Is "Quality" Killing Your AI? Defining Data Fit for Strategic Success скачать в хорошем качестве

Is "Quality" Killing Your AI? Defining Data Fit for Strategic Success

In this episode of Building with Better Data, Andy Hannah (CEO & Co-Founder, Blue Street Data) and Malcolm Hawker (Head of Data Strategy, Profisee) challenge one of the biggest misconceptions in AI: data quality isn’t the primary barrier to success - data fit is. When data works perfectly for a BI dashboard but fails inside a Large Language Model, organizations risk model breakdowns, wasted investment, and lost trust. This session explores why “clean” data isn’t enough and what it really means to ensure your data is fit for purpose. This session covers: • The “Fit vs. Quality” framework for defining the right data for AI vs. BI use cases • Why models fail when “good” human-readable data becomes problematic for machines • How to calculate the risk of deploying models without proper fit validation • Practical steps to align data sourcing and preparation with measurable business outcomes Whether you’re a CDO, AI architect, or analytics leader, this conversation will help you move beyond generic data cleanliness and toward a fit-first strategy that drives reliable AI and analytics performance.