Data Quality Management Workflows

A Sales Operations Analyst uses this dashboard to prevent corrupt lead records from silently entering the CRM. Out of 1,250 extracted records, bullet gauges reveal a 67.2% valid rate, leaving 410 flagged records requiring remediation. A horizontal bar chart categorizes these extraction failures from the AI vision model. The primary issue is Address/Postal Mismatch affecting 150 records (36.6%), followed by Placeholder/Junk Values (120 records), Invalid Email Formats (85 records), and Missing Critical Data (55 records). By isolating these specific error types, the analyst can deploy data scrubbing tools to fix geographic mismatches and prevent hard bounces, ensuring only quality-gated lead lists advance into the outbound campaign pipeline.

What it shows:

Categorizing flagged records by specific error types enables targeted remediation before CRM ingestion.

Prior to a CRM migration, a data operations analyst uses this report to evaluate address record completeness and parsing gaps. A bar chart shows six core fields achieve 100% completeness, while postal codes appear in 57.6% of records and state/region data drops to 10.5%. Text panels reveal country-specific issues, noting that Kosovo shows 0% coverage for postal and state fields across 831 rows. A donut chart visualizes missing-field patterns, indicating 47.1% of rows lack only the state/region, while 42.4% are missing both postal code and state. This visibility acts as a baseline for data quality testing tools to identify formatting issues in JSON-serialized address data.

What it shows:

Visualizing missing-field patterns highlights systemic parsing gaps that must be addressed prior to migration.