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

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!
See omnystudio.com/listener for privacy information.

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
See omnystudio.com/listener for privacy information.

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.
See omnystudio.com/listener for privacy information.

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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