This is an important distinction that gets overlooked in a lot of AI discussions.
Data answers what happened. Metadata adds context. Knowledge captures why people trust certain paths and ignore others.
The challenge for enterprise AI isn't usually accessing more data it's surfacing the tribal knowledge that lives in senior engineers' heads, Slack threads, and years of operational experience.
That's also why many AI analytics initiatives struggle despite having great data catalogs. The model can read the schema, but it doesn't automatically know which metric the business actually trusts or which pipeline everyone avoids. Turning that tacit knowledge into something machines can reason about feels like one of the next big problems to solve.