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.

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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.

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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! 

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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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This is the takeaway episode with Tristan Handy, President and Co-founder of Fivetran / dbt Labs where he joins Juan and Tim to discuss how agents will build data infrastructure at every layer and become its primary consumers. We unpack the big shift that is happening: teams need to move from working in the process to working on the process, 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. 

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AI doesn't create your problems. It inherits the business choices you never made and the results nobody owns. David Krauza, VP of Data Strategy and Governance at Comcast, joins Juan and Tim to make the case that most "data problems" aren't data problems at all. They are accountability problems, definition problems, assumption problems, and what-are-we-even-measuring problems. The fix isn't better tooling. Data teams are so focused on delivery that they never stop to ask what the business is actually trying to do. It's going to the C-suite, asking about the top three goals, and following the definitions until the cracks show up. Topics discussed:

• AI inherits your unmade decisions
• The output trap
• Definitions are the real foundation
• Talk to the business
• Data as a cost center vs. a value driver
• Single source of data, multiple versions of the truth
• Think like a product owner
• The revenge of the liberal arts majors

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