Send us Fan Mail

Trust collapses fast when a dashboard misleads or an AI agent learns from messy data. We dig into how data quality became business critical—and how to move from reactive fire drills to proactive systems—through real stories from clinical trials and large platforms where a single broken test could escalate to the C‑suite. With Stan and David, we map the shifts driving this moment: AI adoption, rising reliance on metrics, and the urgent need for shared definitions, lineage, and monitoring that let teams find root causes before customers feel the impact.

We get practical about agents that actually help. Instead of vague hype, we break down a low‑risk architecture for read‑only, metadata‑aware agents that handle repetitive, high‑leverage tasks: writing dbt documentation, proposing data tests, performing lineage‑driven root cause analysis, and auto‑drafting tickets with queries, diffs, and impact notes. We explain why integrated agents beat copy‑paste prompts, how to add guardrails that limit scope and permissions, and what human‑in‑the‑loop review should look like to build real trust without slowing the work.

Expect candid guidance on adoption and observability: two layers of visibility—agent behavior and data quality posture—help teams track costs, measure time to resolution, spot repeating incidents, and choose structural fixes. We also explore buy vs build as platforms begin embedding agent capabilities, and we share a clear starting path for any team: prioritize critical datasets, standardize KPIs and definitions, enable tests, and surface lineage so automation has the context it needs. By the end, you’ll have a blueprint to reduce firefighting, improve stakeholder confidence, and make your AI agents smarter by feeding them cleaner, governed data. If this resonates, follow the show, share with your data team, and leave a review with the one task you’d automate first.

Send us Fan Mail

In our final episode of 2025, we sit down with Tim Van Erum to unpack what really stood out at Tech Expo Amsterdam.

Together we revisit the most misunderstood AI statistic of the year, exploring why the “95% of AI projects fail” headline is misleading and how hype versus reality played out across the conference. Tim shares why failure and experimentation are not setbacks but essential drivers of innovation, and we highlight Reddit’s Scaling Safety strategy as a powerful example of machine learning in action.

This candid conversation closes out the year with lessons on what AI truly delivered in 2025.

Send us Fan Mail

We go inside Mediahuis to see how a small GenAI team is transforming newsroom workflows without losing editorial judgment. From RAG search to headline suggestions and text‑to‑video assists, this episode shares what works, what doesn’t, and how adoption spreads across brands.

You’ll hear about:

• Ten priority use cases shipped across the group
• Headline and summary suggestions that boost clarity and speed
• RAG‑powered search turning archives into instant context
• Text‑to‑video tools that free up local video teams
• The hurdles of adoption, quality, and scaling prototypes into production

Their playbook blends engineering discipline with editorial empathy: use rules where you can, prompt carefully when you must, and always keep journalists in the loop. We also cover policies, guardrails, AI literacy, and how to survive model churn with reusable templates and grounded tests.

The result: a practical path to AI in media — protecting judgment, raising quality, and scaling tools without losing each brand’s voice.

🎧 If this sparks ideas for your newsroom or product team, follow the show, share with a colleague, and leave a quick review with your favorite takeaway.

Send us Fan Mail

In this episode of Data Topics, Ben speaks with Kim Smets, VP Data & AI at Telenet, about his journey from early machine learning work to leading enterprise-wide AI transformation at Telenet. Kim shares how he built a central data & AI team, shifted from fragmented reporting to product thinking, and embedded governance that actually works. They discuss the importance of simplicity, storytelling, and sustainable practices in making AI easy, relevant, and famous across the business. From GenAI exploration to real-world deployment, this episode is packed with practical insights on scaling AI with purpose.

Send us Fan Mail

In this episode, Gaelle Helsmoortel joins us to discuss how to make AI truly deliver business impact, not just proof of concept.

With over 25 years of experience spanning L’Oréal, startup leadership, and her current role at Dataroots, Gaelle shares her approach to turning business challenges into measurable value. She breaks down her proven 5Ps framework (Purpose, People, Process, Platform, and Performance) and explains how companies can bridge the gap between strategy and execution to generate real results.

🎧 You’ll learn:

• Why most AI projects fail (and how to prevent it)
• How to move from proof of concept to proof of value
• How to align business purpose, data, and people for maximum impact
• Why “purpose before platform” is key to successful AI adoption

Whether you’re a business leader, strategist, or data professional, this episode will help you understand how to make AI work for business and deliver tangible results.

🔗 Connect with Gaëlle:

• 🌐 Website & Newsletter: Generative Booster – The Game Changer List
  
  
• ▶️ YouTube Channel: @GenerativeBooster

Send us Fan Mail

In this episode, we're joined by Sam Debruyn and Dorian Van den Heede who reflect on their talks at SQL Bits 2025 and dive into the technical content they presented. Sam walks through how dbt integrates with Microsoft Fabric, explaining how it improves lakehouse and warehouse workflows by adding modularity, testing, and documentation to SQL development. He also touches on Fusion’s SQL optimization features and how it compares to tools like SQLMesh.

Dorian shares his MLOps demo, which simulates beating football bookmakers using historical data,nshowing how to build a full pipeline with Azure ML, from feature engineering to model deployment. They discuss the role of Python modeling in dbt, orchestration with Azure ML, and the practical challenges of implementing MLOps in real-world scenarios.

Toward the end, they explore how AI tools like Copilot are changing the way engineers learn and debug code, raising questions about explainability, skill development, and the future of junior roles in tech.

It’s rich conversation covering dbt, MLOps, Python, Azure ML, and the evolving role of AI in engineering.

Send us Fan Mail

In this episode, we explore how public media can build scalable, transparent, and mission-driven data infrastructure - with Emilie Nenquin, Head of Data & Intelligence at VRT, and Stijn Dolphen, Team Lead & Analytics Engineer at Dataroots.

Emilie shares how she architected VRT’s data transformation from the ground up: evolving from basic analytics to a full-stack data organization with 45+ specialists across engineering, analytics, AI, and user management. We dive into the strategic shift from Adobe Analytics to Snowplow, and what it means to own your data pipeline in a public service context.

Stijn joins to unpack the technical decisions behind VRT’s current architecture, including real-time event tracking, metadata modeling, and integrating 70+ digital platforms into a unified ecosystem.

💡 Topics include:

• Designing data infrastructure for transparency and scale
• Building a modular, privacy-conscious analytics stack
• Metadata governance across fragmented content systems
• Recommendation systems for discovery, not just engagement
• The circular relationship between data quality and AI performance
• Applying machine learning in service of cultural and civic missions

Whether you're leading a data team, rethinking your stack, or exploring ethical AI in media, this episode offers practical insights into how data strategy can align with public value.

Send us Fan Mail

What happens when AI hype collides with enterprise reality? Tim Leers, Global Generative & Agentic AI Lead at Dataroots, pulls back the curtain on what's actually working—and what's not—in enterprise AI deployment today.

We begin by examining why companies like Klarna publicly announced replacing customer service teams with AI, only to quietly backtrack months later when quality suffered. This pattern of inflated expectations followed by reality checks has become common, creating what Tim calls "AI theater" – impressive demos with minimal business impact.

The conversation tackles the often misunderstood concept of "agentic AI." Rather than viewing it as a specific technology, Tim frames agency as fundamentally about delegated authority – the ability to trust AI systems with meaningful responsibilities. However, this delegation requires contextual intelligence—providing the right data at the right time—which most organizations struggle to implement effectively.

"Models are commodities, data is your moat," Tim explains, arguing that proprietary business context will remain the key differentiator even as AI models continue advancing. This perspective challenges the conventional wisdom that focuses primarily on model capabilities rather than data infrastructure.

Perhaps most valuably, Tim outlines three pillars for successful enterprise AI: contextual intelligence, continuous improvement (designing systems that evolve with changing business contexts), and human-AI collaboration. This framework shifts focus from technology deployment to sustainable business value creation.

The discussion concludes with eight practical lessons for organizations implementing generative AI, from avoiding the temptation to build proprietary models to recognizing that teaching employees to prompt effectively isn't sufficient for enterprise-wide adoption. Each lesson reinforces a central theme: successful AI implementation requires designing for change rather than building rigid systems that quickly become obsolete.

Whether you're a technical leader evaluating vendor claims or a business executive trying to separate AI reality from fantasy, this episode provides the practical guidance needed to move beyond the hype cycle toward meaningful implementation.