Five Tips to Operationalize AI-ready data with DataOps Automation

Keith Belanger, Field CTO at DataOps.live, explains why good data is important for AI success

By the time most organizations realize their AI initiatives are struggling, the root cause is already clear: the data isn’t ready. According to former Gartner analyst Sanjeev Mohan, successful AI depends on far more than model selection or infrastructure. It requires data that is contextual, unified, accessible, governed, accurate, and iterative. The six pillars describe what AI-ready data looks like, but many data leaders are now asking a harder question: “How do we implement this in practice, at enterprise scale, without burning out our teams?”  

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