Kristy Mayer-Mejia, Global Head of Data Transformation at Kraken (the utility AI platform managing 90M+ customer accounts), argues that AI success depends less on model quality and more on clean, unified, well-documented data. Key insights include: siloed data is the single biggest blocker to AI value; AI acts as a forcing function for better data hygiene and documentation; static PDFs no longer suffice as documentation for AI — metadata must travel with the data (enabled by Unity Catalog and Delta Sharing); and the hardest part of data readiness is business context, not technical infrastructure. She warns against treating data as an IT problem rather than a business asset, and notes that organizations investing in data culture and skills are widening their lead over those that haven't.
Table of contents
Why data unification is table stakesThe number no one trustsAI is the forcing function enterprise analytics neededMetadata as the missing ingredientFrom monthly reports to hourly decisionsGetting people into the dataThe advice most C-suite get wrongWhat good looks like from hereClosing ThoughtsSort: