A blog about Product Management, Data Management and Self-Driven Learning
Generative AI requires a distinct design approach compared to traditional machine learning, emphasising the need to classify use cases by their impact: informational, decision-support, or action-taking. Organisations should establish guidelines addressing acceptable error levels, prohibited areas, and human oversight, enabling effective management of risks associated with GenAI outputs.
The DASUD framework—Design, Acquire, Store, Use, Delete—serves as a valuable model for AI governance, enhancing existing data management practices. It outlines a structured approach to integrate governance throughout the AI lifecycle, ensuring clarity in decision-making, accountability, and risk management while adapting familiar processes for AI applications.
After launching the DASUD Framework, I am now focused on AI governance, emphasising the importance of understanding fundamental problems to govern efficiently. I highlight the risks of multi-agent systems compromising data security and stress collaborative governance solutions. The piece invites readers to seek guidance on Data Governance and related topics.
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.
High-level architecture diagrams offer crucial insights into data flow and connectivity within organisations. They enhance clarity, facilitate collaboration, and promote scalability. Adopting standards like BPMN can streamline processes, while tools such as Microsoft Visio aid in creating these diagrams. Regular updates ensure accuracy, supporting effective data governance strategies.
