In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Carsten Bange, founder and CEO of BARC, one of Europe’s leading analyst firms focused on data, analytics, and AI. With more than 25 years advising enterprises on data strategy, analytics platforms, and organizational transformation, Carsten shares an independent analyst perspective on why so many data and AI initiatives still struggle to deliver business value, despite massive investments in technology. Together, they explore: Why data culture has become one of the most important success factors in enterprise AI The gap between technology investments and actual business adoption Why curiosity, leadership, and organizational change matter more than ever How enterprises are moving from AI hype toward rebuilding their data foundations What BARC’s latest research reveals about the relationship between data products and AI success They also discuss the growing realization that the biggest barriers to AI transformation are often not technical, but human. 🎧 Tune in for a conversation on enterprise AI, data culture, analyst independence, and why successful transformation requires much more than new technology. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Kierra Dotson, Associate Director of Data Science & AI Strategy at Further, keynote speaker, and thought leader known for helping enterprise leaders build AI strategies that actually deliver value. Working at the intersection of data, AI, and operations, Kierra advises Fortune 500 executives on how to move beyond experimentation—designing AI initiatives that are scalable, trustworthy, and worth talking about. Together, they discuss: How the rise of AI is reshaping the data community, and why fundamentals still matter more than ever The difference between analytics-ready data and AI-ready data Why data governance and AI governance are more connected than most think The growing importance of architecture, trade-offs, and “strategic engineers” in an AI-driven world How shifts in infrastructure—from cloud to edge to on-prem—are being driven by cost, performance, and data sovereignty Kierra also shares practical advice for the next generation of data and AI professionals, from building meaningful projects to developing the business and architectural thinking that sets candidates apart. 🎧 Tune in for a conversation on AI strategy, enterprise reality, and why mastering the fundamentals is still the best way to stay ahead. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Ashleigh Faith, Director of AI and Semantic Innovation at EBSCO and creator of the IsA DataThing YouTube channel, to explore why knowledge graphs and ontologies are becoming essential in the age of AI. From her early days as a taxonomist to her role as a leading voice in semantic technologies, Ashleigh explains why structuring meaning—not just data—is what makes AI outputs trustworthy and usable. Together, they discuss: Why knowledge graphs are critical to making AI outputs more reliable and verifiable The most common mistakes teams make when building ontologies, and how to avoid them Why starting with use cases (not tools) is key to successful semantic models How AI can assist, but not replace, human understanding in ontology design The future of graph architectures, from large centralized graphs to domain-specific, contextual models 🎧 Tune in for a conversation on semantics, knowledge graphs, and why making AI smarter starts with making data more meaningful. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Juha Korpela, independent consultant and co-founder of Helsinki Data Week, to explore why semantics is rapidly becoming a core pillar of modern data work. Drawing on his experience in data modeling, metadata management, and enterprise architecture, Juha shares how topics once considered “soft”—like meaning, context, and conceptual models—are now becoming critical infrastructure in the age of AI. Together, they discuss: Why semantics is shifting from a human concern to a machine requirement How AI is accelerating the need for structured, governed metadata at scale The resurgence of data modeling and conceptual thinking in modern architectures The challenge of managing semantics beyond individual solutions and at the enterprise level How community-driven initiatives like the Helsinki Data Week are shaping the future of data practice 🎧 Tune in for a conversation on metadata, meaning, and why semantic data work is becoming essential to making AI actually work in the enterprise. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Bjørn Broum, Director of Business Development for Technology, Architecture, and Data at the Norwegian Public Roads Administration (NPRA), to explore how data strategy works inside one of Norway’s largest public infrastructure organizations. With more than 30 years of experience in the data space—and after helping launch the organization's data-driven transformation as CDO—Bjørn now works at the intersection of technology, architecture, and strategy to help modernize how data is used across the public authority, which operates with a powerful mission: Vision Zero, eliminating road fatalities by 2050. Together, they discuss: What it takes to build a data-driven culture in a large public authority How sensor data, road geometry, and machine learning identify dangerous locations Why reducing fatalities requires integrating analytics with engineering expertise The role of real-time data in future traffic systems How AI may reshape data engineering and move intelligence closer to operational systems 🎧 Tune in for a conversation about data, infrastructure, and how analytics can shape safer societies. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Amra Dorjbayar, former co-founder and CEO of Wobby (recently acquired by Actian), to unpack what Agentic BI really means and why the next wave of analytics won’t look like dashboards, reports, or even chatbots. Amra shares the moment in late 2022 when he realized LLMs could change how people work with data, why he left data journalism to build Wobby, and what it takes for AI agents to generate insights you can actually trust. Together, they explore: Why “agentic BI” needs an operating system, not just a chat interface How semantic layers and knowledge graphs make AI analysis reliable Why AI-native teams move differently, and how process can kill speed The shift from “ask questions” to proactive insights that surface themselves The emerging role she calls “guardians of meaning” for enterprise data 🎧 Tune in for a conversation on the future of BI, meta-functional teams, and what it takes to connect data foundations with agentic outcomes. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Malcolm Hawker, Chief Data Officer at Profisee and author of The Data Hero Playbook, for a conversation about why so many data programs stall, even when teams follow the “right” playbook. Drawing on years of conversations with data leaders (including his time as an industry analyst), Malcolm argues that what sets successful CDOs apart is rarely technology. It’s a mindset. Along the way, he shares his contrarian take on data mesh, why the term is fading, and what still matters from the movement. Together, they explore: Why “best practices” often fail in the real world Growth mindset vs. fixed mindset, and how it shows up in data teams The dangerous comfort of “garbage in, garbage out” thinking Malcolm’s “data product spectrum” and why one definition won’t cut it Why enterprise context still demands some centralization (even in domain-driven models) 🎧 Tune in for a spicy episode on leadership, mindset, and how to create impact in data without hiding behind frameworks Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Ruud Kuil, longtime data quality expert and now co-founder of MAD-Quality, to discuss what really breaks in organizations when data is wrong and why fixing it requires rethinking both tooling and incentives. After 15+ years advising enterprises on data quality, Ruud took the plunge into startup life to build a platform that shows the real cost of bad data. In conversation, he explains why the “AI will fix data” myth persists and what it takes to make data quality everyone’s responsibility. Together, they explore: Why most data quality tools miss the point — and how to flip the model The challenge of selling long-term value in short-term cultures Why AI only works when your foundations do (and what happens when they don’t) How to calculate and communicate the business impact of poor data The case for a new kind of KPI: delayed bonuses tied to durable data practices 🎧 Tune in for a conversation about the economics of data quality, the illusions of automation, and what it really means to build trust in an AI-first world. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Frédéric Verhelst — a leading voice in semantic technologies and agentic AI, and longtime semantic web advocate — for a practical dive into ontologies, knowledge graphs, and why they matter more than ever in an AI-first world. From his early work with TotalEnergies to today’s experiments in integrating AI into digital service platforms, Frédéric brings clarity and historical depth to a topic many still find intimidating. Together, they explore: What an ontology actually is, and why it’s not as scary as it sounds. The difference between a knowledge graph and a graph database, and why that difference matters. Why LLMs need structured knowledge, and why hallucinations were a necessary wake-up call. What Google, Netflix, and AstraZeneca all get right about semantics. Why there’s a global shortage of knowledge graph talent, and how philosophy grads might help. 🎧 Tune in for a conversation that moves from data modeling to AI safety, and shows why the most innovative organizations are investing in semantics — not just models. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Mario Meir-Huber — author of Designing Data Products and longtime data leader — to talk about what happens when you stop treating data like a one-time project and start managing it as a product that evolves. Mario shares the (literal) honeymoon moment that sparked the book, and explains why great data products require more than architecture. They need governance, people, and a design mindset built for continuous change. Together, they explore: Why the GAP (Governance, Architecture, People) is often what makes or breaks a data product The reason data products should be managed as living processes, not one-off projects How poor metadata management slows everything down, and why it’s critical for AI What business leaders really want from data, and why simplicity wins Where data products are headed next, especially in an AI-first world 🎧 Tune in for a conversation about the human, technical, and strategic layers behind successful data products — and why we all need to start thinking more like product managers. Hosted on Ausha. See ausha.co/privacy-policy for more information.