Oct 15 2025
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Next-Gen Data Access for Pharma: How Integrated Data Ecosystems Are Powering Global Pharma Decision-Making
Introduction:
In today’s dynamic pharmaceutical landscape, the need for seamless data access and faster decision-making has made AI-powered, cloud-based solutions a cornerstone of innovation. As a pharma company that works across multiple therapy areas simultaneously, traditional data silos have proven inefficient for providing insights into country-specific disease burden, epidemiology research, and, essentially, market forecasts. To address these challenges, Thelansis developed an AI-enabled cloud platform that transforms how organizations access, analyze, and apply epidemiology and market insights. This case study explores how Thelansis partnered with a global biopharma client to streamline their research processes.
Objective:
A client with a diverse product portfolio sought a centralized, scalable, and Competitive Intelligence system to access reliable epidemiology and disease landscape insights. Our team collaborated with theirs to understand their requirements, based on the product pipeline, lifecycle management priorities, and future interests. Our primary goal was to simplify access to epidemiology and market forecasts across multiple therapeutic areas and geographies, enabling them to support their needs with real-time, high-quality insights.
Approach:
We further customized our proprietary AI-enabled cloud platform, transforming data into a user-friendly technology, and provided access to epidemiology and landscape insights for all prioritized indications. The platform offered intuitive dashboards, data visualization, and exportable reports, ensuring easy integration with the client’s internal systems.
During demo sessions, our team showcased the depth and breadth of epidemiology coverage at a global level, including prevalence, incidence, demographic distributions, severity-wise distribution, diagnosis, prognosis, treatment utilization, patient journey, and emerging therapy.
In the pilot phase, the client’s teams accessed the platform to get real-time research insights. They mentioned that this tool helped them save a significant amount of time and provided 24/7 analyst-like support to address queries, ensuring seamless adoption and confidence in data quality.
Outcome:
- 60% reduction in time spent gathering and validating disease area landscape data
- Enhanced decision-making through consistent, validated, and high-quality datasets with visual analytics
- 5X times faster data processing and report generation