The history and limitations of legacy test data management tools
Legacy TDM tools were introduced in the 1990s and early 2000s to support manual testing by anonymising, subsetting, and copying production data to non-production environments. These tools focused on data privacy and storage efficiency, using “mask and copy” methods to reduce risk and cost.
Modern software delivery has introduced new demands, including agile methodologies, CI/CD pipelines, automated testing, and the need to support cloud-native architectures and diverse data types. The rise of Artificial Intelligence (AI) and Machine Learning (ML) enablement has further increased the need for high-quality, representative, and compliant synthetic data.
Legacy TDM tools, built for a different era, lack the flexibility and scalability required today. As a result, they often become bottlenecks in environments that demand speed, precision, and innovation. The world has changed, your test data strategy should too.