5 Best Practices For Test Data Management

Many organizations these days have abandoned the waterfall approach when it comes to app development and delivery. Now, they are getting more into agile methodologies and realize the need for continuous testing. The change from the old approach to a more dynamic, flexible development process means they need rapid access to the appropriate test data. Putting a test data management approach into use involves a few different steps that can help you simplify the testing process with five best practices to test data management before you go into production.

1. Discover and understand your test data.

Data comes in different formats and is typically scattered across systems. On top of that, different rules can be applied to your data depending on its location and type. You need to identify your test data requirements based on the test cases; this means you have to capture the end-to-end business process and the associated data. For example, you might have an inventory management app, a CRM (customer relationship management) system, and a financial app, all of which are related and will require test data.

2. Extract a subset of production data from different sources.

The extraction of a subset of data is meant to ensure referentially intact, realistic test data from across a varied data landscape without adding administrative challenges or costs. The best way to do this is to obtain metadata in the subset in order to accommodate data model changes both quickly and accurately.

3. Mask or de-identify sensitive test data.

Masking will help you secure sensitive client, employee, and corporate information and also helps you to ensure compliance with both industry and government regulations. Your capabilities for de-identifying confidential data should consistently mask complete business objects across your test systems.

4. Automate actual and expected result comparisons.

It is essential to be able to identify data inconsistencies and anomalies during testing. This helps to measure the overall quality of your application. The most efficient way to achieve this is by using an automated capability for comparing your baseline test data against results from previous test runs. Speed and accuracy are essential here. The automation from these comparisons will help you save time and to identify any problems that you might otherwise miss.

5. Refresh your test data.

Test data often diverges from the baseline during the testing process. This can give you a less-than-optimal test environment. When you refresh your test data, this can help you improve your testing efficiencies and streamline your entire testing process while, at the same time, keeping a manageable and consistent test environment.

Why Test Data Management Is Important

Proper management of your test data is essential if you want to have cost-effective continuous testing. When you lack a test data management strategy aligned to your business results, you might suffer from issues like:

  • requests for test data that come too late;
  • too many redundancies;
  • a lack of efficient processes to provide test data;
  • old test data being used;
  • high number of defects from using old test data; and
  • security risks.

Hidden costs as a result of these issues can be attributed to the following source:

  • time spent manipulating data so it will work for different testing scenarios;
  • the labour needed to move data back and forth from back-end systems;
  • the time needed to identify the right data needed for tests;
  • production defects not tested due to lack of test data availability;
  • storage space for test data;
  • masking sensitive data in order to protect privacy; and
  • skipped test scenarios.