Hexaware Positioned as a Visionary in the 2026 Gartner® Magic Quadrant™ for Custom Software Development Services
Hexaware Positioned as a Visionary in the 2026 Gartner® Magic Quadrant™ for Custom Software Development Services
Gartner, Magic Quadrant for Custom Software Development Services, By Jaideep Thyagarajan, Ryan McKinney, Nathan Davie, 7 October 2026. Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Hexaware.
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– Test data management covers preparing realistic, non-production data for development and QA.
– Five best practices include discovering, subsetting, masking, refreshing, and comparing test data.
– IBM’s InfoSphere Optim creates referentially intact, right-sized test databases across platforms.
Test data management is the process of creating realistic test data for non production purposes such as development, testing, training or QA. A better test data management strategy not only ensures greater development and testing efficiencies, but helps organizations identify and correct defects early in the development process, when they are cheapest and easiest to fix
Typically, test data management involves two major activities: test data preparation and test data usage. Test data preparation involves manufacturing data by copying or sub setting data from production or by developing test data generation scripts and provisioning them for multiple testing environments. Referential integrity, data quality and data relationships must be retained during the preparation stage. The skills required to complete these tasks typically lie with DBAs, since they are the ones with knowledge of the underlying data model. Test data usage shifts focus to the tester or developer, who may not be database-savvy. This may create inefficiencies because the tester or developer absolutely requires proper test data and if this test data is not available, the tester must go back to a DBA for help. The tester understands “test conditions” and tries to map those to accurate, physically available data in the test environment. The tester’s mission is to ensure safe passage of the required tests, not to create high-quality, referentially intact test data.
When implementing a test data management approach, five best practices help streamline test data preparation and usage:
The IBM InfoSphere Optim Test Data Management solution offers comprehensive test data management capabilities for creating right sized, fictionalized test databases that accurately reflect end-to end business processes. InfoSphere Optim software scales to meet development and testing requirements across multiple applications, databases, operating systems and hardware platforms. It also helps facilitate modern software delivery models including agile development by making test data continuously accessible to testers and developers so they can quickly meet test requirements.
Organizations eliminate costly cloning processes and correct defects early in the development process. InfoSphere Optim scales to across applications, databases, operating systems and hardware platforms. Secure your test environments, improve quality, accelerate release cycles and reduce costs with InfoSphere Optim.
Below diagram illustrates a typical Optim Test Data Management Process: