In the evolving landscape of drug development, real-world evidence (RWE) has moved from being a complementary source of data to a central strategic asset. Yet, as per industry insiders, its true potential has long been hampered by inconsistencies in data quality, fragmented systems, and inefficient workflows. That bottleneck, however, appears to be breaking, thanks to a data modernization push led by experts deeply entrenched in pharmaceutical analytics.
Reportedly, recent advancements in centralizing data services for clinical trial planning and forecasting have enabled significant shifts in how pharmaceutical companies make operational decisions. One such initiative, according to sources familiar with its execution, led to a 35% increase in data utilization and a 20% boost in operational efficiency within just six months. This came as part of a broader strategy to enhance data accessibility and transform the forecasting lifecycle using big data and agile methodologies.
Coming from the experts’ table, Pinaki Bose, a data modernization leader working with a major pharmaceutical client, underscored the problem:
“We weren’t just dealing with fragmented datasets—we were battling siloed decision-making. The moment we integrated clinical, financial, and time tracking data, the entire calculus changed. It wasn’t just about having data; it was about trusting it.”
That trust, reportedly, didn’t come easy. A major hurdle was the consolidation of disparate real-world datasets—from clinical planning systems to ERP platforms and third-party providers like Citeline—into a centralized data lake. Using technologies such as AWS, Informatica, SQL, Alteryx, Denodo, and Tableau, Bose and his team streamlined processes that had traditionally required weeks of manual reconciliation. Forecast accuracy subsequently improved by 25%, and data discrepancies dropped by an estimated 25%, directly enhancing decision-making across the board.
Additionally, Bose led efforts to modernize legacy infrastructure, shifting from outdated data warehouses to scalable, cloud-based solutions. “The shift to AWS architecture wasn’t just a tech upgrade,” Bose noted.
“It enabled us to implement real-time dashboards, mobile-ready analytics, and set the foundation for advanced analytics and machine learning.”
These transformations are already reshaping RWE’s role in the pharma pipeline. As per the reports, centralized data repositories have been established to handle not just internal performance metrics, but also competitive analytics. A single source of truth is now informing cross-functional teams—from clinical operations to finance and supply chain—on critical metrics like cost-per-patient, which previously remained elusive due to disconnected systems.
Among the major challenges overcome was the calculation of cost per patient across granular dimensions—a task that involved data harmonization from multiple roles, time logs, actuals, and planning modules. According to Bose, this was previously a blind spot in many trial models:
“Without that level of financial insight, you’re flying blind. With clean, structured RWE, we could project not just patient enrollment but budget impacts, dropout risks, and timeline shifts.”
Notably, the team also implemented a robust data governance framework. This wasn’t a box-ticking exercise, insiders say, but a comprehensive system that directly addressed the inconsistent nature of RWE. By implementing standard validation protocols and metadata management practices, the team reduced quality-related data issues substantially—freeing up resources and enabling data scientists to focus on analysis rather than remediation.
As per experts monitoring this space, the implications are far-reaching. According to Pinaki Bose, the future of RWE in pharma isn’t just about analytics—it’s about precision.
“We are approaching an era where real-world data will allow for adaptive clinical trial design. From identifying target populations to optimizing dosing strategies, RWE will serve as the map, not just the terrain.”
The trends are already materializing. Bose points to dynamic site feasibility planning, protocol refinements based on real-world treatment performance, and enhanced comparator arm identification through RWE benchmarks. “We can now run leaner, faster trials. And more importantly, we can run trials that reflect actual patient journeys,” he said.
Forecasting too has taken a leap. Granular enrollment projections and intelligent budget models—previously hindered by outdated tools—are now possible with real-time data feeds. By modeling patient flows and risk profiles based on historical adherence and treatment data, teams are proactively managing trial risks before they manifest.
Experts suggest the evolution doesn’t stop at the trial stage. RWE is becoming increasingly pivotal in post-approval phases—supporting market access discussions, refining value-based pricing models, and enabling continuous efficacy monitoring in diverse populations.
“We’re moving beyond collecting data,” Bose emphasized.
Indeed, as pharma companies seek to bring therapies to market faster and with greater confidence, the quality and usability of their real-world data will likely determine who leads—and who lags. If the recent wave of modernization is any indication, the industry may finally be catching up to the promise of RWE.
And as experts like Pinaki Bose continue to bridge the gap between raw data and real insight, the stakes—both scientific and human—have never been clearer.