Ep. 9 Why Enterprises Using Generative AI Are Hitting a Data Wall | AI Insights and Innovation

In this episode of AI Insights and Innovation, David Linthicum, principal analyst at theCUBE Research, delves into the essential challenges enterprises face with data when transitioning to generative AI systems. Linthicum, a leading cloud architect and AI expert, shares his perspectives, underscoring the critical need for strong data management to unlock AI's potential.
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The discussion highlights the pervasive issues of data silos, poor data hygiene and the necessity for a common semantic understanding of information. Linthicum emphasizes the importance of transforming data architecture to avoid technical debt, integrate data sources effectively and secure the systems against growing cybersecurity threats.
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By leveraging small language models for practical applications and making informed architectural decisions, enterprises can strategically harness the power of generative AI. Gain actionable insights on building a solid data foundation for AI success and navigating the complex landscape of modern data-driven technologies.
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[@Abhinayanagarajan-x8p]

Great insights on the challenges enterprises face with generative AI! SymthOS always brings valuable perspectives.

[@SridharIyengar]

Practical advise to address the data problem - was true in 1980 , true now will be in 2050 - no short cuts to good architecture and good data design

[@djerhyjnbaptiste4030]

Not sure how relevant that is re data quality: the fact that a lot of company owned data will be AI generated soon, with all the inaccuracies and inherent problems we are seeing now.

[@erekthus]

Sounds like a political mine field to access the right data you need for your AI to work properly.