Super Data Science: ML & AI Podcast with Jon Krohn
846: Making Enterprise Data Ready for AI, with Anu Jain and Mahesh Kumar
Dec 20, 2024
Anu Jain, CEO of Nexus Cognitive, and Mahesh Kumar, CMO of Acceldata, delve into transforming enterprise data for AI success. They discuss the critical need for updated data governance, emphasizing how minor data errors can drastically impact financial decisions. The conversation highlights the integration of composable data architectures and the role of data observability in ensuring high-quality data for AI applications. They also explore strategies to reduce vendor lock-in and enhance automation in data management.
18:04
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Quick takeaways
- Accurate and up-to-date data is essential for AI models, as even small errors can lead to significant financial losses for enterprises.
- The shift from centralized to decentralized data governance requires integration of automation to enhance compliance and maintain data quality across platforms.
Deep dives
Impact of Small Data Errors
Small data errors can significantly impact AI models, leading to potential financial losses for enterprises. For instance, one example highlighted involved a major bank using outdated credit scores, which resulted in costly miscalculations in loan approvals. This emphasizes the critical need for accurate and up-to-date data to ensure AI models function effectively. By implementing data observability platforms, organizations can proactively identify and correct these errors before they escalate into larger issues.
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