Executive Summary:
The Life Sciences industry is transitioning from siloed, manual data processes to centralized, productized data strategies that drive predictive intelligence and proactive decision-making. A leading biopharma company partnered with Infosys to overcome inefficiencies caused by fragmented systems and outdated reporting. By building a unified data architecture, implementing advanced ETL pipelines, and integrating GenAI-powered insights, the company transformed its portfolio management capabilities. This shift has improved data accuracy, reduced manual effort, and enabled real-time, strategic decisions.
Over the past two decades, the Life Sciences industry has steadily evolved from building data lakes and automating processes, to reducing manual workflows. Traditionally, organizations tackled operational inefficiencies by increasing resources, outsourcing, creating siloed point solutions, or automating individual processes.
Today, however, the industry is undergoing a significant shift toward centralized data strategies and product-centric approaches. Companies are increasingly developing data products with integrated, reusable data assets that offer insights at the product level rather than on a project-by-project basis. This evolution is enabling predictive intelligence and more proactive decision-making.
From Lagging Reports to Leading Decisions: A Biopharma’s Strategic Data Shift
A large biopharmaceutical company aimed to accelerate drug time-to-market while ensuring zero compliance misses. To support this vision, their Product & Portfolio Management (PPM) team needed accurate, real-time insights into costs, resourcing, budgeting, forecasting, and competitive intelligence.
However, the existing process was:
- Highly manual and fragmented
- Dependent on clinical data collection from over 11 disconnected systems
- Prone to errors and delays, especially in global data collation
- Generating reports that were outdated by the time decisions were made
This outdated approach was impeding informed decision-making at the portfolio level.
From Fragmentation to Flow: Engineering a Seamless Data Ecosystem
To address these issues, we collaborated with the client to:
- Develop a new Master Data Management (MDM) system focused on capturing only relevant data points
- Design a One Mesh Architecture to unify data from disparate sources
- Implement robust Extract, Transform, Load (ETL) pipelines to extract, transform, and load data into a centralized cloud environment
- Reintegrate the cleaned and structured data into visualization tools like Power BI and Tableau
Outcome – Real-Time Insights and Scalable Decision-Making
With the foundational architecture in place, we further enhanced the system by integrating Generative AI, enabling a chatbot-like interface for on-demand, conversational insights.
This transformation has:
- Significantly reduced manual reporting
- Improved data accuracy and availability
- Enabled faster, more informed decision-making for the Product & Portfolio Management Team
As a result, the client is now better equipped to meet their strategic goal of speeding up drug delivery to market without compromising compliance.
