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HIGHLIGHTS
- Our paper, "AI Empowered Metadata Governance," was recognized as the best paper in the metadata management category at PharmaSUG 2025, one of the industry's highly rated conferences, showcasing the innovative solutions we bring to the life sciences ecosystem.
- Current challenges in metadata governance are marked by complex trial designs that create siloed standards protocols with specific nuances, exacerbating difficulties and justifying deviations from established norms, despite metadata's importance for streamlined data collection, analysis, and standardized transformations.
- The paper demonstrates how AI and ML enrich metadata management by enabling metadata discovery and delivering benefits that boost reusability, traceability, and automation while providing easier maintenance and elevated metadata quality with reduced complexities.
We are thrilled to share that our paper, "AI empowered metadata governance," has been recognized as the best paper in the metadata management category at PharmaSUG 2025 — the annual conference of the non-profit PharmaSUG, and one of the industry's highly rated conferences took place between June 1-4 at San Diego, CA.
The achievement reflects our leadership's forward-thinking approach to applying next-generation technologies to transform clinical data and analysis and showcases innovative solutions we bring to the life sciences ecosystem.
Significance of metadata governance
Efficient metadata governance is pivotal for seamless digital data flow, ensuring streamlined data collection, analysis, and standardized transformations. It cultivates standardization, harmonization, reusability, and automation. Despite the evolving importance of metadata, complex trial designs often complicate governance, making it an intricate process. This leads to siloed standards and protocols with specific nuances, further exacerbating the challenges and justifying deviations from established norms.
Transformative role of AI and ML
The paper highlights the disruptive role of AI/ML in improving metadata management. It explores the transformative role of AI and ML in enriching metadata management. AI and ML present a paradigm shift through metadata discovery, creating sponsor-aligned metadata standards, facilitating rapid transitions between multiple versions of standards, and maintaining the lineage between them.
The paper highlights the ease of maintaining standards, generating traceability and transformation specifications, and identifying redundancies. It accommodates protocol-specific changes within the defined organizational hierarchy. The invaluable benefits of AI/ML to elevate metadata quality, minimize complexities, and boost reusability, traceability, and automation also find a place in the paper. Explore the paper here.
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