Marketing Over Coffee Marketing Podcast Do You Have A Data Quality Problem? Foog Da Boot It!
Oct 17, 2025
Katie Robbert, CEO of Trust Insights and a data quality expert, shares her insights on the importance of clean data for effective AI strategies. She introduces the AI-ready data quality audit, explaining how it prevents costly AI project failures due to poor data. Katie emphasizes the need for data audits in AI planning and highlights the risks of AI-generated videos, stressing the importance of source verification. The conversation also touches on the challenges of outreach marketing tactics and the evolving landscape of AI in entertainment.
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Audit Data Before Building AI
- Do audit your data before building AI projects to avoid wasting effort on poor inputs.
- Provide a sample CSV and your intended use so Trust Insights can score and roadmap fixes in days.
Audit Revealed GA4 Issues
- Katie discovered her own Google Analytics 4 data wasn't as high-quality as assumed after running an audit live.
- That internal audit motivated building an automated, shareable AI-ready data check for others.
AI Failures Often Start With Inputs
- Many AI failures stem from poor input data rather than bad model instructions.
- Validating inputs first reframes failures as fixable data issues, not impossible model problems.