Enterprise Wide Search

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 authorityHow sensor data, road geometry, and machine learning identify dangerous locationsWhy reducing fatalities requires integrating analytics with engineering expertiseThe role of real-time data in future traffic systemsHow 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 interfaceHow semantic layers and knowledge graphs make AI analysis reliableWhy AI-native teams move differently, and how process can kill speedThe shift from “ask questions” to proactive insights that surface themselvesThe 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 worldGrowth mindset vs. fixed mindset, and how it shows up in data teamsThe dangerous comfort of “garbage in, garbage out” thinkingMalcolm’s “data product spectrum” and why one definition won’t cut itWhy 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 frameworksHosted 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 modelThe challenge of selling long-term value in short-term culturesWhy AI only works when your foundations do (and what happens when they don’t)How to calculate and communicate the business impact of poor dataThe 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 productThe reason data products should be managed as living processes, not one-off projectsHow poor metadata management slows everything down, and why it’s critical for AIWhat business leaders really want from data, and why simplicity winsWhere 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.