Based on our understanding at Austere Technologies, Test Data implies the measure of data that is produced for testing of the application. Test data administration is the way of outlining, arranging, driving and putting away programming quality-testing procedures and systems.
The world is moving towards growth of relevant data to create and use niche emerging technologies for various purposes. All businesses and sectors need to rely on data to package best customer experience for their clients. We are seeing scenarios of organizations working on continuously changing business goals to reach their end users expectation. Test Data Management (TDM) plays a role in sourcing the right data with ever changing needs to make sure software testing is done effectively.
In the needs of testing a mission critical software application, the QA teams need to be ready with the necessary test data at hand. If the right data is not available, it impacts the QA activity and inadequate test coverage. Test Data is volatile in nature as it constantly gets changed or versioned that needs to be referred again and again to determine the desired outcome.
Unavailability of test data or partial availability of test data will lead to delay in the SDLC of the application. It will impact the go to market strategy of the product and the marketing costs associated with it. Some basics related to test data like creation of test data, availability of test data, segmentation of test data and maintenance of test data need to be got right. This is nothing but Test Data Management (TDM). Test Data Management involves coordination of various work groups that needs to collate the test data in form of test cases and use the right set of tools to segment the test data for easy availability.
From our expertise, we outline the following strategies that can contributed to an effective TDM. The core theme is to get your basics right first, before innovating for more.
Continuous cleaning of test data:
Testing is a continuous activity and needs different versions of test data at every test node. It is important to keep a check on the current version of test data and once the test data is consumed at a certain level it needs to be flushed out to ensure the right test data only remains for the next test cycle. Continuous cleaning of test data is the first strategy to be employed.
Simulate the production environment:
Based on our understanding of the application we are building on and its expected performance in a certain environment, it would be advised to simulate the same production environment and then test the application with the similar test data as expected in the production environment. This will give the testing teams fair idea of the components involved and will enable them to create a checklist of the data that will be useful to test the application in real time at the time of delivery for its performance in real time.
Test data is generated from test cases that are built on end to end test scenarios to capture finding and be ready with inputs for documentation. With effective data analysis, the scattered data over different test cases across various test scenarios are collated with effective data analysis which makes the test data measurable, segmented for future reference.
Test Data Automation involves frameworks leveraged to automate development on test data. Frameworks throw up possible errors during test process which is further used to compare with other test data sets. Many scenarios of test data management can be automated to make the task easy and quick. This effectively brings down testing efforts and resources efforts involved in TDM.
At Austere Technologies, we have implemented robust test data management practices for complex enterprise platforms. To learn more on this please visit, www.austeretech.com