In today’s rapidly evolving pharmaceutical landscape, ensuring the safety and efficacy of drugs is more critical than ever—and pharmacovigilance plays a pivotal role in this mission. With the vast amount of data generated across the industry, maintaining high-quality information is essential for safeguarding patient health. However, common data challenges such as redundancy, inconsistencies, and gaps can disrupt pharmacovigilance workflows, delaying crucial safety decisions.
This must-attend webinar will dive into the importance of data quality in pharmacovigilance and offer practical solutions to tackle these challenges. Our expert speaker will provide insights into innovative strategies, including advanced data pipeline techniques that effectively detect and manage redundant or isolated data. You’ll also learn about best practices for optimizing data acquisition and integration, ensuring seamless pharmacovigilance operations.
We’ll explore real-world examples where deduplication and normalization techniques have been successfully applied to enhance data quality and streamline processes. Through case studies, you’ll see firsthand how efficient data management can dramatically improve the speed and accuracy of safety monitoring.
Finally, we’ll discuss the future of pharmacovigilance, with a focus on how emerging technologies like AI and machine learning are set to revolutionize data quality and integration, unlocking new opportunities to enhance pharmacovigilance operations.
Key Takeaways:
Join us to equip yourself with the latest knowledge and tools needed to enhance data quality in pharmacovigilance, ensuring better safety and efficacy of pharmaceutical products.
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Senior Director of Customer Success
Chandra has over 25 years of experience as a technology leader specializing in Data, Analytics, and AI/ML. He has a wealth of experience working with US-based pharmaceuticals and life science organizations across various domains including Clinical Data Management, Patient Services, R&D, Drug Discovery, Quality Assurance, Safety/Pharmacovigilance (PCV), Commercial Operations, Omnichannel Marketing, and Enterprise Solutions. Chandra is dedicated to driving customer satisfaction and fostering the growth of current accounts by delivering innovative cloud-based clinical data and analytics solutions, aiming to become a trusted partner in our customers' data journey. His career highlights include significant roles at Cognizant Technology Solutions, Morgan Stanley, L&T Infotech, IBM, Oracle, and Accenture, where he contributed to major projects and provided advanced technology solutions. With his extensive experience in the US, Singapore, and India, Chandra brings a global perspective and deep understanding of the clinical data industry, ensuring MaxisIT continues to deliver cutting-edge solutions that meet the evolving needs of our clients.
Product Marketing Lead
With more than ten years in high-impact marketing roles, Ani is at the forefront of MaxisIT's drive in the life sciences sector. Her expertise lies in intuitively grasping customer needs and skillfully converting these insights into practical solutions, ensuring the delivery of pertinent products. Ani's professional journey, which began in commercial banking, evolved through co-founding a thriving marketing agency, where she catered to global corporations like Johnson & Johnson and Coca-Cola. Now at the helm of MaxisIT's data analytics platform, Ani is committed to enhancing its efficiency in clinical trials. Her diverse background empowers her to not only maintain the platform’s esteemed reputation but also to expand its capabilities in delivering impactful trial outcomes. Her approach is customer-centric, always focusing on how MaxisIT can better serve and innovate within the life sciences sector.
MaxisIT helps pharmaceutical and biopharmaceutical companies of all sizes plan and execute effective clinical trials. Our source-agnostic integrated clinical data analytics platform unifies data across clinical systems and CROs to enable real-time monitoring and on-time insights.