Our focus is on researching and classifying anomalous behavior/error conditions across complex data lifecycles. We take charge of creating classifiers using AI algorithms, delving into methodologies to mitigate data quality degradation, and collaborating closely with the Human-centric Tools work package when human intervention becomes necessary. We contribute to establishing efficient data transfer infrastructure between data stores, which involves intelligent data placement and ensuring secure connectivity. Furthermore, we assist in providing an experimental data space as a service for companies interested in data economy and sharing. This allows for testing of business concepts and proof of concepts before implementing them in real-life scenarios.