Breaking the Cycle of Data Redundancy in Large Pharma

In today’s digital era, pharmaceutical companies invest heavily in data, using it to drive clinical research, regulatory decisions, commercial strategies, and patient engagement. Yet, despite these investments, large pharma remains inefficient in how it purchases and manages data. The same datasets are often bought multiple times across business units, therapeutic areas, and geographies, resulting in redundant spending that can easily run into hundreds of millions of dollars annually. For example, companies may purchase the same medical claims data, electronic medical records, genomic data, and consumer data to support various therapeutic areas and patient journeys.

This inefficiency is a hidden cost burden, weighing down profitability and slowing decision-making. The cost of data can vary significantly depending on the granularity and coverage required. For instance, data sets with detailed patient information for rare diseases can be more expensive due to the high value of each additional patient record. A top 10 pharma company can spend over $100 million a year on data, with as much as 20-30% of this spend being unnecessary due to duplication and fragmentation. The issue is not a lack of budget but a lack of coordination.

Why Pharma Companies Struggle with Data Efficiency

The root cause of this problem lies in the siloed nature of large pharma organizations. Business units, therapeutic areas, and country-level teams operate independently, making decentralized purchasing decisions without visibility into enterprise-wide data assets. Procurement teams focus on individual deals rather than optimizing across the company. Vendor contracts vary by region, resulting in different pricing structures for the same data. IT and business teams lack a single view of what data already exists, leading to unnecessary purchases and storage costs. Without clear governance, teams often find it easier to buy new datasets rather than locate and reuse what is already available.

A Roadmap to Reducing Redundant Data Spend

To tackle this problem, pharmaceutical companies need a fundamental shift in how they manage data procurement.

• The first step is establishing a centralized data office that brings together business, IT, and procurement to oversee all data acquisition.
• Cloud-based centralized and unified data management platform should be implemented to avoid unnecessary duplication and integration costs. Consolidating data assets spread across the organization is a critical step to achieve this unification.
• This office should develop a single enterprise data marketplace where all purchased datasets are indexed, making them easily discoverable and accessible across teams.
• Instead of managing multiple fragmented contracts, companies should negotiate enterprise-wide licensing agreements with key data providers, leveraging their purchasing power for better terms and lower costs.
• Governance is critical to ensuring sustained efficiency. Clear guidelines on data sharing and usage should be established, and approval workflows must be put in place to prevent redundant purchases.
• AI-driven tools can help detect overlapping data purchases and recommend consolidation (e.g. Large language models (LLMs) can scan internal databases and suggest existing datasets that meet business requirements, reducing the need for new purchases).

To measure impact, companies must track key performance indicators such as the percentage of redundant spend eliminated and conduct regular audits to identify new savings opportunities.

Pharma companies cannot afford to keep operating in silos when it comes to data. Breaking this cycle of inefficiency requires strong leadership, an enterprise-wide mindset, and a commitment to treating data as a strategic asset rather than an operational expense.

The opportunity is massive (to unlock millions of dollars in cost savings and drive better patient insights, faster innovation, and a stronger competitive edge). The time to act is now - those who streamline their data ecosystem today will lead the industry in efficiency, agility, and scientific breakthroughs tomorrow.