Where Do Companies Go Wrong When Implementing AI and Data Governance?

Описание: Join the Insight Jam today! — https://insightjam.com/

Can generative AI create a new push for better data quality?
How can companies balance the need for re-architecting with the urgency to adopt new technologies?

Our expert panel discusses common challenges companies face when implementing AI. They emphasize that poor data quality and inadequate data infrastructure are major hurdles in successful AI implementation. GenAI might help improve data analysis and quality, but new AI initiatives may require re-architecting data systems. The conversation also touches on the benefits of adopting streaming architectures and the importance of balancing comprehensive re-architecting with tactical, incremental improvements.

This is a key takeaway from the Insight Jam 2024 Q3 Mini Jam panel, "AI in Data Lifecycle Management: Automating Risk, Compliance & Security". Check out the full discussion here: https://youtube.com/live/OMXKDK_bq_0

Panelists:
Mark Diamond - President & CEO - Contoural
Dave Cameron - Analytcs Strategy Consultant & Educator - ‪@UChicago‬
David Loshin - Principal Consultant - Knowledge Integrity, Inc
Eric Kavanagh - Host - ‪@InsideAnalysis-DMRadio‬
William McKnight - President - McKnight Consulting Group
Yves Mulkers - Founder - ‪@7wData‬

0:00 Common challenges in AI implementation
0:59 Generative AI's impact on data quality
1:36 Importance of data architecture in AI projects
2:44 Re-architecting data systems: necessity or luxury?
3:24 Benefits of streaming architectures
4:25 Balancing re-architecture and tactical improvements
5:14 Importance of expertise in data transformation