Group 1 - Governance is perceived as a barrier rather than a facilitator, often leading to delays and workarounds in data access [1][2] - The DAMA model provides a structured approach to understanding governance beyond just access control, emphasizing the importance of trust, traceability, and long-term maintainability [4][8] - Governance encompasses not only who can access data but also who is responsible for managing and making decisions about that data [5][6][9] Group 2 - The DAMA framework outlines 11 distinct areas of data management, with governance at its core, integrating various aspects such as data architecture, metadata management, and data quality [12][13] - Metadata serves as the system's memory, enhancing data discoverability and reducing reliance on tribal knowledge, while lineage provides visibility into data processes and transformations [14][16][20] - Quality must be embedded in the design of data systems rather than being an afterthought, with clear expectations set from the outset [21][24][26] Group 3 - Security and classification should be integral to system design, ensuring that data is appropriately labeled and governed from the start to prevent misuse [27][30] - Machine learning governance presents unique challenges, necessitating a focus on model behavior, version control, and accountability [31][34] - A governance-first design checklist can help organizations ensure that their systems are built with governance principles in mind, promoting long-term sustainability [35][38][39]