Description

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Ruud Kuil, longtime data quality expert and now co-founder of MAD-Quality, to discuss what really breaks in organizations when data is wrong and why fixing it requires rethinking both tooling and incentives.

After 15+ years advising enterprises on data quality, Ruud took the plunge into startup life to build a platform that shows the real cost of bad data. In conversation, he explains why the “AI will fix data” myth persists and what it takes to make data quality everyone’s responsibility.

Together, they explore:

• Why most data quality tools miss the point — and how to flip the model

• The challenge of selling long-term value in short-term cultures

• Why AI only works when your foundations do (and what happens when they don’t)

• How to calculate and communicate the business impact of poor data

• The case for a new kind of KPI: delayed bonuses tied to durable data practices

🎧 Tune in for a conversation about the economics of data quality, the illusions of automation, and what it really means to build trust in an AI-first world.

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