In the dynamic landscape of software development, the significance of effective Test Data Management (TDM) cannot be overstated. Traditional approaches, such as IBM InfoSphere Optim, have long been the backbone of this crucial process, ensuring data integrity and reliability. However, as organizations embrace the modern Data Development Stack, characterized by the complexities of machine learning (ML) and advanced analytics, a pivotal question emerges: Can traditional TDM keep pace with the evolving demands of the digital era?
In this whitepaper it is covered a comparision, including the strengths and limitations of traditional TDM, through IBM InfoSphere Optim, with the progressive paradigm of AI-driven synthetic data generation, through YData Fabric.
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