Best Practices in Test Data Provisioning for Automated Testing

In part one of this series, we covered the importance of automated testing in the FinTech space.

In this segment, we highlight best practices in test data provisioning, an essential component of successful automated testing.

To meet the demands of business growth and customer satisfaction, technology teams are adopting Agile and DevOps methods to keep up with the relentless pace of software development and enhancements required to support growth.

In order to transform to a true DevOps model and reap the benefits of this approach, development and QA/testing teams are shifting testing to the left, automating more tests and automating the provision of test environments. These are all designed to make them more productive and reduce the time it takes to develop, test and deploy.

While development and testing methods are getting a makeover, access to test environments and data continues to be a huge hurdle for QA & Testing teams.

The 2018-2019 Capgemini World Quality report states that nearly 50% of all respondents cite lack of access to test environments and test data as their biggest challenge in applying testing in agile development.

Lean/Agile and DevOps organizations face a difficult challenge to provision the right, high-quality data necessary to produce quality results; use an actual copy of production data and put the organization at risk or accept incomplete or inaccurate data. Neither of these options are good ones.

It is time for a new model for test data acquisition. This new model must result in the right data at the right time.

In the new model consumers of the data will identify the data needed to satisfy specific test cases when the test cases are built. The data will be tagged to the test case for later use. If the data is sensitive, it will be de-identified prior to viewing by the consumer. At the time of testing it will be loaded to ephemeral environments or traditional test environments.

To make this new model robust, development teams need to work with the latest solutions and methodologies to extract large volumes of data from production environment and sanitize the data to remove any personally identifiable information. This requires a significant up-front investment. If you can invest in off-the-shelf solutions to robustly protect your production data while sub-setting parts of it for testing easily, you can create realistic datasets with all the nuances of real data without the risks. By using a robust Test Data Management provisioning tool, one can enter data requirements through data selection, obfuscation of sensitive data like names, accounts, PCI using consistent, easily available, algorithms and then land the data in your testing environments ready to use.

An early investment in test data provisioning will result in robust test data and successful automated testing, allowing your FinTech organization to rapidly scale in today’s demanding world.