Everyone wants software that works well and has no bugs. You will be well familiar with the fact that users get frustrated when they use software or apps that are slow, crash, or have errors, right? That’s why testing plays an important role before the software goes live.
Testing helps to find and fix any problems in the software. But testing needs good test data, too. That means data should be realistic, reliable, and secure. In that aspect, DevOps teams use a test data management strategy to create, manage, and use data for testing. This helps to evaluate the software thoroughly with the help of reliable test data.
IBM Big Data and Analytics Hub website mentioned a real-life example where a US insurance company spent about 15% of their time and resources just collecting test data for both the backend and frontend systems.
Read this article to learn more about TDM. We’ll discuss why TDM strategy is important and how to develop it effectively.
What is test data?
Before we talk about TDM, it’s important to have an idea about test data.
Test data means any data that developers use to test their software. DevOps teams use this data to check how their apps will run in a live production environment.
In a nutshell, test data is information that helps you evaluate your software and make it better. For example, DevOps teams may use it to test the performance or security of the applications.
Types of test data
Got the meaning of test data? Now, let’s discuss the types of test data that developers use to conduct test data management strategies.
– Valid test data
It is the data that matches the expected format and values of the input. For example, if you want to test a login form, valid test data would be a correct username and password. Valid test data helps you check if the system works as intended and meets the requirements.
– Invalid test data
Invalid test data is the data that does not match the expected format and values of the input. For example, if you want to test a login form, invalid test data would be a wrong username or password or an empty input. Invalid test data helps you check if the system handles errors and exceptions properly and prevents unwanted outcomes.
– Absent test data
It is the data that is missing or not available for the input. For example, if you want to test a login form, absent test data would be a missing username or password or a network failure. Absent test data helps you check if the system can cope with incomplete or unavailable data and provides appropriate feedback to the user.
What test data challenges do developers commonly face?
Where there is a facility, there might be challenges, right? Test data seems to be a simple-to-do concept, but there are also some challenges that developers may face when creating and deploying test data. So, here are some of those common challenges that can appear when using test data:
· Lack of Relevant Data
Sometimes, developers do not have enough test data that matches real-world scenarios and user behaviors. This can make the testing process less effective and reliable.
For that, developers have to create synthetic data as a temporary solution.
· Manually Creating Test Data
Another challenge is that creating test data can be time-consuming and tedious. Developers may have to manually enter or modify data values or write scripts or code to generate test data. This can slow down the development cycle and reduce productivity.
In that case, most teams prefer to automate the process completely to create the data.
· Data Validity and Integrity
Last but not least, test data can become invalid or corrupted over time. The data may change or expire due to updates, modifications, or deletions in the source data or the software.
To ensure integrity, developers need to analyze the data deeply throughout its lifecycle.
What is automated test data management (TDM)?
TDM is the process of creating, storing, and handling data that is used for testing software applications.
Human is to err. You can’t guarantee the best test data quality when handling it by hand. For that, it’s important to consider automating TDM wherever it is possible.
Automated test data management solutions can help you in various ways, such as:
Indeed, manual approach testing is good to deeply analyze the complete test data but that’s time-consuming. On the other hand, a test automation strategy can help generate data quickly and easily. You don’t have to spend time and effort creating data by hand. It’s all about adopting a time-saving yet reliable approach.
Also, an automated strategy can help you follow the rules and regulations for data privacy and security. For example, you can mask or anonymize sensitive data to protect it from unauthorized access. And? You can track and audit data usage and quality.
The ultimate goal of the testing is to ensure the quality of the software. Automated test data management processes can improve the quality and reliability of your software testing. You can create data that matches the real-world scenarios and conditions. It’s all about creating high-quality software with minimum bugs at a higher production speed.
How to develop a test data management strategy?
Here are the best practices for test data management:
1. Create a preparation plan
Before you start testing, you need to plan what data you need, how much, and where to get it from. While in that case, you also need to define the scope, objectives, and criteria of your testing. A good preparation plan that your team will follow helps you save time and resources.
2. Verify your data accuracy
Once the data is available, data teams need to make sure it is accurate, complete, and relevant. On that ground, checking if it complies with the data privacy and security regulations is a MUST.
Teams can use tools like data masking, data subsetting, and data profiling to check and improve the data quality.
After receiving data, developers should be confident about data privacy. By checking data in advance will lead to a reliable, fast and secure testing environment.
3. Store test environments in a single repository
One of the challenges of managing software at scale is keeping track of all the test environments. A good solution to this challenge is to centralize your test environments in a single repository.
This means that you store all your test environments in one place, where you can easily access and manage them. There should be the latest test environment versions in the repository.
Centralizing your test environments has many benefits for effective management. For example, it helps to avoid conflicts and errors, save time and resources, and improve your visibility and control.
4. Deploy robust governance
When dealing with test data, the chances of potential security and privacy risks increase. To avoid this, you need to deploy robust data governance throughout your entire cloud environment.
Governance refers to the set of rules and policies that define how you handle your test data. You need to have clear roles and responsibilities, data quality standards, data security measures, and data retention and disposal policies.
Data governance tools help you ensure compliance, accountability, and efficiency.
5. Research reliable open-source tools for test data management
There are many open-source tools in the market you can use to make your test data management strategy better. But you should find a reliable source to integrate these tools.
They can help you with various aspects of TDM, such as data generation, data masking, data subsetting, data profiling, and data provisioning.
Some open-source tools are Databucket, Databene Benerator, and DataFactory.
What are the key benefits of test data management strategies?
The TDM strategy has many benefits. For example, they:
- Save you time and money by reducing the need for manual data creation and maintenance.
- Improve the quality and reliability of your test results by ensuring that your test data is accurate, consistent, and relevant.
- Protect your sensitive and confidential data by masking or anonymizing it before using it for testing.
- Enable you to comply with data privacy and security regulations by following the best practices and standards.
- Support your agile and DevOps processes by facilitating data sharing and collaboration among your teams and tools.
Conclusion
The TDM strategy is the best approach to test software to improve its performance and quality. Developers use different kinds of test data, such as valid, invalid, or absent data. To develop this, you need to follow some steps. First, you have to ensure what data you need, how much, and where to get it from. Second, you have to ensure that test data is accurate and reliable. That means the data follows the data privacy and security regulations. Third, you should centralize your test environments in a single repository. Fourth and last, you should deploy robust data governance throughout your entire cloud environment. Developing this strategy can benefit you in different ways. For example, it can save your time, protect your sensitive data, improve the quality and reliability of your test results, and enable you to comply with data privacy and security regulations.
Franking 
Leave a comment