Data and AI practices share a deep connection. Every component of an AI system can be understood as a combination of data and IT assets, making AI systems inherently subject to data management and governance principles. However, this does not imply that AI and data governance and management must be fully integrated. Instead, it highlights the need to ensure their alignment is clearly defined and effectively established.
This series of articles aims to:
- Analyze and compare various regulations related to data and AI
- Explore existing industry frameworks for data, AI, and associated risk management and governance
- Present a method to harmonize frameworks for data, AI, and risk management
- Evaluate real-world use cases for data and AI strategies
By addressing these objectives, this series provides actionable insights to help organizations navigate the interdependencies between data and AI governance and management.
December 2024
AI Systems and Data & IT Products: Finding Common Ground
This article discuss the common grounds of AI systems and data products & assets. In the previous article of this series, I demonstrated that various legislations worldwide take significantly different approaches to defining an AI [...]
AI Regulations Globally: Same Goal, Different Paths
This article discusses the differences in AI regulations. In the previous article, “Harmonizing Data and AI Governance: To Do or Not To Do,” I discussed five key factors that influence the decision on how to [...]
October 2024
Harmonizing Data and AI Governance: To Do or Not To Do?
Harmonizing Data and AI Governance: "To Do or Not to Do"? This is a question every data management professional should ask. This article summarizes the key topics of my presentation at the upcoming DG&AI Conference [...]
