How to Do Test Data Management in Agile: 8 Best Practices

Once upon a time, application testing was really easy. All you had was a single mainframe and limited data sets. Users were satisfied with a simple application that provided the basic features. But nowadays the competition to develop the best app is intense. Companies are focusing on developing applications that provide the best user experience and features. This means companies are also focusing on application testing to ensure the features they implement work. And with testing, there comes a need to understand how to do test data management in agile. Why?

When it comes to testing, there are many factors that require consideration. Compliance standards like the European Union’s General Data Protection Regulation (GDPR) have added new challenges to testing, and overlooking even one area can expose an organization to massive risks, not the least of which are lawsuits and compliance fines. So how do you avoid these pitfalls? What do you think should be the number one task for testing teams?

With an increase in companies adopting agile, the need to accurately and efficiently manage test data has also increased. Proper test data is a must when shifting to a more flexible development process. After all, it’s the best way to improve the quality of tests. Therefore, it’s important to understand how to do test data management (TDM) in agile. Because there are various boxes to check before you can mark a test complete, TDM should be a high priority when it comes to streamlining testing.

Because agile is relatively new, handling TDM can be a challenge. So, in this post, we’ll take a detailed look at how to handle TDM in agile. Before we get started, though, it’s important to understand exactly what TDM means.

What Is TDM?

Any data that can be used for testing is called test data. TDM is the process behind planning, designing, storing, and managing test data. Nowadays there are many restrictions on the data that a tester can use for testing. For example, per GDPR, a user’s real data cannot be used for testing purposes. TDM makes sure that the data used adheres to all security and compliance standards. It does so either through manual checking or by using a TDM suite. The process also ensures that the tester can get the right data easily without much searching. TDM achieves this by following a proper storage procedure.

Now that we know what TDM is, let’s discuss best practices for effectively managing test data in agile.

Best Practices for Handling TDM in Agile

In agile, managing test data is a hectic procedure, so it’s not always clear how to do test data management in agile. You have to worry about storing and managing test data in every sprint. Then, during regression testing, you have to think about accessing and using the old test data. But don’t worry. Following the best practices listed in this section will make the process easier. Let’s get started.

1. Ensure Proper Test Data Discovery

Companies have a lot of data available across different systems, and different rules apply to various forms of data. For instance, if your company or project is healthcare based, the data must comply with Health Insurance Portability and Accountability Act (HIPAA) requirements.

The foremost duty of a company is to identify its test data. Beyond that, it is also important to sort the data and assign it to where it is needed. Team members in charge of storing the data must have information about the data used for testing. They should know how end-to-end processes for the business work. Sometimes testers can spend a great deal of time discovering vital test data. To prevent that, find and sort test data before the testing begins.

2. Ensure Security of Sensitive Information

There are certain industry standards and government compliance that you have to keep in mind when handling data. This includes employee, client, and corporate data. It is important to identify sensitive data and ensure its security.

Per GDPR, you cannot use real data for testing. Data masking is a useful process in which real data is hidden and another set of data is created for testing. The dummy data is similar in structure and algorithm to the real data.

Data masking can be a helpful practice, but every company must ensure that real data isn’t lost or misplaced while masking.

3. Keep Safety a High Priority

Every company has a massive pool of data stored in existing databases. For testing and other operations, these data pools act as a kind of repository.

In an agile framework, the data can also come from end users who already have an account or have used the application. This is a common practice for discovering test data, but it also exposes personal information to a lot of risks.┬áThis is because apps under development don’t have that many security protocols. In some cases, it becomes necessary to wipe apps clean of information such as names and addresses. This prevents the theft or use of data for malicious purposes. However, it is important to simulate the data as close to reality as possible.

4. Refresh the Data

Haven’t we always seen experts promoting the reuse of test cases whenever possible? After all, the relevance of test data doesn’t decline after a single test cycle; it remains valid for multiple cycles. But here is the most important reason why reusing test cases is important: Generating and discovering new test data can be time consuming.

Reusing old test cases can save a lot of time. But this doesn’t mean you keep on storing data that is no longer relevant. The best solution is to identify what data no longer serves the firm. Once you do that, remove the data that’s no longer needed. In the process of refreshing the data, you can also add more data that holds greater relevance.

5. Efficiently Derive Production Data

It is important that you determine what kind of data the testing team needs. For example, if your team is testing the login functionality of a website, they will need a set of usernames and passwords that matches the data stored in the system. Once you’ve identified the necessary data, you need to derive the data that promotes realistic TDM.

The tester in charge also has to make sure that they collect the changes accurately. For that, it is important to extract precise data and obtain the metadata from the subset. This helps to create realistic databases for testing. Efficiently deriving data also supports rapid test case execution.

6. Plan Well

Managing a test environment involves a lot of overhead. But if you standardize tests across groups, you will notice a reduction in this overhead. Proper planning makes this possible because it helps you gain a better understanding of standardizing tests across groups. Think about it this way: You won’t be able to use all the data anyway. That’s why you need to choose a subset mimicking production. Planning helps make the entire TDM process in agile easier.

7. Ensure Proper Maintenance and Auditing

When the volume of data rises, proper storage and management is important. Also, you have to update the data to maintain its relevance. The cost of this maintenance and storage can take up a significant portion of your budget, so it’s important to manage all efforts in a cost-effective way.

Different workloads run in the production environment. The accurate representation of these workloads by test data is a must. Frequent test data audits make that accurate representation possible. Auditing test data also helps maintain data integrity and security because auditing checks whether you have proper security measures in place to keep data safe.

8. Implement Automation as Much as Possible

In today’s world, every field is focused on minimizing human effort as much as possible. The same applies to the software development industry. Manually running repetitive test cases wastes a lot of time. How about focusing most of the manual labor on helping the company progress instead? Manual effort should be focused on innovative thinking and finding new ways to get things done. Clearly, that’s not possible if testers are running monotonous test scripts.

Just like most firms automate regression testing, you can also automate test data production. Automating repetitive test cases is also an effective way to save time and money. Another process to automate is the refreshing of data. Think about it this way: A tester has to compare the results of consecutive test runs. When you automate this comparison process, it saves time and exposes errors in the early stages.

Get the Most out of Agile With Better TDM Handling

I hope this post gave you a better understanding of how to do test data management in agile. It is important to set up test data in the system to ensure the credible running of tests. This improves reporting and increases accuracy in testing. Proper data for software or an application is like fuel for a car: Without it, it just won’t run. Only TDM implemented using best practices can help a company soar while getting the best out of its agile framework.

This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.