Logical First, Physical Second: A Pragmatic Path to Trusted Data

Fast pipelines and modern data stacks aren’t enough. Without shared business meaning, data platforms break down as they scale. In this episode of The Data Engineering Podcast, host Tobias Macey speaks with Jamie Knowles (Product Director, ER/Studio) about why successful data engineering starts with a logical foundation, not just physical implementation. They explore how logical data models create alignment across business and technical teams, helping organizations reduce rework, improve consistency, and build trusted data for analytics and AI. What you’ll learn: 🔹 Why AI increases the need for clear, agreed data definitions 🔹 Why logical data models are essential for scalable data platforms 🔹 How semantic drift creates rework and breaks trust in data 🔹 How shared business meaning aligns architects, engineers, and analysts 🔹 Practical ways to introduce data architecture without slowing delivery 💡 Key takeaway Start with shared meaning. Logical data modeling creates the foundation that enables scalable pipelines, consistent analytics, and trustworthy AI. 🔗 Listen to the full episode 📌 Logical First, Physical Second: A Pragmatic Path to Trusted Data https://erstudio.com/resource-center/... 📚 Explore more ER/Studio resources 📌 From Models to Momentum: Uniting Architects and Engineers https://erstudio.com/resource-center/... 📌 ER/Studio Enterprise: The Platform Built for Real Data Teams https://erstudio.com/resource-center/ 📌 From Business Requirements to Data Products https://erstudio.com/resource-center/ 📌 Avoiding Common Medallion Design Traps https://erstudio.com/resource-center/ #dataengineering #dataarchitecture #datamodeling #datagovernance #analytics #aidata #dataquality #metadata #erstudio #datastrategy #moderndatastack