Table of Contents

    Table of Contents

    DevOps Test Data Management: Addressing the Challenges

    Ian Tick

    Ian Tick

    Head of Content, K2view

    DevOps is associated with frequent changes to applications, tools, and technologies, making it difficult to manage test data across multiple environments.

    Table of Contents


    What is DevOps Test Data Management?
    The DevOps Test Data Management Intersection
    The Need for Stable, High-Quality Test Data
    Managing DevOps Test Data Cross-Company
    Addressing DevOps Test Data Management Challenges
    DevOps Test Data Management Based on Business Entities

    What is DevOps Test Data Management?

    DevOps test data management is the process of generating, managing, and provisioning test data in DevOps environments. DevOps combines software development (Dev) and IT operations (Ops) to shorten and quicken the Software Development Life Cycle (SDLC). Test data – the data used to test software applications – must be accurate, complete, and up-to-date for effective testing.

    The use of test data management tools in DevOps environments can be challenging, due to frequent:

    • Changes to applications, making it difficult to keep test data fresh and timely

    • Use of different tools and technologies, which complicate test data management across diverse environments

    Get Gartner's latest report on test data management.


    The DevOps Test Data Management Intersection

    With DevOps-driven software development and agile methodologies such as the shift-left testing approach, software quality is gaining greater prominence. As the need for intelligent business ecosystems grows, organizations are bettering cross-team collaboration, connecting siloed systems, establishing CI/CD pipelines, and allowing for test data automation.

    Recently, there has been an observable trend towards an increasingly business-driven SDLC approach, and a commensurate rise in demand for end-to-end DevOps test data management software.

    By focusing on accelerating product releases, while giving QA its due importance, enterprises are looking to develop 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 and defining the appropriate test dependencies for end-to-end business flows and to perform the tests across various scrum teams and applications within the organization.

    The Need for Stable, High-Quality Test Data

    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. Test data should be available on demand 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 to have a seamless test data management process. Later, we'll take a deep dive into the challenges related to DevOps test data management, along with practical solution for each of them.

    Managing DevOps Test Data Cross-Company

    Testing requires test data and test environments to be as close as to production as possible to deliver flawless software applications to the end-user. However, due to security and compliance-related risks, production data isn't typically used for running the tests. Therefore, to maximize test coverage with compliant synthetic test data, testing teams are seeking the latest, and most innovative, test data preparation tools.

    As test data is gathered and stored from multiple sources, and in various formats, managing it 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 Personally Identifiable Information (PII) which must comply with privacy regulations, such as CCPR, GDPR, etc. The data needs to be of high quality to gain accurate test results, but it should also undergo data masking for security purposes to use such techniques as substitution, shuffling, and encryption. However, multi-level encryption methodology means the business’s data is secured individually, thus making massive breaches virtually impossible.

    Addressing DevOps Test Data Management Challenges

    To reap the many potential test data management benefits, testing teams should be empowered with provisioning  test data on demand, while trusting it is high-quality and compliant. Moreover, DevOps teams should be able to integrate test data provisioning into their CI/CD pipelines. A DevOps TDM tool enables organizations to:

      • Identify the source, volume, complexities, data subset criteria, and data synthesis needs.


    • Employ data masking tools, and/or synthetic data generation tools, to maintain data privacy by protecting sensitive or personal information.

    • Automate the test data provisioning process as much as possible, to reduce manual, error-prone processes.

    • Achieve a return on their TDM investment, measured in user productivity, reduced testing costs, increased compliance, and faster software delivery.

    For CI/CD pipelines to run smoothly, continuous testing is essential. Otherwise, it might cause the entire test automation suite to come to a standstill, thus compromising the quality of the released software applications.

    DevOps Test Data Management Based on Business Entities

    The entity-based test data management approach can improve compliance, optimize storage, and spending, and enhance the end-user experience. A test data management strategy based on business entities, allows enterprises to efficiently satisfy their DevOps test data needs by taking care of data profiling, analysis, governance, provisioning, generation, environment management, and privacy.

    An entity-based approach bridges the gap between application-centric and data-centric models. By using business entities (digital versions of a person, place, or thing), DevOps teams get holistic, real-time access to their test data.

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