Paul is VP of Data at Booksy, and he thinks the industry’s obsession with data mesh went a little too far. His argument is that when teams cut corners on governance, you end up with different versions of revenue and churn, and AI agents can’t make sense of any of it. He explains how Booksy balances the two extremes with a central data team embedded in the business, how that set them up to build a customer support agent that has doubled its success rate, and what he looks for when hiring data professionals now that AI can write the code.
We cover:
Why Paul thinks the industry’s obsession with data mesh went too far, and what teams trade away when they chase speed
Why inconsistent metrics like revenue and churn leave AI agents struggling to give reliable answers
How Booksy runs a central data team embedded in the business, with data owning the who and the how and the business owning the why and the what
How Booksy built a customer support agent that doubled its success rate, and the knowledge base and guardrails behind it
What Paul looks for when hiring data professionals now that AI can write the code, and why problem solving matters more than the code itself
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