Catalog & Cocktails: The Honest, No-BS Data Podcast

Victoria Gamerman, a human-centered transformation leader in healthcare and life sciences, discusses why AI governance forces organizations to face the human side of adoption. She unpacks the POC-to-production gap. She highlights people, process, and data as human-centered pillars. She defines what "AI-ready data" means and warns against treating AI as just a tech problem.

Victoria Gamerman, an AI and data leader championing human-centered AI governance. She discusses how governance shifts focus to value, moving proofs of concept into responsible production, and treating AI adoption as a muscle. Conversations cover people, culture, processes, data readiness, and capturing hidden workflows to enable safe, impactful AI.

Diana Wu David, Director of Futures at ServiceNow and futurist on strategic foresight and human-centered AI. She challenges tool-first thinking and spotlights human agency, redesigning workflows, and deciding which decisions belong to machines. Talks include foresight for exponential change, governance and data sovereignty, team experiments, and measuring new value beyond simple adoption metrics.

They dig into why human agency matters for AI adoption and who actually uses new tools inside organizations. Leaders are urged to rethink value and reorganize people and processes around impact. Small-team experiments and top-down strategy are contrasted as change levers. National data sovereignty and market fragmentation are flagged as rising challenges.

A Friday rant about AI's breakneck speed and its implications for creativity, research, and continuous reskilling. A call to center work and decisions over pipelines, reimagining Medallion Architecture with feedback loops and action. A warning about agent swarms, the need for governance, and how library science and taste will shape the future data stack.

Amalia Child, a librarian who adapts library practices to data strategy, and Jenna Jordan, a data librarian focused on reference interview techniques, explore how librarianship maps to data work. They discuss the reference interview, listening and questioning techniques, collection development, aligning data to business needs, and roles that bridge trust and domain knowledge.

Amalia Child, a data librarian who applies library science to data teams. She explains the reference interview as a framework for uncovering real information needs. Short, practical techniques are discussed for building trust, asking the right questions, and translating business requests into precise data requirements.

Pete Williams, experienced data leader who builds decision systems and bridges data to action. He maps the structural gap where data reveals insights but lacks authority. He argues for reorganizing around decisions, using lead indicators, embedding data into decision paths, and documenting decision logic before automating with AI.

Pete Williams, an experienced data leader who focuses on aligning data with business choices. He explores the structural void between horizontal data teams and vertical decision-making. Short takes cover why data orgs prioritize platforms, mapping decisions not use cases, the three E's with a push to exploit, and focusing on the ~20 decisions that move the needle.

Jesús Barrasa, Field CTO for AI at Neo4j and knowledge-graph veteran, joins for a lively rant. They explore why ontologies are resurging, how ontologies serve WHAT (shared formal meaning) and WHY (interoperability, automation), how context graphs capture decision traces, and how agents and graphs accelerate AI and ontology engineering.