Top Test Data Challenges
What Is Test Data Management?When you’re about to test a software application, you need some data to write the test cases. For example, testing a login functionality of an app will require a set of user names and passwords. This data is known as test data. For manual testing, the QA team stores test data in an Excel sheet. For automation testing, the QA team uses XML or a database from which the automation tool can access the test data. So, what is test data management? It’s the process of planning, creating, and storing test data. This allows the QA team to have control over the data and their usage rules during the software testing life cycle. Although TDM sounds simple, for a large application, it’s quite complex. There are many challenges the QA team must face while managing test data. Let’s discuss some common TDM challenges.
Challenges Faced During Test Data ManagementThere are many challenges the testing team may face while managing test data. In this post, we’ll discuss 13 such challenges.
1. Lack of Awareness and Standardization Surrounding TDMOne of the biggest mistakes companies make is allotting TDM only to team leaders. But the truth is that each team member needs to be aware of TDM, not just the team leaders. This includes various processes and tools. The lack of awareness about TDM can be fatal for a firm. When team members aren’t fully informed, it creates room for errors. Moreover, if there’s no standard data request form, the length of the testing cycles increases. This is because the team will be requesting data in different types of formats. Since they have to carry out different kinds of testing, it creates confusion. Educating all team members about TDM helps in this case. Also, having a standard for data formats can help solve this issue.
2. Lack of Relevant Data During TestingTesting real-time data is not always in compliance with privacy and safety standards. Relevant test data is a must-have to ensure successful test case execution. Most companies face a serious shortage of meaningful data that they can use without any risk. Using automated test data generation software can help the team to deal with this problem.
3. Invalid Data and Lack of Data ConsistencyMost businesses fail to realize a simple fact: data ages, and it matters. Once-relevant information can become obsolete over time. Thus, you need appropriate data versioning and maintenance of data integrity. When data ages, it loses some of its context. Most firms don’t make it a point to validate data integrity from time to time. As a result, they experience problems that are difficult to troubleshoot. You have to trace the data at an end-to-end level until the end of the life cycle. This means you should begin tracing data from inception itself. Do that, and you’ll overcome this challenge.
4. Compromised Data PrivacySuppose you’re developing a tax-filing application like TurboTax. The app may contain many sensitive information related to the user as well as the government. Applications with such sensitive information need data masking. Some of that information includes government regulations and mandates. Without proper encryption, the chances of a data leak increase. And a data breach can result in the malicious use of that data. It’s expensive to sort it out and get things back on track. As a result, a company can lose money dealing with lawsuits or get into a lot of trouble if some sensitive government information gets leaked. To prevent that, it’s a good practice to encrypt information and mask sensitive data from the beginning.
5. Lack of Accuracy in Data PerformanceExternal factors play an important role in an application’s success. Those factors include the device used, location, and internet connection. Often, QA teams find it challenging to deal with the problem when any one of those external factors causes an application to go haywire. An example of one challenge they may have is a loss of data due to a hardware issue. This may impact the delivery timeline as well as product quality. To ensure optimum app performance, take care of external factors. Use proper deployment tools, back up data, and use real-time test suites.
6. More Efforts on TDM Equating to Less EfficiencyTesting is quite a time-consuming process. When TDM comes into the situation, that time and effort increases. For processes like data engineering, data mocking, or data provisioning, teams have to work manually. Besides, there is no way of reusing test data artifacts. Thus, it reduces the team’s efficiency. Wondering how to deal with time complexity? Well, the solution is simple. Start adopting automated test data management solutions. An efficient TDM tool can handle large heaps of data, and it can generate reports. For other manual tasks, if you have less time, appoint more testers who will share the workload.
7. Safety of Stored DataDevelopers dedicate a lot of time and effort to developing the perfect test data. They put a lot of thought into choosing the right tools for execution. But some teams tend to overlook something critical: storage. Poor data quality is the main reason for most testing failures. Even unintentional data breaches can turn out to be costly. If a company compromises data storage safety, it can prove to be ruinous. Thus, deciding where to store data is an essential part of TDM.
8. Different Data Compliance NeedsTo safeguard customer data, many regulations are in place. It’s always a good thing to protect customers from data-related threats. But there are frequent updates in these regulations. This can make it difficult for a company to keep up with the data compliance requirements. The result? A data breach is hardly out of the question. Let’s share an example of how that could happen. Suppose you’re keeping test data on a cloud platform. Only a few members of your team have access to that. Data security regulations involve keeping a strong password and never sharing it with anyone. What if someone has a habit of storing all the account information in a notebook? And what if that notebook falls into wrong hands? Someone with criminal intent may use it, access the data, and use it for malicious purposes. An increasing data breach may damage a company as well as the QA team’s reputation. One solution is to use a data compliance suite. It will help you identify where a security breach may reside.
9. Compromised Data IntegrationThere’s no shortage of data management tools for the waterfall model. But they’re not likely to integrate with agile projects. Not all APIs or plugins provide integration support. As a result, companies can face challenges while migrating to agile. We all know that continuous integration is a must in agile frameworks. Hence, the QA team needs to keep up with the latest technologies. The only way to do that is by integrating TDM with automation.
10. Complex Data Tools and Compromised ExpertiseSince agile is relatively new, its tools can seem complex to most team members. They need a level of expertise and a skill set to work with. Sometimes, companies have to make their teams undergo special training. TDM expertise is also scarce. So it adds to the problem. Educating the entire team about these tools can help you overcome this challenge.
11. Centralized Approach Towards TDMMost companies have a centralized team to handle TDM ownership. This team is independent of DevOps teams and agile sprints. Apart from that, the volume of data requests is high. As a result, the length of data provisioning cycles increases. This hampers continuous integration. Not only that, the testing team becomes unable to exercise some test practices with efficiency. So, to reap the full benefits of agile and DevOps, working with multiple sprint teams is a must.
12. Difficulties in Regression TestingRegression testing ensures that the existing features remain unaffected by changes. If an existing feature breaks because of some new change in the code, it can cost the company a lot. Some products have a direct impact on the lives of customers. This may include banking or financial applications. If a feature in such an application breaks because of a new change, the client can face a huge loss. Hence, regression testing is a must to carry out whenever a small change is made in an application. The detailed nature of TDM results in challenging regression testing. Finding appropriate test data is time-consuming and challenging. But you have to do it.
13. Complexity in WorkflowDepending on your product, the workflow can get complex. If your product has many features, you probably already know how difficult it is to keep track of different scenarios. For example, if you’re developing an application for an insurance company, it should have different services—life insurance, vehicle insurance, and property insurance, to name a few. It also should have different transactions, like claims, new business, and policy renewal. The real challenge comes when all these features or services have a distinct workflow. Hence, having a great diversity in business scenarios makes TDM a tedious task, and it’s a challenge you’ll have to face.
Summing It UpCorrect integration and the use of accurate data are must-haves that determine the success of a company. It helps if you have a precise emulation of your users’ workflow and interaction with your app. But TDM only works wonders for a company as long as that company can overcome some major challenges. Planning can help a company increase the robustness of the process. When you keep a keen eye on different steps of TDM, it will help you build a solid base for testing any application. So stay aware of TDM challenges and strive towards overcoming them. You can also consider using a data compliance suite. This will provide you with end-to-end transparency and a management solution for your te
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