Dirty data isn’t just an annoyance; it’s a hidden tax on your entire business.

From dashboards no one trusts, to AI models trained on flawed inputs, to analysts spending hours fixing the same problems over and over again, poor data quality quietly drains time, money, and confidence.

In this episode of Mavens of Data, we'll be joined by Susan Walsh (data quality expert and The Classification Guru!) to unpack what “dirty data” actually looks like in the real world.

We'll talk through the most common types of dirty data, the downstream problems they cause across analytics, AI, and operations, and Susan’s COAT framework for tackling data quality in a way that actually sticks.

This isn’t about perfection or endless clean-up projects; it’s about building smarter processes, preventing problems at the source, and saving yourself (and your team) countless hours down the line.

Whether you’re an analyst, data engineer, analytics leader, or just someone tired of fixing the same broken fields every week, this conversation will change how you think about data quality.

What You’ll Learn:

• The most common (and most expensive) types of dirty data

• Why dirty data is a business process problem, not a tooling problem

• Susan’s COAT framework and how to apply it in practice

• How small design choices (like dropdowns) can prevent massive downstream issues

• Real-world horror stories and how they could have been avoided