I Built an AI-Assisted Data Quality Layer for Operations Dashboards

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This article explores how AI-assisted data quality monitoring can detect anomalies, explain issues, and improve dashboard trust.
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This article proposes an AI-assisted data quality layer that sits between raw data sources and business dashboards. Combining schema validation, business-rule enforcement, anomaly detection, severity scoring, and AI-generated explanations, the system aims to identify hidden data issues before they influence business decisions. The central argument is that the most valuable role for AI in analytics may be improving trust in the data that powers dashboards rather than replacing analysts.