Test Data Management: What You Need to Know

Owen Savage

Automation Expert

What is test data and how do you manage it correctly? This brief explainer will tell you what you need to know about test data management, as well as how it relates to test automation

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What is test data? 

What is test data management? 

How to manage test data

Automating test data management 

 

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In today’s market having credible and reliable test data is highly important. Performing good software testing really does depend on the quality of test data. 

Your data needs to be predictable in order to run test cases successfully. Therefore, you’re going to need to know how to manage test data correctly and effectively. 

What is test data? 

Testers write test cases in order to verify and validate the given features and developed scenarios of the application under test.

However, if we want to do so, we need something to use as input for these test cases. If we don’t have this input, we simply can’t identify and locate defects. 

This is what test data is: the data that is used for software testing. 

Test data management 

So now you know what test data is. But how does it need to be managed? 

The process of test data management is the creation, delivering and managing of test data for application teams. 

Testers typically use what is called a ‘testbed preparation’: this involves setting up the software requirements using predefined data values. 

It is when you plan, design, store and manage software-quality testing processes and methodologies.

One of the main things a tester is looking to achieve with test data management is minimizing and optimizing the size of software testing data.

Read more: What is a Test Automation Strategy?  

Also known as software test data management, this process helps your testing team maintain control over the following things: 

  • Data
  • Files 
  • Rules
  • Policies 

How to manage test data 

What do you need to consider when managing test data? 

One of the biggest challenges you’ll face when building data for test cases is a lack of a systematic approach. Without one, you’ll risk missing some important tests. 

First of all, you need to figure out how suitable your data is for testing – this brings questions like: what types of data am I using? What environment is it being tested in? 

There are other vital things that testers need to consider in the test data management process. You need to know where to store your data, and whether it will work to use a copy of production data. 

This brings up the question of how careful you need to be when it comes to data protection laws. That depends on whether you’re using real data, or synthetic. 

If a tester uses synthetic data, the data isn’t real so there’s less need to worry about data protection. 

However, this isn’t the case with RPA. Here you’re working in a live environment and using real data, so it’s a must to comply with data protection laws. A part of this requires masking data so that no privacy legislation is violated during the process. 

Automating test data management

If you’re testing on a very small scale, you might be able to manage your test data manually. However, as your organization grows you will need to scale your testing strategy. Otherwise, you could easily be overwhelmed by managing the data required. 

Watch: Codeless Test Automation Webinar 

So how can that be avoided? By using the right tools to manage data automatically. By doing this you will make sure the right test cases get the right data. This is crucial for your testing to run smoothly. 

You’ll want to make sure you have control over which automation flow is started and by whom. This is because once you start moving data around in a testing environment, some things cannot be undone. 

Finally, when automating test management, you need to decide if test cases should clean up their data on their own after being used. 

If you want to know more about test automation strategy, consider downloading our checklist below. It will help you to decide which processes and technologies to automate, as well as define a method for releases and plan how to analyze failures. 

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