• 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.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Together, they explore:

• Dylan’s work helping companies turn vague goals into actionable data strategies.

• The real reasons enterprise-wide search still falls short, and how AI can help fix it.

• Why data catalogs disappointed, and what it takes to build real access and insight.

• The toughest part of connecting data and business strategy.

🎧 Tune in for a conversation about the messy reality of data transformation—and why the best strategies are the ones that actually get used.

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Together, they explore:

• Sofia’s work leading a linked data transformation at the European Commission.

• What librarians have always known—and technologists often forget—about user context and information architecture..

• Why data migration is an opportunity to improve data quality, not just preserve it.

• How FRBR (and now LRM) reshaped library metadata as a graph-based model.

• Why data needs continuous curation, and how to build a culture that supports it.

  
  

🎧 Tune in for a conversation that bridges the gap between tradition and innovation, reminding us that behind every intelligent machine is a century of human effort to describe the world clearly.

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Together, they explore:

• Tim’s journey from bioinformatics PhD to startup veteran and strategic advisor.

• Why he believes your first hire shouldn’t be a data scientist—or even a data engineer.

• The rise of the analytics engineer and why Excel skills still matter more than most admit.

• The myth of the all-knowing centralized data team—and how to rethink service, ownership, and collaboration in real-world enterprises.

🎧 Tune in for an unfiltered, field-tested look at what it really takes to move organizations from spreadsheet chaos to data-driven confidence.

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Together, they explore:

• Nagim’s global journey—from Kabul to Hamburg—and how it shaped his unique worldview on data and systems.

• Why his work focuses on enablement over abstraction, and how data teams can act as support systems rather than consultants.

• What a mulberry leaf, silk, and solitude can teach us about the slow, deliberate nature of data work—and why rushing outcomes often misses the point.

• Why terms like data product and data catalog may need to evolve to reflect more human-centered, collaborative paradigms.

• The hidden dynamics of power in federated data strategies—and why acknowledging them is key to sustainable governance.

🎧 Tune in for a reflective conversation that challenges the way we think about data, control, and collaboration—and why the next big leap in data might just begin with people.

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Together, they explore:

• Andrew’s journey from software engineering to data leadership.

• How unreliable data led him to pioneer the concept of data contracts.

• The surprising connections—and distinctions—between data contracts and data mesh.

• Why data contracts are gaining momentum as the practical backbone of the “shift-left” movement.

• Real-world lessons from organizations implementing contracts to improve data quality, governance, and developer experience.

🎧 Tune in to understand why data contracts aren’t just a trend—they’re becoming the foundation for reliable, federated data management.

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Together, they explore:

• Emma’s early experiences with technology and her path into data engineering.

• The pervasive role of Actian’s technology in everyday life, from morning routines to global transportation systems.

• The evolution of data management: from conserving storage space to enabling AI-driven decision-making.

• Emma’s favorite cities and the cultural insights she’s gained through her travels.

  
  

🎧 Join us as we delve into Emma's experiences and perspectives on the podcast, setting the stage for future explorations of enterprise data.

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As you will hear, Anna has deep technical understanding and - just as important - organizational experience to testify that the Meta Grid is already out there. In this conversation, we discuss the wealth of metadata repositories, that each at least in principle leverage different, distinct capabilities - albeit they contain the same metadata.

However, Anna precisely addresses that this is not always the case, that there is an overlap of capabilities delivered by metadata repositories, and points to the paradox that vendors try to position themselves as unique, although they actually deliver redundant capabilities.

Nevertheless, we expand the topic to include not only vendors, but also consultancies and internal employees - this reality that I call the Good, the Bad, and the Ugly (all three parties can play all three roles).

Also, at least for the next couple of months, I have decided to publish episodes on my podcast a little less frequently, but deeper and slightly longer conversations. I simply have to finalize my book, Fundamentals of Metadata Management (O'Reilly, 2025).

In conclusion, I am very happy I got to have this conversation. I think that tech- and consultancy companies should listen to this episode. For each industrial company I sare to claim: The Meta Grid is already there.

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I also spoke about data influencing with Solomon. I really don't like being called a data influencer, but Solomon totally embraces it, saying that knowledgeable people should fight for influence. I like that! Finally, we talked about the tech scene in New York, that is interesting in the sense that this is the place where the biggest tech customers are.

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Eduardo da Silva has tried exactly this inside bol.com, with the input and feedback from co-author of Team Topologies, Manuel Pais. I interviewed Eduardo and Manuel and this conversation was eye-opening and very stimulating. I was simply surprised that something so smooth and powerful can happen, and you can hear my excitement.

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The preface of my book Fundamentals of Metadata Management

Scott Taylor asked me to send my data story on the Meta Grid, so he could comment on it, and - I’m thankful Scott! Here it is: The following is the current preface of my book Fundamentals of Metadata Management, to be published with O’Reilly in 2025. It contains two paragraphs, About this book and Why I wrote this book. Everyone, including YOU, can read and comment on my Substack version of The Meta Grid Notes. I read all comments!

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Martin is the CEO of Alvin, and if you want to hear what the sponsor behind this podcast is all about, here you go - I partner with Alvin, because I believe in Alvin's ideas!

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