Data quality is crucial for businesses as poor data can lead to missed opportunities, compliance risks, and a loss of trust. With increasing reliance on AI and analytics, 64% of enterprises prioritize data integrity, yet many lack formal measures to ensure it. Effective data governance in the AI era requires measuring data quality through clear metrics and thresholds, such as accuracy, completeness, consistency, timeliness, validity, and uniqueness. These dimensions can be quantified using specific formulas to create a framework that aligns governance policies with actionable insights. Establishing a data quality measurement framework involves identifying critical data elements, mapping business rules to technical checks, setting service level objectives, and assigning ownership to ensure accountability. By employing metrics, KPIs, and scorecards, organizations can translate governance goals into enterprise-wide accountability and visibility, enhancing decision-making and compliance. Tools like Acceldata's Agentic Data Management platform can optimize data quality management by tracking metrics, enforcing thresholds, and consolidating alerts and dashboards, thus operationalizing data quality measures and ensuring trusted insights across the enterprise.