Date & Time: 22 April 2026 • 15:30 (Copenhagen)
Register: Sign up on Zoom
AI is raising the stakes for data quality and exposing just how broken many data foundations really are.
From invoices that don’t add up, to inconsistent logistics data, to finance numbers that refuse to reconcile, the cracks are becoming impossible to ignore. Traditional “cleanup projects” and Excel workarounds simply can’t keep up in an AI-driven world.
In this session, we’ll explore what a modern, always-on approach to data quality looks like, and why continuous monitoring is the only way forward.
This won’t be a typical one-way presentation. Instead, join a dynamic, conversational session where real-world challenges meet live product insights:
- Stefan and Melanie will walk through common (and painfully familiar) data quality issues across billing, logistics, and finance
- Along the way, we’ll jump in with live discoveries from the TimeXtender Data Quality product and roadmap, showing how these exact problems are being solved in real time
Expect a practical, engaging session with:
- A fresh perspective on data quality in an AI-first world
- Real examples of how poor data impacts analytics and AI outcomes
- A live demo of how TimeXtender Data Quality continuously monitors data, applies rule-based controls, and alerts teams before bad data spreads
If you want reliable AI and analytics, you need to fix data quality at the source, continuously, not reactively. What to expect:
✔ Live demo
✔ Real-world scenarios
✔ Interactive, conversational format
