Enterprise Wide Search

In this episode, Ole Olesen-Bagneux sits down with Vanessa Andersson, Senior Data Engineer and founder of Data & AI Stockholm, to explore what the rapid evolution of AI looks like from the perspective of a practitioner working directly with it.

With a background that spans marketing, management, programming, and data engineering, Vanessa brings a grounded perspective to an industry moving at extraordinary speed. As organizations move beyond their first AI experiments, she sees the conversation shifting from what AI can do to harder questions around reliability, governance, responsible use, and how the roles of data professionals themselves are changing.

Together, they explore:
Why organizations are moving from asking “can we build it?” to “can we make it useful and reliable?”
What the rise of AI agents means for data engineers and other data professionals
Why predictions about AI replacing entire professions may overlook how work actually evolves
How organizations can embrace AI without ignoring bias, privacy, security, and responsible use
Why practitioners need spaces to discuss real-world challenges without the sales pitch

They also discuss Stockholm's growing data and AI ecosystem, why sharing challenges can be as valuable as showcasing successes, and what it takes to protect a practitioner community's independence as it grows.

🎧 Tune in for a conversation on how data work is changing in practice and the value of communities where practitioners can learn from one another.

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 Ben Mathew, General Manager of Business Intelligence at Actian and former GM of Jaspersoft, to explore how business intelligence is evolving in the age of AI.

With more than 20 years in enterprise software and two decades of experience in the BI space, Ben looks back at how business intelligence evolved from static reporting to analytics. And why, despite decades of investment, the promise of truly self-service BI has remained largely unfulfilled.

Together, they explore:
How BI evolved from hindsight to insight, and increasingly, foresight

Why self-service BI has struggled to deliver on its original promise

How natural language and AI could finally make analytics accessible to a much broader audience

Why probabilistic AI cannot replace the deterministic outputs enterprises still need

How governance, trust, and auditability will shape the next generation of business intelligence

Ben also shares the story of Jaspersoft, from its open-source beginnings 25 years ago to its recent acquisition by Actian, and explains why he believes AI won't make BI smaller: it will make it more foundational.

🎧 Tune in for a conversation on the evolution of business intelligence, the impact of AI on analytics, and why the future may be less about replacing BI than finally delivering on its original promise.

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 Mariana Hebborn, Global Head of HR AI & Data Governance at Merck, to explore what it really takes to move AI from experimentation into the enterprise.

With a background in computational linguistics and semantics, Mariana has spent her career at the intersection of data, technology, and business. Today, she is applying that experience to one of the most sensitive and consequential areas of enterprise data: HR, where, as she puts it, the data describes someone.

Together, they explore:
Why enterprise AI requires more than access to powerful models

The difference between everyday AI and enterprise AI (and why adoption looks very different for each)

How Merck is using AI agents to simplify complex HR processes

Why data quality, governance, privacy, and security need to evolve alongside AI

How organizations can give employees room to experiment with AI without losing control

Why transparency can help organizations move faster with sensitive data

They also discuss why AI is becoming a business capability rather than simply a technology, the challenge of governing a growing ecosystem of employee-created agents, and why understanding the value of enterprise data remains as important as ever.

🎧 Tune in for a practitioner-led conversation on moving beyond AI experimentation and building enterprise AI that people can actually use, trust, and govern.

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Ole Olesen-Bagneux sits down with Lulit Tesfaye, Joseph Hilger, and Zach Wahl from Enterprise Knowledge to explore why the future of enterprise AI depends on connecting knowledge, data, and semantics.

Drawing from their new book, Bridging Knowledge, Data, and AI, they explain why organizations can no longer treat structured data, unstructured content, taxonomies, ontologies, glossaries, and knowledge graphs as separate disciplines. As AI pushes enterprises to make better use of context, meaning, and organizational knowledge, the need for a true semantic layer becomes more urgent than ever.

Together, they explore:
Why Enterprise Knowledge was created, and how its mission has evolved

What a semantic layer really is, beyond vendor diagrams and buzzwords

Why knowledge assets include much more than structured data

How taxonomies, ontologies, business glossaries, and knowledge graphs work together

Why AI is forcing data, IT, and knowledge teams to collaborate in new ways

The risks of competing semantic layer initiatives inside the same organization

🎧 Tune in for a conversation on semantics, enterprise knowledge, AI readiness, and why the next phase of data work depends on bridging the silos that organizations have built for decades.

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 Xavier Gumara Rigol, author of Data as a Product Driver and Head of Data at Manychat, to explore what happens when data moves beyond reporting and becomes part of how product teams make decisions.

Drawing on more than 15 years in data, engineering, and platform leadership, Xavier explains why treating data as a product is only part of the story. The bigger challenge is helping product organizations become truly data-informed, from idea validation and experimentation to ownership, prioritization, and lifecycle management.

Together, they explore:
Why Data as a Product Driver is not just another book about data products

What data teams can learn from product management, and what product teams need to understand about data

Why centralized data teams often become bottlenecks as organizations grow

How embedding analysts and data scientists into product teams changes decision-making

What good looks like when product teams use data for discovery, experimentation, and outcomes

Why product mindset matters when deciding what data products to build, maintain, or retire

🎧 Tune in for a conversation on data, product thinking, and how organizations can move from building data assets to using data as a driver of better product decisions.

Hosted on Ausha. See ausha.co/privacy-policy for more information.