I've been analyzing customer churn for a fintech startup, and our team spends half our time cleaning messy transactional data before any model sees it. Everyone preaches 'data-driven decisions', but what happens when the underlying data itself is biased from a self-selected user base—like our app being used mostly by young urban males? Should we blindly trust patterns we mine, or do we need to constantly question the collection process and actively seek missing perspectives? I'm leaning toward the latter, but I'd love to hear how others handle data quality when there's no 'ground truth' to compare against.