What happens when a former NHL player who once faced Wayne Gretzky ends up running a global data company at the center of the AI boom? That question kept coming back to me when I reconnected Mike McKeethe CEO of Atacamaseven years after our last conversation. So much has changed in the world since then, yet the theme that shaped this discussion felt surprisingly grounded. None of AI’s great promises can become reality unless leaders can trust the data that lies beneath every system they use.
Mike brings a rare mix of stories and experiences to this theme. His journey from the ice to the C-suite feels like his own lesson in discipline, teamwork and patience, and he openly reflects on how those early years influence how he leads today. But the heart of this conversation lies in the reality he sees within global corporations. Everyone is rushing to build AI-powered services, but the biggest blockers are messy records, inconsistent metadata, long-forgotten databases, and years of quality issues that have never been addressed. It’s a blunt problem, and Mike explains why the companies winning with AI right now are the ones that treat data trust as a foundation, not an afterthought.
During the discussion, he shares stories from organizations like T Mobile and Prudential, where millions of records, thousands of systems, and vast amounts of structured and unstructured data need to be monitored, understood, and managed in real time. Mike shows how teams are rebuilding trust in their data, why quality scores are important, and how automation is now shaping everything from compliance to customer retention. What’s most striking is how quickly expectations have shifted. Executives and CEOs are now treating data as a strategic asset rather than an operational chore, and entire roles have emerged above the Chief Data Officer to drive these programs.
This episode also reminds us that AI progress is never just about models or GPUs. Mike pulls back the curtain on why organizations struggle to measure AI readiness, how to avoid bottlenecks, and what it takes to prioritize the work that really matters. His point is simple. Without reliable data, AI remains a promise rather than a practical tool. This allows companies to act with confidence, respond faster and make decisions that actually improve outcomes for customers and employees.
So as AI penetrates deeper into systems everywhere, how should leaders rethink their approach to data trust, management and quality? And if you’ve been on a data-challenged journey yourself, where have you seen progress and where are you still stuck? I would like to hear your opinion.
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