises many components, one of which is test data management. And that’s exactly what today’s post is about: the what, why, and how of test data management.

We start by defining test data management (TDM) and then proceeds to explain why it’s so important. After that, we show some basic guidance on how to implement TDM, showing the typical stages in a TDM process, and explaining the activities performed in each one.

See also:What is Event-Driven Architecture? (Sponsored)

Before wrapping up, we explain that TDM is becoming a larger and larger challenge and that more advanced approaches (including AI-assisted tools) might be the key to solving it.

What Is Test Data Management?

Let’s begin by defining test data management (TDM.) Test data management is the process of managing the data necessary for fulfilling the needs of automated tests, with zero (or as little as possible) human intervention.

That means that the TDM solution is responsible for creating the required test data, according to the necessities of the tests. It should also ensure that the data is of the highest possible quality. Poor quality test data is worse than having no data at all since it will generate results that can’t be trusted. Another important requirement for test data is fidelity. Meaning, it should resemble, as closely as possible, the real data found in the production servers.

Finally, the TDM process must also guarantee the availability of the test data. It’s no use to have high-quality data that is realistic as possible but doesn’t get to the test cases when it needs to.

So, we can say that the test data management process has three main goals: providing test data that is of high-quality, realistic, and available.

Why Test Data Management Matters

In the previous section, we’ve defined test data management and briefly covered the motivations behind its use. We’re now going to cover those motivations in greater depth.

Automated Testing Needs Quality Data

If you feed poor feedstock to any industrial process, the result is going to be subpar. If you supply bad materials to a talented craftsperson, they’ll probably achieve a better result than a less talented worker, but they can only go so far.

The same is obviously true for testing. It doesn’t matter how great your testing strategy is. If you feed it bad data, you’ll get bad results, every time. Not caring about the quality of your test data is throwing money away. All investments made into your testing strategy will have been for naught.

Automated Testing Needs Available Data

Another crucial responsibility of TDM is to ensure the availability of the test data. Your data might be of the highest quality imaginable, but if it’s not there when needed, it’s useless. The only thing that’s definitely worse would be to have low-quality but readily available data.

So, high-quality data with high-availability is the only result we settle for. There should be no compromise.

You Want to Follow Data Regulations

The previous section ended by mentioning the risk of exposing sensitive client data. This isn’t something you should take lightly. On the contrary: sensitive data exposure might result in catastrophic damage for a business.

Here we’re talking about laws and regulations regarding consumer data. The most known of said regulation is probably GDPR, but it’s not the only one. No matter what specific regulations your jurisdiction is under, what matters is that you have to adhere to it. Failing to do so might result in serious consequences, financially and legally-wise. And that’s not even to mention the damage to the company’s reputation.

You Want to Find Bugs as Early as Possible

The sooner you detect a bug, the cheaper it is to fix it. Having a solid test data management process ensures that the whole automated testing process runs smoothly, which increases the chance of catching more bugs earlier.

This is more of a general benefit of automated testing, but I thought it’d make sense to include it if we consider TDM as an enabler of a great testing strategy.

Test Data Management: The Basic How-To

Now that we’ve covered the “what” and the “why” of TDM, the only question left is the “how.” How does an organization implement test data management? What does the TDM process look like?

The key phases of the TDM process are:

  • Planning
  • Analysis
  • Design
  • Build
  • Maintenance


The planning phase starts by defining both a test data manager and the data requirements for data management. The next step should then be to prepare the necessary documentation, including the list of tests.

At this stage, a test data management team should be formed, and appropriate papers and plans should be signed off as necessary.


The second stage in a typical TDM process is the analysis stage. The main activities that should be performed at this stage are the consolidation and collection of data requirements. Important policies concerning data backup, access, and storage should also be defined at this point.


When we reach the design stage, it’s time to decide the strategy for data preparation. At this point, you should identify data sources and providers, as well as the areas of the test environment that need data to be loaded or reloaded.

This stage is the final point before implementing the TDM strategy, so all remaining required plans need to be created here. Those include, but aren’t restricted to:


This stage is where the TDM process finally gets implemented or built. Here, all plans devised during the previous phases are executed. Data masking, if required, is also performed. Finally, the data is backed up.


After the build phase, we’re finally at the last and longer phase, which is maintenance. After the TDM process is effectively built or implemented, the organization needs to maintain it indefinitely. Maintenance here doesn’t comprise only troubleshooting and fixing problems found with the TDM process and tools themselves, but also responding requests to update existing test data or adding new data when necessary.

TDM Is a Growing Challenge, but You Can Overcome It

Test data management still is a big challenge for software organizations. A modern software testing approach both requires and generates massive amounts of data, so creating a process able to manage all of that data is quite an undertaking.

See also:Overcoming the Barriers to Successfully Scaling AI

But there’s no reason to despair because there’s light at the end of the tunnel. And this light is called “artificial intelligence.” In recent years, the number of testing tools that leverage the power of AI has greatly increased. Such tools are able to help teams beat the challenges that get in their way with an efficiency that just wasn’t possible before.


In this post, you’ve seen what test data management is, why you should care, and how to go about adopting it. We’ve started by defining the term. Then we’ve covered the reasons behind the adoption of TDM. After that, we’ve shown you the basics of how to implement TDM by explaining its basic stages.

We’ve wrapped up with the fact that despite TDM being one of the many challenges that threaten software companies, there’s hope in the form of AI-assisted tools.

Oren Rubin

About Oren Rubin

Oren Rubin is the founder and CEO of Testim. He is a 20+ year veteran of the software industry, focusing mostly on building products for developers and testers for companies such as IBM, Wix, Cadence, Applitools, and Testim.io. In addition to his work as an entrepreneur, Oren is also a development community leader and the co-organizer of the Israeli Google Developer Group meetup and the Selenium-Israel meetup. He has taught at Technion University and mentored at the Google Launchpad Accelerator. Follow Oren on Twitter.

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