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.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Jovita Tam, a business-focused data and AI advisor and attorney, about the hard truths of AI, governance, and organizational change. Known for connecting legal, technical, and cultural perspectives, Jovita shares what it really takes to align strategy with execution — and why AI success has more to do with mindset than with models. Together, they explore: Why “data culture” is just culture — and why it’s so difficult to get right. The myth of top-down transformation, and what actually drives change. What makes AI governance sustainable, and what makes it irrelevant. How legal, data, and CX leaders can partner to drive lasting change. A practical approach to balancing strategy, trust, and adaptability. 🎧 Tune in for a thoughtful conversation that goes beyond checklists and makes the case for awareness, alignment, and giving organizations the space to evolve. Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Ole Olesen-Bagneux is joined by Gaëlle Seret, product leader, writer, and enabler of data transformations at scale. Known for her clear-eyed writing on Substack, Gaëlle brings a mix of operational pragmatism and strategic depth to the question of what it really takes to build data-driven organizations. Drawing from hands-on experience across finance, energy, and retail, Gaëlle reflects on the invisible labor behind good data work — and why transformation always starts with the people who love what they do. Together, they explore: Her journey from finance analyst to data product evangelist. What makes domain-driven design useful for data teams. Why most transformations fail without cross-functional “allies.” How to apply product thinking in overlooked domains like IT. Writing, community, and the value of making complex ideas accessible. 🎧 Tune in for a conversation about data culture, organizational change, and why understanding the “why” behind your work might be more important than the stack you’re using. 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 Robert, Innovation and Data Advisory Director at Apgar, to unpack one of the most intriguing questions circulating in the data community: what happens to the data marketplace now that MCP (Model Context Protocol) is here? Drawing on nearly two decades across master data, governance, and AI programs, Frédéric shares a practitioner’s view on how organizations actually access, curate, and operationalize data — and why the rise of AI products is reshaping the entire value chain. Together, they explore: What a data marketplace really is (and why so many companies misunderstand it). Why AI and analytical products are becoming as important as data products. How MCP could automate large parts of data access, and where humans will still matter. Why metadata remains the foundation for any scalable AI initiative. The innovations he’s most excited about, from predictive data failure to new data‑driven business models. 🎧 Tune in for a grounded conversation about the future of data access — beyond hype, fear, and the marketplace as we knew it. 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 Julia Diez, Localization Software Engineer at McAfee and the author behind one of the most quietly brilliant Substacks in tech today. Trained as a linguist and now working at the intersection of metadata, multilingual ontologies, and LLMs, Julia brings a rare perspective to the world of data and AI—one shaped by bookstores, translation memories, and a deep appreciation for cultural nuance. Together, they explore: Why translating “Welcome, User” might not be as simple as it sounds. The hidden complexity of plurals, pronouns, and color in globalized software. How language, etymology, and pragmatics shape our technologies (and sometimes break them). What it means to build a multilingual knowledge graph, and why it matters in the age of AI. Why localization isn’t just post-processing, but a design principle for global systems. 🎧 Tune in for a conversation that moves from Copenhagen to Cork, metadata to meaning—reminding us that culture, language, and context are just as foundational as code. 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 Lukas Plaehn, Head of Data Office at Bank of Valletta, to explore what makes data so hard to manage in financial institutions—and why that challenge goes deeper than technology. From Malta’s sun-soaked shores to the global stage of regulatory compliance, Lukas brings a thoughtful voice to some of the industry's most complex issues. Together, they explore: Why trust—not technology—is the core tension in financial data management. The layered legacy of banks, and how smart people can slow transformation. What systemic banks are, and why regulators are making data quality the law. How ontologies, data contracts, and shared models are reshaping the industry. Why BI is blurring with operational tech, and how this could finally close the loop from reporting to prediction. 🎧 Tune in for an insightful look into what it takes to modernize data in one of the world’s most risk-averse industries. 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 Jean-Georges Perrin—better known as JGP—to discuss the future of data products, the limitations of BI, and the importance of federated thinking in an era of real-time everything. From his early days self-publishing a 300-page C++ guide with a classmate (armed with nothing but Word and a photocopier) to writing Implementing Data Mesh with O’Reilly, JGP reflects on why storytelling, open standards, and metadata will shape the next era of enterprise data. Together, they explore: Why dashboards aren’t data products—and what that reveals about the lifecycle of data. The origin story of Bitol and its zero-code approach to defining open standards. What’s next for Data Mesh after the GenAI hype wave. Why BI might die (and why that’s not a bad thing). How federated governance beats both centralized and decentralized extremes. 🎧 Tune in for an honest, funny conversation about the future of data platforms—and why sometimes, it’s more helpful to say what something isn’t. Hosted on Ausha. See ausha.co/privacy-policy for more information.