Your warehouse is clean, your agents are running, everything looks like it’s working, and that’s the trap. This floor is where AI mistakes correlation for causation and scales the mistake at machine speed, eroding revenue behind a green dashboard. We unpack the fix: holdouts, guardrails, and a causal context graph that proves what actually works.More
Tag Archives: customer data platform
225: The Fall of CRM gravity (The Dungeon of martech architecture, part 1)
At some point in the last decade, the CRM became a shared apartment with 19 roommates, each adding their own version of the source of truth. This episode argues for a cleaner path: raw data into the warehouse, transformation with tools like dbt, then activation through reverse ETL so definitions stay centralized and audiences stay portable. More
219: Elizabeth Dobbs: Inside Databricks’ stack with 3 AI agents, 1 lakehouse, and 6 years of data work
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
174: Joshua Kanter: A 4-time CMO on the case against data democratization
Joshua spent the earliest parts of his career buried in SQL, only to watch companies hand out dashboards and call it strategy. Teams skim charts to confirm hunches while ignoring what the data actually says. He believes access means nothing without translation. You need people who can turn vague business prompts into clear, interpretable answers.More
169: Elena Hassan: Visa acquires your startup but nobody warns you about the tech stack aftermath and culture shock
Elena has done what most startup marketers only guess at; made it through multiple acquisitions and now leads global integrated marketing at Visa. In this episode, she breaks down what actually changes when you go from scrappy to enterprise and why most martech tools don’t survive security reviews.More
159: Ana Mourão: Privacy-first data literacy and modernizing legacy martech at a global enterprise with data templates and POCs
Ana created the Experimental Marketer framework to help marketers have a more strategic view (and be more hands-on) with martech and become better allies in using and realizing martech value. She takes us on an adventure through a big martech transformation project with a global enterprise and how she applied that framework to help them scale.More
144: Steven Aldrich: Identify the martech you really need with a bottom-up analysis
Like the aftermath of Ragnarök according to Norse mythology, the martech world is emerging stronger, more focused, and ripe with potential. Rather than being overwhelmed by the chaos, marketers should use this time to rethink how to evaluate technology choices through the lens of business value.More
143: Danny Lambert: A guide to data transformation and building a warehouse-first martech stack
Marketers often feel like they’re battling a dragon when it comes to integrating data. We’re overwhelmed by technical jargon, stuck with outdated methods, and facing roadblocks from data teams. Danny walks us through his journey of cautiously entering the data world and the role dbt can play for marketing teams. Here’s how to get started with a warehouse-first martech stack.More
125: Michele Nieberding: Customer data infrastructure and server-side data processing
Michele takes us on a broad journey across job hopping, learning technical martech products, preparing for the cookie apocalypse and diving deep into the world of server side data processions and tag management. She emphasizes the importance of ethical marketing practices, like enhancing first-party data and focusing on consent management, crucial for building consumer trust.More
95: Battle of the CDPs: Packaged vs. Composable, 10 Experts Weigh In
The debate between packaged and composable CDPs boils down to a trade-off between out-of-the-box functionality and tailored flexibility, with industry opinions divided on what offers greater long-term value. Key factors to consider are company needs and data team size. But if you do decide to explore the composable route, consider tools that focus on seamless integration and adaptability rather than those who claim to replace existing CDPs.More