Liz spent 6 years at Databricks building the data infrastructure before deploying any AI on top of it. She’s shipped 3 production agents (Marge, Tagatha, and Atlas) and she’ll tell you exactly what broke first and why the team kept going anyway. You’ll hear how a marketing lakehouse becomes the foundation that makes every agent actually work, why the agent label debate is a distraction.More
Tag Archives: data warehouse
212: Tobias Konitzer: The Causal AI revolution and the boomerang effect in marketing decision science
Tobi challenged marketing’s fixation on prediction. He has built highly accurate LTV models, but accuracy alone does not move revenue. Marketing is intervention. Correlation shows patterns; causality tells you what happens when you pull a lever. That shift reshapes experimentation, explains why dynamic allocation can outperform static A B tests, and highlights how self learning systems can backfire or get stuck in local maxima.More
201: Scott Brinker: If he reset his career today, where would he focus?
Scott Brinker would build one deep specialty to judge AI’s confident mistakes, grow cross-functional range to bridge marketing and engineering, and lean into technical skills like SQL and APIs to turn ideas into working systems. More
193: David Joosten: The Politics and architecture of martech transformation
David learned that martech transformation begins with proof people can feel. Early in his career, he built immaculate systems that looked impressive but delivered nothing real. Everything changed when a VP asked him to show progress instead of idealistic roadmaps. From that moment, David focused on momentum and quick wins. Those early victories turned into stories that spread across the company and built trust naturally.More
188: Rebecca Corliss: Why lifecycle marketers will thrive in the agentic marketing org
Rebecca imagines a future marketing org built on three layers: leadership fluent in data and AI, a dispatch control tower staffed by engineers and privacy experts, and pods that design customer journeys while agents handle scale. More
185: Jonathan Kazarian: Platforms vs point solutions and the marketing operator’s dilemma
Jonathan framed point solutions as late-night distractions, while Phil argued they solve real constraints platforms can’t touch. Darrell pulled the lens to data models and warehouse-native teams lean on composability for speed and control. We also cover costs, support, documentation, integrations and more!More
183: Kevin White: Building a super IC role to escape management burnout and fixing the broken promise of AI SDRs
Kevin rebuilt his career around the work that fuels him. After years leading teams at Segment, Retool and Common Room, he walked away from politics and board decks to create a “super IC” role focused on experiments, product evangelism, and hands‑on growth. He applies that same mindset to go‑to‑market: strip out the bloat, ditch templated outreach, and use real buyer behavior to build small, personal campaigns.More
180: István Mészáros: Merging web and product analytics on top of the warehouse with a zero-copy architecture
István built a warehouse-native analytics layer that lets teams define metrics once, query them directly, and skip the messy syncs across five tools trying to guess what “active user” means. Instead of fighting over numbers, teams walk through SQL together, clean up logic, and move faster.More
155: Meg Gowell: Typeform’s full stack marketer on growth experiments, brand momentum and warehouse-native stacks
Marketing leadership in 2025 is a wild time. After years of learning martech and technical concepts to become a full stack marketer, you finally land that dream director gig… only to watch your hard-earned tech skills collect dust while you drown in meetings. Megan helps us see the way forward.More
152: Sarah Krasnik Bedell: A data eng turned marketer on embedded marketing analysts and batch vs webhook pipelines
What happens when a data engineer with an obsession for truth-testing crashes into marketing’s ‘best practices’? Sarah’s journey from code to growth unfolds like a trained detective story, where she picks apart marketing myths and rebuilds them with an engineer’s first principles. Her fresh take on centralyzed vs decentralyzed data team structures favors embedding an analyst deeply in marketing and growth teams.More