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

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
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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.

How do leaders preserve executive trust and credibility? Dora Boussias, executive advisor and former data executive returns to the podcast to unpack this question. Her answer: protect your judgement.
In this episode, Dora presents the trust equation that most leaders grew up with: expertise + track record + judgment. Dora’s point is that the equation still holds, but each part now means something different. Nobody will out-know AI, so expertise is less about what you know and more about asking the right questions and challenging assumptions. A track record is harder to read when you can’t tell whether a good result came from you or from the model. Judgment carries the most weight now, and it’s the part leaders are most likely to hand off without noticing. Topics discussed:
Trust equation under AI
Expertise is changing
Track record is harder to read
Judgment carries the most weight
Decision fog
Speed isn’t everything
Ford’s rehiring of experienced engineers
Business fundamentals matter more
Alignment and accountability are cross-functional
Real value is “so what”
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This is the takeaway episode with Dora Boussias, executive advisor and former data executive. We unpack the question "How do leaders preserve executive trust and credibility?" Her answer: protect your judgement. In this episode, Dora presents the trust equation that most leaders grew up with: expertise + track record + judgment. Don't miss it!
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Agents will build data infrastructure at every layer and become its primary consumers. Tristan Handy, President and Co-founder of Fivetran / dbt Labs joins Juan and Tim where we unpack the big shift that is happening: teams need to move from working in the process (picking up individual tickets) to working on the process (building the factories that handle those tickets). In this episode, we break down what this means for data roles, why open components matter more than vendor stacks, and the socio-political problem nobody's talking about, deciding what counts as truth so your agents (and humans) ask the right questions in the first place. Topics discussed:
Agents as builders and consumers
Data roles are collapsing
From working IN to working ON
Loop building as the competitive advantage
Business understanding beats technical specs
From humans in the loop to humans out of the loop
Open components over vendor stacks
Defining truth is a political problem
Data observability becomes business observability
Real-time becomes necessary
Humans delegate outcomes, set goals, sit in control
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