A blog about Product Management, Data Management and Self-Driven Learning
The post discusses the importance of quality data in AI product management, outlining the DASUD model: Design, Acquire, Store, Use, and Delete. It emphasises defining success, collecting accurate data, securely storing it, ethical use, and timely deletion. Trust in data is crucial for effective AI implementation, ensuring good governance.
The DASUD Framework aims to launch customised Data Governance within six months, and this post outlines how it was completed in a medical research setting. It outlines various research types and their regulatory considerations, emphasising compliance, collaboration, and research impact. Implementing effective data management practices, including design, acquisition, storage, usage, and deletion, is essential for long-term governance success.
The DASUD framework, developed by Nigel D'Souza, accelerates Data Governance implementation within 6 months by contextualising organisational needs. It focuses on designing data strategies through five key questions: Design, Acquire, Store, Use, and Delete. Prior prerequisites include defining roles, classification, approval processes, prioritising issues, and fostering a change-ready culture.
Nigel D'Souza 