Several enterprises fail to recognize the need for test data management in their application development initiatives. This happens even when they strive toward achieving seamless test automation and improved application quality.
However, without proper data management strategies, enterprises may find their QA practices not yielding the right ROI over time. This is especially true when a large technology landscape is to be covered. Most enterprise technology leaders are unaware of the right data management process in software testing. This can be a major hindrance to the adoption of test data management.
Why Is Test Data Critical in Modern Digital Systems?
Before we cover the definition of test data management, it is essential to know the growing importance of test data. As more organizations rely on digital channels to run the lion’s share of their business, delivering disruption-free and seamless customer experience at all digital touchpoints is a number one priority.
To ensure that the applications they launch to the market offer this guarantee, it is crucial to run strict QA checks. To verify every single workflow and functionality, enterprises must emulate real-life operating conditions for the application. And this is where test data comes into the picture.
Organizations must make available as much realistic test data as possible to cover all testing areas. Hence, the test data volume in modern enterprise application development initiatives will be quite high. Besides, the test data grows in volume and diversity with the number of test scenarios that must be covered in testing. This is the main reason for the evolution of test data management.
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What Is Test Data Management?
You have already seen how test data is vital to modern software development activities. In essence, test data management is the process of seamlessly managing test data in a software testing project to ensure that all test scenarios are given access to the right test data whenever needed.
With test data management, test data is provided in the best format required for test activities and in the right volume to meet all unique testing needs. Test data management is primarily used for automated testing, predominantly end-to-end automated testing activities.
Different test cases may require data of different types. But there are underlying guarantees that every test data needs to assure before use for test activities. They are quality of the data, availability for use, timely access, realistically real-life use data, and compliance to any regulatory or policy framework that governs test data usage.
What Are the Different Test Data Management Strategies?
Now that you are aware of the importance, it is time to explore some of the key test data management strategies that can help drive maximum ROI from test exercises:
Analyze Before Finalizing Data
One of the main strategies to follow is to ensure the creation of all data sets needed for test execution. Suppose it is an acceptance testing phase. In that case, the test data management must propagate a data creation initiative that grabs all possible data types relevant to end-to-end acceptance testing.
Through a comprehensive analysis, every data element that will be a part of the test cycle must be identified and recorded in the test data management process.
Mimic the Production Scenario
Adding to the data analysis part, the production environment forms an equally important aspect of data organization. Having a clear idea of the production environment and then checking for missing data elements is vital. Once identified, they must be added to the test data management records.
Creating test data is not easy, especially with the volume and veracity of requirements in modern digital application testing initiatives. However, there is a silver lining to the cloud. Just like test automation, the creation of test data can also be automated.
From a test data management strategy perspective, this is a core activity. Automation reduces the number of errors that usually find their way into test data. By comparing different test results of consecutive test executions in the same test scenario, it will be easier to improve the accuracy of test cases. The best part is that the comparing part itself can be automated for a truly seamless experience.
Protect Confidential and Sensitive Data
Today, many enterprise applications run on the cloud or conform to the cloud-native paradigm. From a cloud-testing perspective, this implies using sensitive and private data in large volumes in the test environment to check and validate the performance of the cloud-based application.
In such cases, protecting sensitive data from leaks and unauthorized access within test environments is critical. Businesses must formulate a proper strategy to ensure that all participating entities in the test cycle adhere to data protection standards and guarantees.
Maintain a Centralized Test Data Repository
By maintaining a centralized test data repository, enterprises can significantly lower the time for future test initiatives as the data for testing will be readily available. When testing activities detect new data types, they can automatically move to the centralized repository. This setup facilitates better adoption of standards and compliance frameworks because of the centralized distribution of data.
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How Can Enterprises Build the Most Effective Test Data Management Strategy?
We have seen how the data management process in software testing is critical in improving the success rates of testing activities. However, most enterprises fail to build the right test data management strategy and ultimately lose out on competitiveness. The failure to devise a seamless test data management strategy can hurt the automation testing ambitions of the business significantly.
Hence, enterprises need access to test automation strategies that imbibe the principles of test data management. This is where ACCELQ can help make a difference. With our state-of-the-art automation testing platform, enterprises can build resilient test data management strategies and implement them for better digital ROI.
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