We’re introducing the Data Trust Score Engine, an AI-driven, cloud-native system that measures and improves enterprise data reliability by assigning a dynamic trust score from 0 to 100 to each dataset, across multiple quality dimensions such as completeness, consistency, uniqueness, freshness, schema compliance, null-value distribution, statistical anomalies, and AI-detected outliers.

The goal is a scalable, intelligent platform on Google Cloud Platform that continuously monitors data quality on GCP, reducing risk from bad data and boosting decision-making across BI and AI initiatives.

Key beneficiaries include data engineers, cloud architects, governance teams, and AI practitioners who will leverage the platform to monitor dataset health and ensure reliable analytics and AI outcomes.

The engine addresses the limitations of traditional rule-based data quality approaches and offers a scalable, adaptive alternative capable of detecting complex anomalies and evolving data quality issues.

Built on Google Cloud, the solution uses BigQuery for data warehousing, Data Catalog for metadata management and governance, and Vertex AI for anomaly detection and predictive quality analysis, enabling scalable processing and intelligent insights.

Beyond scoring, the engine functions as an enterprise governance platform with interactive dashboards, automated alerts for low trust scores, remediation prioritization by business impact, and REST APIs for external applications to verify dataset trustworthiness.

The article outlines an end-to-end architecture and workflow—from data ingestion and metadata collection to AI-powered quality assessment, trust score calculation, visualization, and automated monitoring—providing a practical blueprint for a production-ready data quality framework.