Poor data quality costs time, trust, and money—yet it’s still treated like an afterthought.
But in the age of AI, that risk just got a lot bigger.

Garbage In, Disaster Out: AI Raises the Stakes on Data Quality

You’ve heard “garbage in, garbage out.” With agentic AI, it’s worse: garbage in, disaster out. Acting on stale or broken data leads to real-world consequences—automated decisions that breach policies, trigger compliance nightmares, or harm customer trust.

In this webinar, see how Conduktor Trust enforces data quality in-stream — before bad data spreads. No retroactive cleanup. No downstream surprises. Just clean, compliant, AI-ready data the moment it hits your pipeline.

What You’ll Learn:

• Define precise rules to flag, fix, or block bad data as it’s created.

• Catch silent data quality issues before they become big problems or corrupt AI outcomes.

• Guarantee trusted data flows into AI systems, event-driven architectures and real-time analytics.

Plus: Live Demo + Interactive Q&A

Why You Can’t Miss This

AI will act on your data. Whether it’s ready or not. If your data isn’t trusted at the source, your AI outcomes will never be.
Join us — and learn how to make your operational data AI-grade by default, not by chance.