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Traditional vs Modern Test Data Management with Synthetic Data

Test data management; synthetic data; quality assurance; data generation

Cover Photo by Avery Evans on Unsplash

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. 

Download this whitepaper to learn more about:

  • What are the new requirements for Test Data Management under the context of data-driven solutions development
  • The different types and applications of Synthetic Data
  • What is YData Fabric and how does it compare to IBM InfoSphere Optim in the context of data management
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