Overview
Data reliability isn’t just a technical challenge. It’s a human one.
In this podcast, Noel Gomez explores one of the biggest barriers to trustworthy data and AI: the disconnect between business and technical teams. Organizations have enormous amounts of knowledge, but much of it remains implicit, living in employees’ heads, buried in conversations, or disappearing when meetings end.
That missing context can lead to what Noel calls “confident wrong numbers.” An AI system may quickly answer a question like, “How many active vendors do we have?” But without understanding what “active” actually means, even a technically correct calculation can produce the wrong business answer.
The solution is not simply more data, more SQL, or more documentation. It starts with capturing the business agreement behind the data.
This is the idea behind Atlas, Datacoves’ Agreement Layer for enterprise AI. Positioned upstream of SQL and data contracts, Atlas captures the why before teams determine the how. Instead of guessing when a business definition is unclear, Atlas surfaces the ambiguity and helps stakeholders resolve it.
Once agreed upon, a definition becomes a versioned, approved, reusable Definition of Record that can be accessed across the organization and by AI systems through the Model Context Protocol (MCP).
The result is a shift in how organizations think about data governance: from a technical documentation exercise to a collaborative organizational habit that helps both people and AI work from the same understanding of what is true.
Reliable data and trusted AI begin with agreement.
🎧 Watch the podcast to learn why business context is critical to data reliability, how “confident wrong numbers” happen, and how Atlas helps organizations create a shared, trusted understanding of their data.
