Skip to main content

GlaxoSmithKline (GSK)


Partha Vuppalapaty, Director, Data Products


GlaxoSmithKline (GSK)


Life Sciences: Pharmaceutical

Business Challenge

GSK vaccines started the journey of undergoing a widespread data and analytics transformation in Q2-2019 across the enterprise. This included an ambition to future-proof data architecture in R&D, manufacturing, supply chain, quality and commercial functions with a specific focus on improving data-driven culture, user experience and decision impact. These capabilities aimed to accelerate science, improve scientific productivity, drive manufacturing production efficiencies and boost commercial growth opportunities.

Analytics Solution

The team developed business architecture, information and platform strategies that modernized business capabilities to deliver desired business outcomes and objectives. Enterprise data lifecycle management and analytics capabilities are deployed with GxP readiness in less than a year, along with all prioritized business solutions.

The team embraced hybrid cloud architecture with features such as disposable infrastructure, data product balance sheets, fully automated data catalogs, self-service BI on SQL or NOSQL databases and search analytics. This also involved agile ways of working and building relationships with technology partners and strategic system integrator partners. Throughout 2020, the team also strengthened academic partnerships by focusing on continued collaboration and mentorship of young talent.


Enterprise alignment was achieved on 30+ data and analytics technology capabilities, including data quality, data integration, CI/CD and search analytics. More than 50 business solutions were delivered in 2020 that positively impacted scientific productivity in R&D and improved manufacturing quality intelligence, campaign analytics and many other business processes. As an example, one of the solutions realized huge business impact by generating timely insights that helped to close more than 90% of deviations on-time. This solution was delivered by applying modern data engineering, AI and machine learning techniques, leveraging historical data to find real-time insights and correlations between signals and events.