In the rapidly evolving landscape of applications and data processing, the traditional approach to managing test data no longer aligns with the continuous, dynamic flow of information across diverse domains.
At least, that is according to Adhisivan Ragunathan, vice president delivery at Qualitest, the New Jersey-headquartered quality assurance vendor, as he singled out two large insurers in the UK and US as how his company approaches test data management.
The current methods of test data management, production golden copy and custom scripting, are proving “inadequate” for the speed and quality demands of today, Ragunathan argues.
“Hence, there is a pressing need for a paradigm shift in our test data management practices to accommodate modern data requirements,” he said.
“This transformation is particularly crucial as the traditional golden copy-based data refresh proves to be time-consuming and less effective. It’s time to embrace a more agile and efficient approach to ensure our readiness for the future,” Ragunathan added.
Obstacles
As the most common constraints in traditional test data management, Ragunathan singled out time wasted on querying database to identify required test data conditions.
Also, concerns about data security when utilizing production data with respect to GDPR and other regulatory requirements are major obstacle, “often necessitating waivers and additional safeguards in lower environments,” he said.
Then there is handling large datasets, as it poses challenges in terms of transfer and storage capacity, Ragunathan noted.
Finally, a lack of support for Agile and DevOps methodologies as well as difficulty in providing test data for Enterprise Commercial off-the-Shelf (COTS) products are major constraints, he shared.
“Beyond these constraints, firms often hesitant to consider transformations due to their significant investments in existing TDM legacy tools, with the entire ecosystem and resources built around them, acting as a key deterrent to considering transformation of this area,” he explained.
According to Ragunathan, navigating these constraints calls for a strategic re-evaluation of test data management practices to align with contemporary needs and industry best practices.
“The core concept behind next-gen TDM is dismantling the barriers between TDM operations teams and data consumers by introducing a more self-serviceable approach to data search, creation, and provisioning,” he argued.
“There is a pressing need for a paradigm shift in our test data management practices to accommodate modern data requirements.”
– Adhisivan Ragunathan
Additionally, he called for “a shift from relying on production golden copies to leveraging more AI-infused synthetically generated data for testing purposes.”
Ragunathan also thinks it is time for synthetic test data generation to gain momentum.
“Move away from relying on production data for the test environment, leverage synthetic test data generation which is much cheaper and faster to provision.”
In addition, firms should embrace “containerised” test data.
“With a micro-services architecture, it is convenient to provision ephemeral test datasets to swap it with a new dataset for every test cycle,” he noted.
“It opens an opportunity to create separate test data sources for each data consumer when needed.”
Then integrate the test data solution into the continuous integration and deployment pipeline, rather than operating as a standalone activity, Ragunathan continued.
“This contributes to enhancing fully automated data provisioning,” he said.
Finally, data virtualization should be taken seriously.
“Use of data virtualization technique to create VDBs in minutes instead of hours of data-copying activities. It helps in speeding up data provisioning and reducing storage spending,” he stated.
Insurance space
Ragunathan claimed that Qualitest recently demonstrated its ability to meet test data requirements in a micro-services architecture.
“We implemented a solution using synthetically generated data for a prominent insurance provider based in the UK,” he said, without disclosing the insurance firm in question.
Specifically, we collaborated with our partner tool, GenRocket, to create test data for insurance claims,” Ragunathan added.
He said the solution integrates with the Continuous Integration (CI) pipeline, offering on-demand test data generation.
“It goes beyond mere generation by intelligently ingesting the necessary data conditions within the system, ensuring a comprehensive and tailored approach to fulfilling the specific needs of the microservices-based architecture for our client in the insurance industry.”
“It’s time to embrace a more agile and efficient approach to ensure our readiness for the future.”
– Adhisivan Ragunathan
In addition, Ragunathan was keen to single out another case study, namely a US-based insurance company.
He said that, earlier this year, Qualitest implemented a hybrid test data management solution for “a prominent US health insurer.”
Established within just two weeks, this solution equips them with the necessary capabilities to perform synthetic test data generation in the X12 837P format, Ragunathan disclosed.
“Additionally, it offers a generation-based masking solution to replace their conventional masking approach.”
He added: “With this solution, we successfully generated numerous sample EDI files within seconds. It enables the customer to leverage reference datasets for creating fully integrated and valid test data for consumption.”
“Furthermore, the solution seamlessly integrates with CI toolsets, allowing the on demand generation of test data,” Ragunathan concluded.
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