What Is Test Data Management in Software Testing?

Testers have quite a lot of responsibilities. Often, when a tester thinks that they’ve covered all the test case scenarios, a regression defect appears. Usually, it appears at a late testing phase or when some new code changes are pushed in the application—the worst case being when the tester from the client side catches these defects in the production environment. Problems like this arise when a proper test data management (TDM) strategy isn’t in place. Managing data is, in fact, the hardest part of the software development life cycle (SDLC).

In this post, we’re going to learn about TDM in detail. We’ll get to know what test data and test data management are. Also, we’ll check out why TDM is important and the challenges that come along with it. Let’s dive into the details.

What Is Test Data Management in Software Testing?

Before getting into what TDM is, let’s discuss test data. Test data is any kind of data that the tester can use. The tester can use the data to test an application or a specific feature of an application. For instance, suppose you’re testing the secured login feature of an application. You can consider the dummy username and password that you’re using to be test data.

Test data management, TDM, is a process that involves the design, planning, storage, and management of all kinds of test data. The testing team has complete control of files, data, rules, and policies during the entire testing cycle. TDM involves keeping production data and test data separate. Apart from that, maintaining test versions and bug-tracking are other useful features of TDM. We shall discuss the features in detail in the following sections.

Importance of Test Data Management

Sometimes, everything works well during staging and testing. But when you’re about to release the code into production, it can cause intermittent errors. This happens when the management of test data isn’t done properly. No matter how careful a tester is, good data is a must-have to do a better job. Businesses can face huge losses if they don’t have a TDM strategy. But managing test data can be time-consuming and needs a lot of effort. However, this doesn’t mean companies can overlook the importance of using appropriate data for quality testing. This is where TDM comes into the picture. Let’s check out some ways in which TDM is important for any firm.

1. Avoiding Redundancy in Test Data

Owners of applications create multiple code copies for development and testing. Most of the time, the size of these copies is the same as the original code. As a result, the production data has many copies. The product owner doesn’t even know which ones are useful and which ones aren’t. But if a sound TDM strategy is in practice, managers can avoid two problems. First, when there isn’t redundant data, storage costs will be less. Secondly, managing tons of copies can be tough. With TDM, this won’t be a requirement. An efficient test data management suite has an algorithm that sorts and points out redundant or duplicate test data. Once that is done, the tester can delete all unnecessary data.

2. Avoiding Data Bloating

As the data in a company grows, the cost of storage and maintenance rises. This calls for a need to keep only healthy data. There is no harm in keeping all the important data you need. But as a project grows, it’s a wise move to generate data for every test case. Another profitable practice is to have more static test data. You can control it better. Once the use of static data is over, you can archive it. Thus, static data serves as a great cost-cutting solution. Stored data from years previous occupies space and also affects performance. So, make sure you optimize the data accordingly. The best way to do that? Having an effective TDM solution. Even if you aren’t using a TDM suite specifically, if you’re using a TDM strategy by following the best practices, it’ll involve routine test data cleanup activities. Thus, you can optimize data and get rid of archived old test data.

3. Enabling Smart QA Analysis

Cognitive QA is a smart approach to data testing. This technique enables implementing automation with quality data. Thus, it simplifies the use of robotic process automation and AI. Cognitive QA also enables the alignment of test functions to the business outcomes that you want. Apart from automating testing, Cognitive QA prioritizes test data for automation. The core benefits include minimal risk and reduced costs. Besides these, quick delivery of high-quality end products is another major perk of TDM enabling smart QA analysis.

4. Lowering Costs Even if Bug Detection Is Late

Every tester strives to find bugs in the early stages of the SDLC. However, if a bug comes up at later stages, the cost of fixing it can increase drastically.

The need for emergency fixes arises when unexpected bugs come up in the later stages of the SDLC. It takes a lot of time to duplicate production data. This leads to high costs of fixing bugs. But when there’s a proper TDM strategy in place, you can avoid the time consumed by duplicating production data since you’ll already have required data that you can use at any stage of the SDLC. Thus, costs don’t rise steeply even if the bug detection is late.

5. Strengthening Customer Trust

Improper coverage of test data is the leading cause of crashing in an app. Such crashes can break the trust of customers. As a result, you can end up with a weak client base. Nothing instills more fear in users than a failing application. They’re highly unlikely to give you a second chance in case of failure. Test data, if not organized, can cause a firm to lose the trust of customers. So, make sure you don’t let that happen. Avoid failures caused by a lack of test data management and build a stronger and wider client base.

How Test Data Management Works

All caught up with why we need TDM in our application? Let’s discuss how it works. Test data management mainly comprises four different techniques.

1. Exploring Test Data

Data is present in different formats and in multiple systems. It’s the job of different teams to select the correct data according to test cases and requirements. But manually locating this data can consume a lot of time. Thus, a robust test management tool is a must for locating the right data. The first technique involves sorting the data based on test cases.

2. Validating Test Data

Test cases should provide a clear picture of the production environment. This step is important as this is where the application goes live. Real data from production databases is essential for testing applications. But teams have to ensure masking of sensitive data. It becomes crucial to validate the test information. This process helps to make sure that the data is valid and complies with data privacy regulations.

3. Ensuring Reusability in Test Data

Reusability in test data is important to save time and effort. In this phase, the team sorts out data that testers can reuse in other test cases. Team members then store the data in central archives. After that, the team leaders can provide the required information to the person responsible for a particular task. In case some test cases require reusable data, testers can pick that up from the archive.

4. Implementing Automation to Accelerate the Process

Some main TDM processes include scripting, data masking, data generation, cloning, etc. These activities are useful, but they consume a lot of time. This decreases the proficiency of TDM. Implementing automation in these processes can help make TDM quicker and efficient. For instance, teams can even automate the production of test data.

Challenges of Test Data Management

A robust TDM helps in delivering quality products quickly. But TDM also gives rise to numerous complexities firms have to cater to. Now that we’ve covered the importance and processes behind TDM, let’s take a look at some major TDM challenges. We’ll also explore how testers can overcome them.

1. Irrelevant, Invalid, and Inconsistent Data

Testers have to use meaningful and realistic data for testing. But it’s not easy to identify relevant test data. Not only that, aged data can become obsolete as time passes. Testers often get confused while sorting test data and deleting data that is irrelevant.

Using test data generation, software testers can prioritize data. You know what another solution is? Validating data from time to time prevents the loss of context. Trace the data from the inception stage to maintain consistency.

2. Lack of Accuracy and Efficiency

Sometimes, external factors such as device, location, or hardware issues can compromise TDM. It’s important to take them into account to maintain accuracy. Extracting, identifying, and conditioning data can be time-consuming. But since departments in most companies work in silos, TDM isn’t as efficient as it should be. Adopting agile practices can help bridge the gap between teams and team members. Use testing suites or solutions that offer test case execution in real-time. This will increase the overall efficiency of TDM.

3. Privacy, Safety, and Compliance Issues

Most companies bear heavy losses if they don’t practice appropriate encryption techniques. For instance, firms have to deal with expensive lawsuits in case of a data breach. Sometimes, unfavorable data storage conditions can result in leakage of data. Masking data can curb this problem to a great extent. Moreover, there are certain data compliances in place. They’re meant to safeguard customer data. But the authorities frequently update the data compliance requirements. This makes it difficult for firms to keep up with sudden changes. Companies feel restricted while cloning production data in case of any breach. One solution is to use data from testbeds. Apart from that, as I said earlier, using a test data management suite is another solution. This suite runs a check and provides a report that shows whether your test data complies with data privacy regulations.

Let’s Wrap It Up

At last, we’ve covered the process, importance, and challenges of TDM. But to give a firm the boost it deserves, it’s important to go ahead with implementing TDM. Planning ahead will help increase the robustness of TDM. If you’re careful with its implementation, TDM will create a strong foundation for testing in any company.

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.