Monitor data quality and compliance

The weekly compliance dashboard shows three data sources failing the validation rules. Send the corrected dataset and the change log to Noor in Quality and to Javier in Compliance for sign-off, Noor first, Javier second, with a Monday morning deadline tied to the audit window. Before sending, run the row-level validation so the failure rows are isolated and include a one-line note explaining each corrective action.

Before I publish the data-quality workbook to Noor, make it easy to accept. Move the failing-rule counts to the top, make the data lineage for each field discoverable in one click, and flag any sources that had manual overrides in the past month. Add a short risk note for each failing source so Noor can decide whether to accept or escalate without jumping into raw tables.

Tonight the dashboard spiked with validation errors from the lab feed and Noor will want answers first thing; she will assume our ETL broke and escalate. I'm worried the team overwrote a code column during a merge. I can't tell if the problem is the source file, my recent merge, or a changed validation rule. What sequence of quick tests will identify the true cause so I can tell Noor what happened before she calls a meeting?

We keep fixing the same data-quality failures every week: teams patch spreadsheets, dashboards pass, then the same source fails next cycle. That pattern costs credibility with Noor and Javier and eats analyst time. Which recurring control or habit—standardized import template, automated pre-submit validation, or a pro forma exception log—will most reliably stop this loop and how do I pilot it with the data owners?