3383: Denodo Technologies On Balancing AI Innovation With Data Integrity Tech Talks Daily podcast

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3383: Denodo Technologies on Balancing AI Innovation with Data Integrity

19h ago 33:39

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In this episode, I am joined by Charles Southwood, Regional GM of Denodo Technologies, a company recognised globally for its leadership in data management. With revenues of $288M and a customer base that includes Hitachi, Informa, Engie, and Walmart, Denodo sits at the heart of how enterprises access, trust, and act on their data. Charles brings over 35 years of experience in the tech industry, offering both a long-term view of how the data landscape has evolved and sharp insights into the challenges businesses face today.

Our conversation begins with a pressing issue for any organisation exploring generative AI: data reliability. With many AI models trained on vast amounts of internet content, there is a real risk of false information creeping into business outputs. Charles explains why mitigating hallucinations and inaccuracies is essential not just for technical quality, but for protecting brand reputation and avoiding costly missteps. We explore alternative approaches that allow enterprises to benefit from AI innovation while maintaining data integrity and control.

We also examine the broader enterprise pressures AI has created. The promise of reduced IT costs and improved agility is enticing, but how much of this is achievable today and how much is inflated by hype? Charles shares why he believes 2025 is a tipping point for achieving true business agility through data virtualisation, and what a virtualised data layer can deliver for teams across IT, marketing, and beyond.

Along the way, Charles reflects on the industry shifts that caught him most by surprise, the decisions he would make differently with the benefit of hindsight, and the one golden rule he would share with younger tech professionals starting their careers now.

This is a conversation for anyone who wants to understand how to harness AI and advanced data integration without falling into the traps of poor data quality, overblown expectations, or short-term thinking.

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