In this episode of Knowledge Distillation, Katrin Ribant talks with Tim Wilson – one of the most respected voices in digital analytics, widely known as the Quintessential Analyst (a title he’ll deny but absolutely deserves) and equally famous for climbing on a soapbox and delivering the kind of rants that somehow leave you smarter when he’s done. Tim has been working with digital data full-time since 2001, holding senior analytics roles at Search Discovery, Analytics Demystified, and across multiple agencies and Fortune 500 consultancies, and is widely known for his no-nonsense, clarity-first approach to getting business value out of data – earning him a reputation as one of the industry’s most beloved (and self-admittedly cranky) analytical thinkers.

Continuing the agentic e-commerce series, this episode goes upstream: before you instrument, before you build the data layer, how do you decide what to measure? Tim draws on over two decades of experience to argue that the agentic commerce shift – comparable in scale to mobile, Amazon, and GA4 combined – demands business clarity first, not more data collection. Together they explore why organizations keep repeating the same measurement mistakes across every technology disruption, how to use hypothesis testing and primary research to cut through the hype, and why the analyst’s real superpower is resisting the urge to solution before the business question is clear. The conversation also dives into the evolving skills analysts need now, from understanding LLMs to prompt and context engineering as the new SQL.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

Josh Silverbauer is Head of Analytics and CRO and Partner at From the Future, where he architects data layers and analytics implementations for brands navigating the messiest parts of measurement. He’s also the host of The Third Party Show—a comedy-musical talk show for digital marketing—and the creator of a rock opera about an alien named Cookie who loses his universe when Universal Analytics sunsets. In this episode, we dig into what happens when a purchase occurs without a browser session. OpenAI’s Instant Checkout means transactions can complete entirely server-side, with no JavaScript firing, no user journey, and no attribution—just an order appearing in your backend. Josh walks through where “ChatGPT / not set” shows up in your unassigned traffic, why server-side webhooks are currently the only way to count agent-driven purchases, and what the GA4 migration taught us about not dragging old assumptions into new architectures. We also explore why Microsoft Clarity is ahead of Google on AI traffic dashboards, and why building the agentic data layer feels less like a migration and more like a construction project with no blueprints.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

“We have so much trash in terms of websites that AI is now supposed to ingest and give us something meaningful. It’s not going to work.” Sani Manić has spent 15+ years optimizing websites for speed, accessibility, and technical health. A contributor to Search Engine Journal and WordPress Core, he’s now turned that expertise toward a new challenge: making the web work for AI agents. In this episode, we explore the emerging data layer beneath the agentic web—where users never see your brand’s colors or copy because an AI assistant completed the purchase for them. Sani and Katrin discuss why clean semantic HTML is the new competitive advantage, why you should never trust a tool built by someone who couldn’t do the job without AI, and the real security exposure of frontier models. Sani hosts the NoHacks Podcast, now subtitled “Optimizing the Web for AI Agents” and is building an auditing tool that shows how different LLMs and agents see, navigate, and transact on your website. For analysts and e-commerce teams, this episode makes a compelling case that GA4’s event-driven architecture and the BigQuery skills analysts built during migration may be their best preparation for tracking the agentic web, even if nobody planned it that way.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

“My mom called me about this cool thing called an LLM. I was like, I’ve been working on this for 10 years.” Yaniv Makover, CEO of Anyword, joins Avigad Oron, Head of Technology for Ask-Y, Two AI engineers with combined 40 years of experience dissect their ChatGPT moment and what it means for the future of work. We discuss the architecture decisions behind enterprise AI—from RAG and ranking to security nightmares where “your data goes into a super complex distributed system with multiple models.” They debate whether transformers will reign forever and agree on one thing: AI is pushing everyone toward senior-level integrative skills while routine tasks disappear.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

Jim Gianoglio has worn every hat in marketing—from photographer to creative director to SEO specialist to data scientist. Now he runs Cauzal Analytics, a boutique consultancy focused on marketing mix modeling and incrementality testing, and co-hosts MMM Hub, a community and newsletter for measurement practitioners. He also co-hosts the Measure Up podcast and built the session board app that digitizes Measure Camp schedules across the country.

In this episode, we explore the dual life of the modern analyst: using AI to power analytics workflows while also building AI-powered tools. Jim shares the “zero to 80% magic moment” of vibe coding with Replit, the frustration of context poisoning when LLMs get stuck in loops, and why he keeps re-testing tasks that failed months ago—because the models are always getting better. We dig into how analysts can future-proof their careers by listing every task they do, prioritizing by value, and systematically automating from the bottom up. Jim also reflects on how context engineering applies to marketing mix modeling and why understanding your business remains the skill AI can’t replace.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

What happens when AI meets bad data? Patrick Thompson has been building and using data tools for over 15 years. He started in growth at Atlassian, then co-founded Iteratively-a data quality platform acquired by Amplitude in 2021-where he became Director of Product. Patrick knows what it takes to build data layers, event pipelines, semantic layers, and quality monitoring systems. He knows what breaks when instrumentation goes wrong. In this episode, we explore how AI is lowering the floor for analysis while raising the bar for what makes analysts valuable. Patrick shares why natural language queries are changing user expectations overnight, why memory and context remain the hardest problems for AI to solve, and the difference between using AI as a tool versus a crutch. We dig into why gut-checking analysis is becoming a critical skill, and what Patrick tells people who ask if their kids should still study software engineering.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

A very special episode of Knowledge Distillation with Michael Helbling, co-founder and co-host of the Analytics Power Hour podcast and founder of Stacked Analytics. Michael has spent two decades building analytics programs as both consultant and practitioner, and he’s not one to sugarcoat anything. Together we break down why dropping an LLM on your data warehouse won’t magically produce insights, why semantic ambiguity kills most AI analytics initiatives, and what the streetlight effect means for AI’s ability to conduct real analysis. We get into the path forward – multi-agent architectures, semantic layers, knowledge graphs – while staying clear-eyed about the heavy lift required. Essential listening for anyone caught between CEO mandates to “AI everything” and the reality that this stuff doesn’t work like the LinkedIn posts suggest.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

In this episode of Knowledge Distillation, Katrin Ribant speaks with Shomik Ghosh, a Silicon Valley VC who has lived through multiple tech hype cycles from both the investing and operating sides. Together they break down how hype forms, peaks, crashes, and ultimately reshapes roles – from DevOps to data science and now the rise of the AI analyst. Shomik explains why this generative AI wave is an enablement shift, spreading faster than any previous platform shift, and why embracing a learning mindset is now the most valuable career differentiator. They discuss which analyst skills will survive every hype cycle – deep understanding of data, critical thinking, and the ability to evaluate AI-generated logic. The episode also explores the future of work with AI agents and what it means to orchestrate virtual teams of autonomous tools. A must-listen for anyone navigating the new analytics landscape and trying to choose what to learn next.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

In this episode of Knowledge Distillation, Katrin Ribant talks with Mike Driscoll – co-founder and CEO of Rill Data and co-inventor of Apache Druid. Mike reflects on the early days of big data, the birth of real-time analytics, and what it meant to be an analyst in 2010. Together, they explore how today’s challenges have shifted from scale to maintaining analytical context across complex workflows. Mike discusses the rise of more technical, code-native analysts and how AI agents are transforming the way data teams operate. He shares why data engineering remains a superpower and how analysts can upscale to thrive as orchestrators of AI-driven processes. The episode offers a forward-looking view on the “AI analyst” skill set and the future of self-serve analytics.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai

In this kickoff episode of Knowledge Distillation, host Katrin Ribant sits down with Martin Kihn, AI strategist at Salesforce and author of Agent Force. Together they explore how the role of the data analyst is shifting from manual data wrangling to orchestrating AI-powered workflows. Martin breaks down why understanding LLMs, prompt engineering, and AI-generated code is becoming essential for modern analysts. They discuss the misconceptions around AI replacing analysts and explain why critical thinking and business context matter more than ever. The episode highlights how AI accelerates hypothesis testing, improves visualization, and expands what analysts can achieve. A great introduction to the emerging skill set of the AI analyst.

All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast

Learn more about ASK-Y: www.ask-y.ai