Overview
Data quality reports fail when they are written for data engineers and read by business stakeholders who don't know what a null rate is or why it matters. A report that lists "column X has 23% null rate" without explaining what decisions are affected by that missing data produces no action — the business stakeholder doesn't know whether 23% is a crisis or acceptable.
A data quality report that drives action translates every technical finding into a business consequence: which reports are affected, which decisions are based on unreliable data, and what the cost of the quality issue is in terms of rework, wrong decisions, or missed opportunities. The technical finding is evidence. The business consequence is the reason to act.
The Data Quality Report Prompt generates a complete quality reporting framework: quality scorecard with business-impact translation, issue prioritization by downstream consequence, root cause analysis, and a remediation roadmap with owners and timelines.
What you get: - Quality scorecard across 6 dimensions with business-impact translation - Issue prioritization by downstream business consequence - Root cause analysis framework - Remediation roadmap with owners and timelines - Trend tracking for quality improvement over time
Built for: data stewards, analytics engineers, and data governance teams communicating data quality status to business and technical stakeholders.