DQLabs enhances IBM Db2 by embedding AI-driven data quality and observability directly into the database environment, ensuring reliable data for critical enterprise applications. With automated monitoring and real-time anomaly detection, DQLabs identifies issues such as missing or inconsistent data, schema mismatches, and outliers that can affect the accuracy and completeness of transactional or analytical datasets stored in Db2. DQLabs applies business rule enforcement across various stages of data processing, flagging discrepancies early and reducing the risk of downstream errors in reporting or analytics. This integration enables organizations to continuously track key data health metrics, such as freshness, accuracy, and consistency, and proactively address potential issues before they escalate. By providing automated data lineage, DQLabs offers full visibility into the data’s flow across the Db2 ecosystem, ensuring compliance with data governance standards and supporting auditability. All of this is driven by automation, empowering teams to ensure trusted data for mission-critical applications and AI-driven initiatives.