Feature engineering vs. more data: where do you invest time first?
The classic tradeoff. What's your diagnostic for whether you're bottlenecked by feature quality, data volume, or model capacity — and how often do you guess wrong?
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The version that ships is always different from the version you planned — the question is whether the delta was intentional.
The metric that moved most wasn't the one we were watching. We only noticed in the quarterly retrospective.
Who owns the decision vs. who owns the outcome is the execution detail that matters most in our context.
The pattern I keep seeing: the signal is visible in the data much earlier than anyone acts on it.
Interesting framing. The bottleneck in our case wasn't where we assumed. Worth a short experiment before committing to a solution.
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