The conditions that decide whether anything delivers.
A loyalty migration made data more accurate and much harder to use. The only thing standing between the numbers and a bad decision was someone who remembered the change. That safeguard was never in a system, and it's exactly the context your AI can't inherit.
A few years ago, I watched a product recommender start behaving oddly. The model was fine. The pipelines were fine. What had changed was something nobody on the data science team knew about. That moment showed me something I now see everywhere: data visibility is AI's most overlooked bottleneck.
The real power of data lies in context: understanding the data itself, its origin, and its intended use. Without clear definitions and metadata, even accurate data gets misinterpreted. Treat data quality as a business decision, not a technical one.