DataTopics: All Things Data, AI & Tech

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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.

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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.

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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.

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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.

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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