What is Test Data Management & Why is it important in 2024


You may see new applications and products in the online market almost every day or once in a while. Businesses are in the race and struggle to be nominated as the “best guy in the market.” However, those whose products or apps appear flawless win the race.

Thinking about what makes them flawless? The only approach is testing. Yes, testing helps pinpoint the spots where your apps glitch or what they lack. However, that requires quality and authentic test data. As a modern-day strategy, test data management (TDM) plays an important role. This strategy ensures that the right data is available to test the performance of the applications. So, what is TDM, and why is it so important? All of that will be discussed in this article.

What is test data management?

Test data management involves creating, managing, and organizing non-production datasets for software testing. These datasets reflect real-world scenarios. All that is done while maintaining the privacy of the real data. With the availability of non-production datasets, the test data management strategy helps to understand and test how the software will work in real-life situations.

Different organizations may use this strategy in different ways, depending on their requirements. However, their ultimate goal is to support robust and reliable testing without leaking sensitive information, i.e., PII (personally identifiable information).

On top of that, these days, customers want to use products that are efficient and reliable. To meet their demands, testing teams use this strategy to test the products before they launch them.

Why is test data management important?

Got the basics? Now, it’s important to know about the hype surrounding TDM.  Here are some factors indicating why it is important in 2024:

1. Ensures relevant data

Let’s suppose you study with outdated or incorrect materials. You might not even pass the exam. Similarly, if the data used for testing software is old or inaccurate, it can lead to mistakes and unreliable results, just like studying from the wrong information can lead to poor performance on an exam. Hence, effective test data management strategies ensure that you’re using the right information to accurately assess the performance and functionality of the software being tested

2. Complies with Data Privacy Regulations

Everyone wants to have their privacy, right? The production data includes real and sensitive information. Though, you don’t want to expose anyone’s information from the production data in your testing,. But what if you do it unknowingly? The consequences can be hefty fines.

In that aspect, TDM comes up to help you with data masking techniques, synthetic data generation, and compliance analysis. So, it helps to avoid lawsuits that damage your company’s reputation.

3. Minimizes data complexity

You will be well-familiar with the fact that having a pile of thoughts doesn’t relax the mind, and everything you feel is just a mess.  Samelike this, storing too many copies of test data can lead to confusion and inefficiency.

By managing test data effectively, you can minimize this data management complexity. This way, you use only what is important and relevant.

4. Launches product in a short time

Time has become a priority for everyone, and no one wants to waste it at any cost. Therefore, everyone expects quick solutions, saving them from the time-wasting factor.

Don’t you think collecting and managing data manually from different sources takes days to weeks to test your product? Yes, it does.

Automated testing in test data management solutions cuts down testing time. As a result, the development speed will increase, and the products will be launched in a short time.

What are the crucial phases in the test data management process?

Knowing just meaning and importance is not enough to win the game using TDM. You must have a clear idea of what the process looks like and what phases it consists of. So, there are five phases:

1. Planning

This is the first and crucial stage. Here, testing teams clarify which aspects of software they will test.  Also, it includes what data is required for each test. For example, if it is a login test, it might require usernames and passwords.  Having plans for such test cases will lead to effective decision-making.

2. Analysis

This stage helps to analyze different things, such as:

  • Type, format, and volume of data
  • Strategies for data safety and to create copies in case of accidental loss
  • Who can access and modify the test data?
  • Appropriate storage solutions

3. Create the type of test data

This phase comes before the teams finally implement test data management strategies. Here, they finalize whether they should clone the production data or generate synthetic data.

Also, businesses should be clear about where the test data will be loaded. That means whether it is a separate testing environment or the production environment itself.

4. Build

That’s where the previous three stages come into existence—implementation of the strategies. Along with data backup, if there is any need to implement data masking techniques, teams should do that in order to keep sensitive information safe.

5. Maintenance

Maintenance is the last but essential stage in the TDM lifecycle. It helps to achieve the overall success of your strategy. This stage identifies and resolves the issues that might arise. Here, you will respond to the test data requests.

For example, you can add new data sets or update the existing ones to reflect changes in the software being tested.

Best practices for test data management?

As we’re done with this and TDM, let’s finally delve into how to manage the test data effectively. For that, consider the following practices:

Be clear about what you truly need

Businesses should determine what data is required for test cases.  Having clear information about this requirement will avoid unnecessary efforts to create irrelevant test data.

Let’s say you’re testing an e-commerce platform’s shopping cart functionality. A test case might involve adding multiple items to the cart and checking if the total price reflects the combined cost of those items accurately. In this case, you don’t need a bulk of real product data for each item. What truly matters is the quantity and price of each item. Hence, you could create simple test data with just product IDs, quantities, and corresponding prices.

Create subsets

The next step is to create subsets of the production data. These subsets are small versions of the data that execute the testing quickly.

This way, testers will focus on what is relevant without getting bogged down in massive datasets. No doubt, they are subsets, but remember, they shouldn’t miss the important points of the production data.

Use masking techniques

As we have mentioned above, breaching sensitive information can result in hefty fines. So, you should never overlook this step when implementing the best test data management practices.

Before shifting data to the testing environments, it is important to find out if there is any sensitive information about the customers or clients. If it is, companies should use masking techniques to maintain data privacy and then transfer it.

Update test data

Out-of-date data can lead to test failures that don’t reflect real-world scenarios. In this case, updating the data keeps the tests relevant and avoids wasting time on irrelevant issues.

Along with that, by using consistent, updated data, testers can ensure a predictable testing environment. This makes it easier to identify and reproduce bugs. Ultimately, it saves testers time and effort when creating and running tests.

Use automated tools to compare test data results

Last but not least, after running tests with various data sets, the crucial thing is to compare the results with the baseline to see if they match. To do that, human intervention can also be an option. But that takes a lot of time and can be error-prone.

The advice here is to use an automated tool to streamline the process. The tool can quickly highlight any deviations from the baseline, which allows testers to identify issues sooner.

5 Best Test Data Management Tools of 2024

Here is a list of the 5 best test data management tools for 2024 to help you effectively manage test data:

  1. K2View handles almost 5 billion queries per day and is a better option for highly complex and enterprise environments.
  2. GenRocket is a self-service platform with almost 660 data generators and 80 data receivers.
  3. Datprof facilitates one central platform to manage the test data in different test environments.
  4. Informatica is specifically made for Salesforce and is popular for its automated data discovery features.
  5. InfoSphere Optim is highly focused on complying with privacy regulations at every stage.

Final Thought

Test data management is a way to create and manage non-production datasets for software testing. Businesses are considering this strategy for various reasons. For example, it ensures relevant data for testing, complies with data privacy regulations to help you avoid hefty penalties, minimizes data complexity, and helps to launch products in no time. To get an idea of how TDM looks alike, you should consider five phases. They are planning, analyzing, creating the test data, building, and maintaining. Moreover, when looking to manage this data effectively, there are different practices to follow. They include clearing your requirements, creating subsets, using masking techniques, updating test data, and leveraging automated tools to compare test data results.

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