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.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Rachael Greaves, CEO of Castlepoint Systems and pioneer of regulation-as-code AI, to unpack why records and information management are more relevant—and more technical—than ever. Together, they explore: The sometimes overlooked discipline of records management and why it matters in the age of AI. Why data lifecycle decisions are so hard, and why deletion is sometimes the safest option. What ethical AI actually means and how to make it explainable and contestable by design. How Castlepoint uses rules-as-code to govern massive, messy, enterprise-scale datasets. Rachael’s advice to anyone (especially women) entering regtech, cyber, or data governance. 🎧 Tune in for a conversation that connects the dots between compliance, security, metadata, and meaning—and why AI won't fix your governance mess if your rules aren’t clear. 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 Thais Cooke, Senior Healthcare Data Analyst and LinkedIn Learning instructor, to discuss what happens when curiosity meets chaos—and how a career shift from frontline healthcare to data analytics has unlocked new ways of thinking about impact, problem-solving, and AI. Together, they explore: Thais’ transition from burned-out dental professional to SQL-obsessed analyst. How learning to “follow the pain” helped her solve real-world problems with data. Her approach to using AI without outsourcing critical thinking. Why domain expertise is often the missing piece in data analysis and governance. How she created two LinkedIn Learning courses tailored to healthcare professionals. 🎧 Tune in for an inspiring take on learning, career change, and making AI part of your workflow, without losing your voice along the way. 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 Jérémy Ravenel, researcher, advisor, and founder of the open-source NaasAI platform. Known for bridging the gap between theory and execution, Jérémy shares his journey from Excel-heavy finance roles to leading cutting-edge research in AI interoperability and enterprise knowledge graphs. Together, they explore: Why so many GenAI initiatives fail without semantic structure and context. What it means to “clean your room” before deploying AI agents. How ontologies can evolve from niche academic tools to practical building blocks of enterprise-scale AI. The tension between perfection and pragmatism in data work. 🎧 Tune in for a conversation on the hidden structures behind meaningful AI, and why the future might depend on how well we model what we already know. 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 Dr. Shweta Shah, a seasoned data and AI architect with a fresh perspective on how we think about quality in the age of unstructured data and GenAI. Shweta introduces “Content Quality Management” (CQM), a new framework designed to help organizations prepare unstructured content for trustworthy, auditable, AI-powered outcomes. Together, they explore: Why traditional data quality frameworks fall short in GenAI systems. The seven dimensions of content quality. Why unstructured data needs more than structure to be AI-ready. The role of team diversity and ethical design in building AI that works for everyone. How CQM could evolve into a new standard—or even a product—for enterprise AI readiness. 🎧 Tune in for a timely conversation that reframes unstructured data not as a challenge, but as an opportunity to rethink what “quality” really means in modern data work. Hosted on Ausha. See ausha.co/privacy-policy for more information.