This webinar, brought to you by DNAnexus, will be hosted live and available on-demand.
Tuesday, May 12, 2026
11:00 AM - 12:30 PM ET
Research and development (R&D) organizations depend on reliable data to support regulatory compliance, clinical progress, and scientific decision making. When information remains distributed across disconnected platforms, affected by inconsistent standards, or burdened by unresolved quality issues, artificial intelligence (AI) initiatives struggle to deliver meaningful results. Addressing fragmented legacy datasets, often referred to as dark data, has become essential for organizations seeking to improve operational efficiency and enable advanced analytics.
In this webinar, brought to you by DNAnexus, Katherine Novak, Zai Chang, and Raju Rayavarapu will present a modern framework for R&D data modernization grounded in practical implementation. They will examine how subject matter experts can assume ownership of critical data domains, how high-velocity remediation workflows restore data usability, and how continuous monitoring supports evolving regulatory expectations. Drawing on real-world experience, they will demonstrate how strong data foundations enable scalable AI adoption and strengthen confidence in research outcomes.
Topics to be covered
- The relationship between data quality and AI readiness
- Risks associated with fragmented and poorly governed R&D data
- Modern data governance frameworks for regulated environments
- High-velocity data remediation strategies
- Continuous monitoring for evolving regulatory and AI requirements
- Building sustainable data modernization operating models
 | Katherine Novak |
 | Zai Chang |
 | Raju Rayavarapu |
