With a DevOps-driven software development ecosystem and the introduction of shift-left approach, software quality is gaining greater prominence. As the need for building a smart and connected business ecosystem arises, organizations are advocating for increased collaboration across teams, dissolving the organizational silos, establishing CI/CD pipeline, and automating the majority of their software development and software testing life cycle. Recently, there is an observable, growing trend toward an increasingly business-driven approach, rise in demand for end-to-end testing, and interest in AI.
By focusing on accelerating product releases, while giving QA its due importance, global businesses are looking todevelop a test data management strategy that can help them build a cohesive test environment. Such an environment is essential for empowering the QA teams to close the gap between identifying & defining the appropriate test dependencies for the end-to-end business flows and to perform the tests across various scrum teams and applications within the organization.
For a test cycle to be effective, whether it is manual or automated, the most critical aspect is the availability of a stable test data that is as close to production as possible. In fact, the key challenges faced by organizations seeking to automate testing today are the testing environment and the test data itself. Further, DevOps requires automation to be successful, and that automation needs data to run smoothly, and it needs to be able to access qualitative, consistent, and predictable sets of data. Adequate & on-demand test data should be available for running fully automated test suites, and the test data should not limit or constrain the automated tests that can be run by the team in order to have a seamless test data management process. Let us take a deep dive into the challenges related to test data in DevOps while proposing a practical solution for each of them.
DevOps Test Data Challenges
Testing requires test data and test environments to be as close as to production to deliver flawless software applications to the end-user. However, due to security and compliance-related risks, the production environment, or the production data, is not used for running the tests. Therefore, to maximize test coverage with excellent test data without compromising on the regulatory aspects, it is imperative to embrace a test data management strategy.
Test data management ensures sufficient and on-demand availability of the data as needed by the automated DevOps cycles. Usually, one of the primary challenges faced during test automation is either restricted usage of the data available for running a complete automation suite or lack of processes to dispose of the test data when it is no longer required.
As test data is gathered and stored from multiple sources and in various formats, managing them becomes complex as the data needs to be altered as changes are made in testing. Sometimes, if vast volumes of data are used for validating the business process, it can result in overtly time-consuming and costly test cycles.
Organizations often lack the techniques to handle the Personal Identity Information (PII)-related data which is required to be compliant with regulatory bodies, such as HIPAA, PCI, GLBA, SOX, etc.The data needs to be of high quality to gain accurate test results, but it should also be properly masked for security purposes by using techniques such as substitution, shuffling, and encryption. However, K2View TDM’s multi-leveled encryption methodology means the business’s data is secured individually, thus making massive breaches virtually impossible.
Resolving the challenges
Taking a ‘Center of Excellence’ approach for test data management can enable organizations to create and maintain real-time test data from production systems, ensure consistency, and deliver it with an as-a-service basis to scrum teams. With a well-developed test data management framework in place, organizations can:
- Identify the source, volume, complexities, data subset policies, and data synthesis needs.
- Measure data health in each environment and use reduced volumes of production data when a full load is not required.
- Use data masking, substitutions, or encryption techniques to maintain data security by protecting sensitive data.
- Reduce the manual process of creating test data and minimize the possibility of errors.
- Promote reuse of subset criteria and masking policies on various sources.
For the CI/CD pipeline of DevOps to run smoothly, continuous and on-demand supply of test data is essential. Otherwise, it might cause the entire test automation suite to come at a standstill, thus compromising the quality of the released software applications.
How Can We Help
Test data management can improve compliance and optimize storage, spending, and enhance the end-user experience. A robust test data management strategy allows enterprises to efficiently satisfy the test data needs in their DevOps automation cycle by taking care of data profiling & analysis, governance, provisioning, generation, environment management, and privacy.
K2View also bridges the gap between application-centric and data-centric approaches. By using the Digital Entity, which is the digital version of a person, place, or thing, K2View gives organizations holistic, real-time access to their data. To learn more about K2View’s TDM capabilities, view the demo here.