The Stacked Data Podcast

Most agencies that build a proprietary client platform end up maintaining a different version for every client, and a maintenance burden that eventually eats the team. Medialab did the harder thing. Apollo, their proprietary marketing intelligence platform, serves more than 50 clients off one codebase, and every client still gets the bespoke feel of something built just for them. Medialab's Head of Data & Engineering, Adam Cunnington, joins Harry to explain how. He arrived in 2021 to a platform that had happy users but was difficult to scale and over the last five years, has established an upgraded platform built around three principles: transparency, technical excellence and operational scale. A detailed, practical conversation about the architecture decisions that make or break a multi-tenant data platform. Where Medialab handle customisation vs. standard logic, why operating a platform is a bigger job than building one, and why a deliberately lean team turned out to be an advantage rather than a constraint.

Emanuela leads enterprise analytics at Compare the Market, and she's just back from the DBT Summit in Vegas, where the biggest conversation was how to make data foundations bulletproof enough for AI agents to be trusted. She shares what stood out, including whether analytics engineering is becoming context engineering, and what dbt's announcements say about where the company is heading. She also walks through her talk on moving from dashboards to data products, how she gets leadership buy-in for foundation work, and how the analytics engineer role is changing. We cover: * What stood out at the dbt Summit, and why AI makes data foundations more important than ever * Whether analytics engineering is becoming context engineering, and what semantic and context layers mean for the role * Her read on dbt's announcements, including dbt Charts, and where dbt is heading * Why moving from dashboards to data products was about changing how the team thinks and works, not just the tooling * How to get leadership buy-in for foundation work, and how the analytics engineer role is changing in the AI era

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

Roman leads analytics for marketing and media at CarWow, and he's not interested in a team that just ships things. His argument is that most analytics teams get stuck answering questions instead of shaping decisions, and fixing that has almost nothing to do with better tooling. He walks through why he'd rather kill the ticketing system than keep it, why mandating AI adoption across a team never works, and why using AI on ninety five percent of his team's code still hasn't tripled their output. We cover: * Why shipping a dashboard or a pipeline isn't the same as actually driving business impact * Why he'd rather scrap the ticketing system than let his team just take requests * His risk-based framework for when a system needs to be bulletproof, and when moving fast matters more * Why mandating AI adoption across a team doesn't work, and what he did instead * Why using AI on ninety five percent of his team's code still hasn't tripled their output, and what that reveals about where the real bottleneck is

Rich leads the data practice at AND Digital, and he's not buying into the "AI's coming for your job" panic. If anything, he believes everyone else in the business is about to need the exact skills data leaders already have. His argument is that data's moved through three distinct eras: warehousing, then impact and value, and now the agentic era, where the gap between having an insight and acting on it has basically closed. And this, he says, is the time for data leaders to step up and own that space. We cover: * Why Rich thinks data leaders are best placed to own the agentic era * His three-eras model of data, and what's actually different this time round * The paradox of modern data teams: everything's changed, but also nothing has * Why AI's real bottleneck is context, not intelligence, and what that means for semantic data layers * His framework for building a business case a CFO will sign off on, including why joint sponsorship matters