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Data Governance in the AI Era: 3 Big Problems and How to Solve Them
- The Great Data Debate focused on the challenges of data governance in the AI era.
- Key experts including Tiankai Feng, Sunil Soares, Sonali Basak, Bojan Simic, and Brian Ames discussed evolving governance needs.
- Three major data governance problems were highlighted during the debate.
- The first problem discussed was data governance being treated as an afterthought, rather than a proactive approach from the start.
- The panel emphasized the need to integrate governance into processes early on and tie it to business outcomes to shift from a reactive to proactive approach.
- AI's introduction has made governance more challenging by amplifying flaws in data and creating new risks like data bias and lack of explainability.
- To govern AI effectively, organizations need proactive AI governance strategies, automation, and clear policies defining AI boundaries.
- Another key issue highlighted was the resistance to traditional governance methods due to their manual, slow, and disconnected nature.
- To make governance seamless, experts suggested automating processes, integrating governance tools into existing workflows, and leveraging AI to reduce manual efforts.
- The importance of embedding governance into daily workflows, letting AI govern AI, tying governance to business impact, and investing in AI governance was underlined in the debate.
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