Designing Regulatory Intelligence Dashboards for Pharma Portfolio Management

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Lucky Jha

ABESIT

Crossings Republik, Ghaziabad, Uttar Pradesh 201009,

luckyjha200405@gmail.com

Abstract
In the contemporary pharmaceutical environment, regulatory landscapes shift rapidly, driven by evolving guidelines, novel health crises, and geopolitical factors. Traditional compliance tracking—often reliant on manual spreadsheets and disparate internal portals—can no longer keep pace with the volume, velocity, and variety of regulatory data. Regulatory intelligence dashboards have emerged as transformative tools, consolidating structured and unstructured data streams into unified visual interfaces. By integrating real-time alerts, predictive analytics, and customizable reporting modules, these dashboards empower portfolio managers to anticipate regulatory risks, optimize submission strategies, and prioritize assets across global markets. This study presents a holistic framework for designing such dashboards, grounded in user-centered design principles and agile data engineering practices. Through stakeholder interviews across 12 mid-to-large pharma firms, iterative prototyping in Power BI, and performance benchmarking under simulated regulatory load, we demonstrate significant efficiency gains: a 40% reduction in report-generation time, a 30% improvement in dossier readiness scores, and enhanced cross-functional collaboration. Simulation scenarios further illustrate how AI-driven signal detection can preempt compliance delays by up to six weeks. We conclude by discussing the architectural requirements for scalable, cloud-native deployments, the need for semantic data standards (IDMP, FHIR) to automate metadata harmonization, and the governance frameworks necessary to safeguard sensitive dossier information. Although our findings are derived from firms with mature data infrastructures, they lay the groundwork for broader adoption across the industry.

Keywords

Regulatory Intelligence, Pharma Portfolio Management, Dashboards, Compliance Analytics, Data Visualization, Drug Lifecycle, Global Regulations, AI in Pharma, Risk Assessment, Decision Support

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• Satish Krishnamurthy, Krishna Kishor Tirupati, Sandhyarani Ganipaneni, Er. Aman Shrivastav, Prof. (Dr.) Sangeet Vashishtha, & Shalu Jain. (2024). “Leveraging AI and Machine Learning to Optimize Retail Operations and Enhance.” Darpan International Research Analysis, 12(3), 1037–1069. https://doi.org/10.36676/dira.v12.i3.140.
• Akisetty, Antony Satya Vivek Vardhan, Rakesh Jena, Rajas Paresh Kshirsagar, Om Goel, Arpit Jain, and Punit Goel. 2024. “Leveraging NLP for Automated Customer Support with Conversational AI Agents.” International Journal of Research in Modern Engineering and Emerging Technology 12(5). Retrieved from https://www.ijrmeet.org.
• Akisetty, A. S. V. V., Ayyagari, A., Pagidi, R. K., Singh, D. S. P., Kumar, P. (Dr) S., & Jain, S. (2024). “Optimizing Marketing Strategies with MMM (Marketing Mix Modeling) Techniques.” Journal of Quantum Science and Technology (JQST), 1(3), Aug(20–36). Retrieved from https://jqst.org/index.php/j/article/view/88.
• Vardhan Akisetty, Antony Satya Vivek, Sandhyarani Ganipaneni, Sivaprasad Nadukuru, Om Goel, Niharika Singh, and Prof. (Dr.) Arpit Jain. 2024. “Developing Data Storage and Query Optimization Systems with GCP’s BigQuery.” International Journal of Worldwide Engineering Research 02(11):268-284. doi: 10.XXXX/ijwer.2584-1645.
• Vardhan Akisetty, Antony Satya Vivek, Aravind Ayyagari, Ravi Kiran Pagidi, Dr. S P Singh, Prof. (Dr.) Sandeep Kumar, and Shalu Jain. 2024. “Optimizing Cloud Based SQL Query Performance for Data Analytics.” International Journal of Worldwide Engineering Research 02(11):285-301.
• Vardhan Akisetty, Antony Satya Vivek, Ashvini Byri, Archit Joshi, Om Goel, Dr. Lalit Kumar, and Prof. Dr. Arpit Jain. 2024. “Improving Manufacturing Efficiency with Predictive Analytics on Streaming Data.” International Journal of Progressive Research in Engineering Management and Science 4(6):2528-2644. https://www.doi.org/10.58257/IJPREMS35036.
• Bhat, Smita Raghavendra, Rakesh Jena, Rajas Paresh Kshirsagar, Om Goel, Arpit Jain, and Punit Goel. 2024. “Developing Fraud Detection Models with Ensemble Techniques in Finance.” International Journal of Research in Modern Engineering and Emerging Technology 12(5):35. https://www.ijrmeet.org.
• Bhat, S. R., Ayyagari, A., & Pagidi, R. K. (2024). “Time Series Forecasting Models for Energy Load Prediction.” Journal of Quantum Science and Technology (JQST), 1(3), Aug(37–52). Retrieved from https://jqst.org/index.php/j/article/view/89.
• Bhat, Smita Raghavendra, Aravind Ayyagari, Ravi Kiran Pagidi, Dr. S P Singh, Prof. (Dr.) Sandeep Kumar, and Shalu Jain. 2024. “Optimizing Cloud-Based SQL Query Performance for Data Analytics.” International Journal of Worldwide Engineering Research 02(11):285-301.
• Abdul, Rafa, Arth Dave, Rahul Arulkumaran, Om Goel, Lalit Kumar, and Arpit Jain. 2024. “Impact of Cloud-Based PLM Systems on Modern Manufacturing Engineering.” International Journal of Research in Modern Engineering and Emerging Technology 12(5):53. https://www.ijrmeet.org.
• Abdul, R., Khan, I., Vadlamani, S., Kumar, D. L., Goel, P. (Dr) P., & Khair, M. A. (2024). “Integrated Solutions for Power and Cooling Asset Management through Oracle PLM.” Journal of Quantum Science and Technology (JQST), 1(3), Aug(53–69). Retrieved from https://jqst.org/index.php/j/article/view/90.
• Abdul, Rafa, Priyank Mohan, Phanindra Kumar, Niharika Singh, Prof. (Dr.) Punit Goel, and Om Goel. 2024. “Reducing Supply Chain Constraints with Data-Driven PLM Processes.” International Journal of Worldwide Engineering Research 02(11):302-317. e-ISSN 2584-1645.