What kills data quality—and how do we fix it?

Poor definitions, stale data, duplicates, and weak lineage. Bias and sampling errors skew models. Fix the basics: assign data owners, define business terms, set validation rules, standardize IDs, and implement MDM where needed. Monitor quality with automated tests and scorecards; route issues like tickets.