Data Quality Processor Dashboards
Data Quality (DQ) Processor Dashboards screen provides a consolidated view of the outcomes generated after executing Data Quality (DQ) rules. It enables users to assess data quality performance, identify potential issues, and take corrective actions based on rule evaluation results.
The screen is organized into the following sections:
Displays machine learning based anomaly detection insights for analyzer metrics. It helps users identify unusual patterns, deviations from historical trends, and unexpected changes in data quality metrics. Users can review detected anomalies, analyze metric behavior against predicted baselines, and provide feedback to improve future anomaly detection accuracy.
Deviation Analysis for Analyzer Metrics:
Displays statistical and trend-based insights derived from analyzer rules, helping users understand variations, anomalies, and changes in data quality metrics over time.
Shows the results of automated or configured issue resolution actions applied to detected data quality issues, including the status of fixes and any remaining unresolved records.
Presents the execution outcomes of validator rules, highlighting passed and failed rules, affected records, and the severity of identified data quality violations.
Together, these sections provide a comprehensive view of data quality health, enabling users to monitor trends, investigate issues, and ensure that datasets meet defined quality standards.
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