Embedding Data Governance in Biologics Manufacturing for Greater Reliability and Efficiency

Key Takeaways

• Understand the strategic role of data governance in ensuring patient safety and drug efficacy in biologics manufacturing.
• Learn best practices for maintaining data integrity and traceability across development, manufacturing, and commercial operations.
• Explore how manual and digital processes can introduce data risks and ways to mitigate them.
• Discover how AI and machine learning enhance process efficiency, quality, and continuous improvement in biologics production.
• See how robust data governance supports regulatory compliance, operational efficiency, and waste reduction.
• Understand the impact of data and AI on CDMO–biotech collaboration, from speeding early-stage development to ensuring reliable commercial manufacturing.

Discover how data governance and AI are transforming biologics manufacturing, from ensuring patient safety to improving process efficiency. Sundar Ramanan, PhD, MBA, Chief Quality Officer at Enzene, shares insights on leveraging data for quality, compliance, and collaboration between CDMOs and biotech innovators.

Sponsored by Enzene

Sundar Ramanan, PhD, MBA, Chief Quality Officer at Enzene, discusses the growing strategic importance of data governance in biologics manufacturing. He emphasizes that patient safety and drug efficacy rely on the integrity of data collected across development, manufacturing, quality control, and quality assurance, with data governance providing the framework for reliable decision-making and regulatory compliance.

To ensure data integrity and traceability, Dr Ramanan explains that companies should map the entire process from development to patient distribution, classifying each step as manual or digital and identifying potential errors. Most data integrity issues are unintentional, and implementing prevention, detection, and optimization mechanisms strengthens reliability and traceability.

Strong data governance not only supports compliance but also drives operational efficiency, reduces waste, and enhances overall quality. Once data is digitized, machine learning and AI can detect trends, improve process robustness, and support continuous improvement.

At Enzene, data and AI are being integrated across computerized and manual systems to optimize yield, decision-making, and predictive analysis. Looking ahead, Dr Ramanan notes that these tools will enhance efficiency, transparency, and collaboration between CDMOs and biotech innovators, building trust, accelerating clinical timelines, and ensuring consistent production of safe and effective biologics.

Source : https://www.biopharminternational.com/