Effective Steps to Improve Data Testing
Effective Steps to Improve Data Testing

When you migrate to a new application software, you may need to extract data which is done with the help of thorough testing. But when the data is too large, the testing team will find it difficult to determine how much to test or even what to test. Even creating random test cases could be overwhelming in such a scenario. According to the software testing UAE community, this is often one of the biggest challenges for testers especially when the project deadline is close and the data being sent is wrong. 

Here are a few steps that can help in such scenarios for the testers to understand what to test, how much to test, and when to stop testing.

Ensure that everyone involved understands the requirements

This is important particularly in Agile environments. The whole team including developers, testers, and even clients should have the same template or the same requirements document they can take personal notes on. 

Companies in the application software development Dubai sector has finally started adopting Agile methodology on a wide scale. Such dynamic environments require the shared requirements template to be continually updated on the go whenever changes or decisions are made. In addition, the testers should be given more time for bugs and to clarify misunderstandings should any arise. 

Creating a test plan and deciding what to test

Data testing is technically quite basic – you have data and you test it. So many companies don’t bother creating test plans. A proper testing gives testers a good idea on what to test and how much to test. They would also know when they should mark tests as passed. It also helps programmers make sense of what’s being tested, so they can share their thoughts and insights as well. 

Before deciding what to test, the team should ensure whether the data provided is valid and that the data format is correct. 

Deciding how much to test

This step is where they can create test cases while making sure that the riskier areas are addressed. It may not be possible to test everything when large volumes of data are involved. The best approach is to identify critical areas of the data, like for instance the area that is more prone to errors (risky areas).

If there are test cases for these specific areas, it’d help testers test all the important data in the project. If there are still too many areas, then there would be too many test cases. In such scenarios, it’d be best to categorize the test cases and determine priorities. After this, the testers can choose a few from each categories depending on the priority. This can be logged into the test plan to share with everyone involved in the project for their insights. 


These steps can only give the testing team a solid start. With support from an experienced testing lead, the team can improvise their strategies and have data testing done without much challenges. For this tactic to work effectively, the software development company or the testing company should stick to Agile practices.