Summary

As data environments become more complex—with sprawling pipelines, diverse systems, and growing volumes of information—ensuring consistent, trustworthy data is more critical than ever. Inconsistent data quality and fragmented lineage can undermine compliance, slow decision-making, and erode stakeholder confidence.

Join us live to explore how Data Quality Automation empowers organizations to automate quality monitoring across their enterprise data landscape. Using intelligent, context-aware rules driven by profiling, business terms, and relationship detection, the solution enables scalable, proactive governance. Discover how integrated data quality and unified lineage provide a clear, actionable view of your data ecosystem—allowing both technical and business users to drive trust and improve data quality across the entire data ecosystem.

Key Takeaways:

• End-to-End Visibility: Trace the full lifecycle of data, from ingestion to consumption.
• Integrated Context: Combine technical lineage with business metadata, including quality scores and governance policies.
• Proactive Issue Management: Detect and resolve quality issues, dependencies, and compliance risks before they impact operations.
• Stronger Trust & Compliance: Improve data reliability while meeting evolving regulatory requirements.
• Enhance unstructured data governance: Automated metadata enrichment, data cataloging, and access controls

Key speakers

Vishwas Balakrishna, Senior Product Manager, IBM Data Intelligence



Jakub Moravec, Product Manager, IBM Manta Data Lineage



Anish Patel, Program Director, Product Management, IBM Data and AI