Is "Quality" Killing Your AI? Defining Data Fit for Strategic Success
On-Demand Webinar | Watch the recording of our session with Andy Hannah & Malcolm Hawker
In this session from Blue Street Data’s Building with Better Data series, Andy Hannah (CEO & Co-Founder, Blue Street Data) and Malcolm Hawker (CDO, Profisee) explored a controversial but critical reality: data quality wasn’t the biggest barrier to AI adoption — data fit was.
They discussed how data that appeared “high quality” for a BI dashboard could become “toxic” for a Large Language Model, leading to model failure, wasted investment, and eroded trust. The conversation focused on how organizations could move beyond generic data cleanliness and instead define, measure, and verify true data fit to ensure AI and analytics models performed reliably and at scale.
They covered:
- What the “Fit vs. Quality” framework looked like in practice for AI vs. BI use cases
- Why models broke when “good” data for humans became problematic for machines
- How to calculate the risk of deploying models without proper fit testing
- Practical steps to align data procurement and preparation with specific business outcomes
Whether you’re a CDO, AI architect, or analytics leader, this webinar provided a clear shift from chasing “clean” data to securing “fit” data — turning data strategy into a measurable competitive advantage.
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