Featuring Julian Schwarzenbach and Tim King, authors of Managing Data Quality, this webinar will dive into the challenges and opportunities for data quality to becomes a strategic enabler, rather than a technical afterthought, in AI-driven organisations.
Drawing on real-world industry examples, this webinar explores how and why complex data landscapes, unclear ownership, legacy migrations and human behaviours can serve to undermine the reliability required for AI systems.
To avoid failed automation, misinformed models and flawed decisions from poor‑quality data, the authors will outline how standard such as ISO 8000‑61 provide a practical and scalable route to trustworthy data through explicit specifications, monitoring, assurance and continual improvement.
Key Takeaways:
- How to diagnose data quality challenges before scaling AI
- Lessons from major data‑driven failures
- How ISO 8000‑61 builds sustainable, organisation‑wide data quality
- Practical steps to embed behaviour‑change and prevent recurring data issues
Who Should Attend:
- CDOs, CIO/CTOs, data and transformation leaders.
- Data analysts / AI teams / Business process owners.
- Compliance and data governance managers.
