The Year in Review- the year in Data Analytics

The hype says AI. The reality says data. As AI adoption accelerated across industries, organizations discovered an uncomfortable truth: the quality of AI outputs depends entirely on the quality, accessibility, and trustworthiness of the data that underpins them. There is a pattern across this year's pieces - the shift from data as a technical problem to data as a strategic enabler. Whether it's breaking down silos, democratizing access for non-technical users, or building the monitoring infrastructure needed to trust what AI delivers, the enterprises making real progress are those that understand a simple truth - without trusted, well-governed data, AI is just an expensive experiment.

This isn't about agent-washing. Our customers are already doing agentic AI - what they need now is a way to do it at scale, with confidence and visibility.

Why? With enterprise AI moving from experimentation to production, Databricks' launch of Lakeflow Designer and Agent Bricks ...