The Return of Conceptual Data Modeling w/ Sami Hero (CEO of Ellie.ai)

In this episode, I sit down with Sami Hero, CEO of Ellie.ai, to talk through the evolution of data modeling and why foundational conceptual modeling is more critical now than ever. With the explosion of AI agents, LLMs, and semantic layers, many teams assume they can bypass data modeling and just let an AI infer meaning directly from raw tables. We dig into why that mindset leads to bad decisions, how companies end up with 15,000 Snowflake tables and thousands of unmaintained dashboards, and why defining core business concepts is the real prerequisite for reliable AI. We also discuss the resurgence of ontologies, lessons from old-school methodologies like MERISE, and how to balance speed with sound architecture. Timestamps 00:00 - Introduction and Catching Up 01:05 - Sami Hero’s Background in Data and Databases 02:01 - What Has Changed (and Stayed the Same) in Data Modeling 04:41 - The Challenge of Defining Business Concepts 06:37 - Why Relying Purely on LLMs for Insights Fails 10:43 - Semantic Layers, Ontologies, and Anthropic’s Stance 14:49 - What Is Conceptual Data Modeling? 17:09 - 15,000 Snowflake Tables and Dashboard Bloat 20:06 - Shifting Left vs. Shifting Right: Fixing Logic in BI Tools 22:23 - The Data Town Plan: Conceptual, Logical, and Physical Layers 26:03 - Using AI for Modeling (and Why You Still Need a Human) 27:56 - Moving Beyond Siloed Data Architecture to Nimble Modeling 31:26 - The Illusion of Speed and Autonomous Decision-Making 37:01 - What the Industry Gets Wrong About Data Modeling Tools 40:34 - Proving the ROI of Proper Data Modeling 42:23 - The MERISE Methodology and Desktop Modeling Tool Legacy 45:26 - The Convergence of Semantic Modeling and AI Agents 48:48 - Where to Connect with Sami Hero