Catalog & Cocktails: The Honest, No-BS Data Podcast

In this special rant episode, Sonny Rivera joins Juan and Tim during a live recording and rant about AI-generated dashboards, semantic layers, and what it takes to trust an answer. The situation: dashboards slop is easy to build which makes foundation matter more, not less. We discussed:  Building a dashboard was never the hard part. Being able to explain where a number came from is. Speed without a governed foundation produces wrong answers faster. As Sonny put it, “fast and wrong is still wrong.” Modeling data is really modeling the business. The job has not changed. How it gets done is changing. A single source of truth did not hold up. Favors an interoperable set of semantic layers and context, with shared definitions where people actually care. Logic written in the moment, in a dashboard or a pipeline, often never makes it back into the semantic layer. Closing that loop is the gap to fix. Friction between teams is a sign that a definition matters. Make the differences visible rather than forbidding them. Trust is what data leaders can least afford to lose, which is why testing belongs in dashboards too. Writing code is no longer the slow part. Product management and testing are becoming the bottlenecks. See omnystudio.com/listener for privacy information.

Nick Zervoudis, data product manager, trainer, and consultant at Value from Data & AI, joins Juan and Time to talk about why many data teams end up working like a service desk and what to do about it. The answer is: DO DISCOVERY! In this conversation, we cover how a service desk model breaks down when a request can’t be described in a few words, why dashboards get opened once and then forgotten, and how product thinking differs from project management in data work. We also discuss how to start doing discovery without a full transformation, and what AI does and doesn’t change about that work.See omnystudio.com/listener for privacy information.

This is the takeaway episode with Nick Zervoudis, data product manager, trainer, and consultant at Value from Data & AI, joins Juan and Time to talk about why many data teams end up working like a service desk and what to do about it. The answer is: DO DISCOVERY! See omnystudio.com/listener for privacy information.

Mohammed Aaser, former chief data officer at McKinsey and former chief data strategy officer at Domo, recently visited Juan and shared his framework on talent in the AI era. It was incredibly insightful and we asked if he could come back to the podcast (after 5 years!) His argument is that the potential of AI will be unlocked by humans because humans understand the problems, and are driven/motivated to solve the problems.  In this episode, Mohammed lays out a framework for talent in the AI era. Automation is already taking over the known, repeatable work. What remains is the unknown, and that takes people who keep learning and who take initiative. The framework plots people on two dimensions, learning and drive. You have Doers, Philosophers, Commanders, Hackers, Strategist, Innovators, Game changers and more. Topics discussed Automation and the innovation engine The known versus the unknown Two dimensions of talent The talent quadrants The multiplier effect Commanders and scaling Environments that don’t build initiative Rarity of high-potential people Investing in human potential The human mind as the limit Critical thinking through discomfort See omnystudio.com/listener for privacy information.

This is the takeaway episode with Mohammed Aaser, former chief data officer at McKinsey and former chief data strategy officer at Domo. He recently visited Juan and shared his framework on talent in the AI era. It was incredibly insightful and we asked if he could come back to the podcast (after 5 years!) His argument is that the potential of AI will be unlocked by humans because humans understand the problems, and are driven/motivated to solve the problems. See omnystudio.com/listener for privacy information.