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Test Data Management Services

Ensuring comprehensive coverage with the right set of data is critical for effective testing. Many product defects are caused by inadequate test data coverage. Our Test Data Management services include data masking and synthetic data generation. We help businesses mitigate risks and ensure they stay compliant with data privacy regulations, leading to robust and reliable testing outcomes.

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Test Data Management Services

Ensuring comprehensive coverage with the right set of data is critical for effective testing. Many product defects are caused by inadequate test data coverage. Our Test Data Management services include data masking and synthetic data generation. We help businesses mitigate risks and ensure they stay compliant with data privacy regulations, leading to robust and reliable testing outcomes.

Enquire Today Contact US

Test Data Management Services

Ensuring comprehensive coverage with the right set of data is critical for effective testing. Many product defects are caused by inadequate test data coverage. Our Test Data Management services include data masking and synthetic data generation. We help businesses mitigate risks and ensure they stay compliant with data privacy regulations, leading to robust and reliable testing outcomes.

Enquire Today

Current Challenges with Test Data

Mocked or synthetic data

Mocked or synthetic data refers to artificially generated data that mimics the characteristics and patterns of real data. The purpose of synthetic data is to provide a substitute for real data in situations where privacy concerns, data sensitivity, or data availability pose limitations.

Synthetic data is typically designed to meet specific requirements but often falls short in covering error, edge, or alternate conditions. It lacks the richness and variation found in production data, leading to potential gaps in testing scenarios. Generating the necessary data for volume and performance testing can be both costly and time-consuming. According to Gartner's 2022 research, there are over 40 vendors in the synthetic data space, most established since 2018, highlighting the growing yet challenging landscape of synthetic data generation.

    Current Challenges with Test Data

  • Mocked or synthetic data

    Mocked or synthetic data refers to artificially generated data that mimics the characteristics and patterns of real data. The purpose of synthetic data is to provide a substitute for real data in situations where privacy concerns, data sensitivity, or data availability pose limitations.

    Synthetic data is typically designed to meet specific requirements but often falls short in covering error, edge, or alternate conditions. It lacks the richness and variation found in production data, leading to potential gaps in testing scenarios. Generating the necessary data for volume and performance testing can be both costly and time-consuming. According to Gartner's 2022 research, there are over 40 vendors in the synthetic data space, most established since 2018, highlighting the growing yet challenging landscape of synthetic data generation.

Production Data

Using production data for testing necessitates masking and synchronising across all systems, a process that is both costly and requires specialised skills, platform, and domain knowledge. This synchronisation is crucial to maintain data integrity and compliance but often proves to be a resource-intensive task. For lower environments, a cut-down version of production data may be needed, but achieving a synchronised, sub-set of masked production data is not always feasible and is invariably time-consuming.

  • Production Data

    Using production data for testing necessitates masking and synchronising across all systems, a process that is both costly and requires specialised skills, platform, and domain knowledge. This synchronisation is crucial to maintain data integrity and compliance but often proves to be a resource-intensive task. For lower environments, a cut-down version of production data may be needed, but achieving a synchronised, sub-set of masked production data is not always feasible and is invariably time-consuming.

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With potential fines of up to $50M for data breaches under upcoming privacy legislation amendments, safeguarding your data has never been more critical. Planit’s robust test data management solutions ensure your data is protected, compliant, and effective for thorough testing.

How can we help?

Enquire Today

Ready to deliver more ambitious technical outcomes at improved efficiency? At Planit, we have over 2,000 consultants at the forefront of quality engineering and assurance. Every day, they test themselves on a variety of challenging projects across our top-tier client base.

Contact us to find out how our Quality Engineers and Software Developers in Test can introduce innovative and right-size testing practices, tooling, and automation solutions to optimise your lifecycle.

Featured Insights

Enquire Today

Ready to deliver more ambitious technical outcomes at improved efficiency? At Planit, we have over 2,000 consultants at the forefront of quality engineering and assurance. Every day, they test themselves on a variety of challenging projects across our top-tier client base.

Contact us to find out how our Quality Engineers and Software Developers in Test can introduce innovative and right-size testing practices, tooling, and automation solutions to optimise your lifecycle.