As global biopharmas expand into new markets, they introduce more systems to handle increasing data volumes and business operations, building layers of disconnected data, systems, and processes. While this fragmentation may not seem urgent, it quietly drives up costs and inefficiencies.
According to Gartner, poor and disconnected data can cost companies an estimated $12.9 million annually. Beyond financial losses, data fragmentation is the biggest roadblock preventing AI and analytics initiatives from taking off.
Recognizing these challenges, major players in the industry have taken proactive steps to harmonize their data globally.
A data leader in a top 20 biopharma shares, “With 150 years of history, our data is spread across numerous systems, making it difficult to connect the dots and maximize customer value. To address this, we began harmonizing global reference data to create a single, unified customer record. While it’s an ongoing process, we’re laying the foundation for future success in over 100 markets.”
Meanwhile, another top 20 biopharma completed a significant transformation driven by a global initiative to centralize governance, highlighting the critical need for foundational data. This company successfully rolled out a single global data model across more than 50 markets. Both organizations have reported benefits from global data harmonization, including faster field agility, improved reporting, and reduced costs.
Experts agree that harmonizing data globally delivers significant benefits, but taking the first leap is the hardest part.
From securing buy-in to managing change, they share some key lessons that contributed to their transformation’s success.
Before embarking on a global project, measuring existing inefficiencies and setting clear benchmarks is essential. Key metrics — such as time-to-insight and reduction in manual processes — help demonstrate value to stakeholders and track progress over time.
For example, one company aimed to create a 360-degree view of HCPs across clinical, commercial, and medical teams. Unified data allowed them to create this view 90% faster than previously possible, enabling teams to access all HCP interactions on a single screen, significantly improving time-to-insight.
Another company has transformed customer engagement speed, reducing processes like adding new customers, capturing consent, and digital engagement from 3-5 days to mere minutes. Similarly, they also simplified data provisioning. Before, subscribing to new customer data took around 3-4 months, and involved both business and IT. A unified data model enabled a faster turnaround time for new data,
Tying projects to measurable outcomes — such as agility, accuracy, and cost savings — ensures stakeholder buy-in and alignment with company goals.
Although technical challenges in data harmonization are significant, the biggest hurdle is often navigating resistance to change.
Some companies prioritized change management, recognizing that a shift to a single global data model meant reworking established local processes. “We’re changing from several different providers to a new way of working, so we needed a change management process that took our customers, users, and stakeholders through that journey,” says a data leader. Nearly two years after implementation, the company uses this framework to ensure ongoing adoption and success.
Other companies took a different approach, allowing local markets to make the final call on their data provider. The rationale is that data quality and completeness are primarily local responsibilities, with local teams being the best equipped to make these judgments. For instance, understanding and managing data in a market like Korea requires local expertise.
A phased rollout strategy is essential for managing complexity and ensuring smooth adoption.
Several leading companies have prioritized their larger, high-impact markets first. This approach provides a structured way to scale the initiative. It allows organizations to refine processes and address challenges in these initial markets before expanding to smaller ones. Furthermore, it enables smaller markets to observe and learn from the experiences of the larger markets, adapting their own approaches accordingly.
Implementing global data harmonization isn’t just about technology — it requires the proper organizational structure and processes to support it. Companies have had to rethink how they handle territory alignments, reporting structures, and project management methodologies to ensure a smooth transition.
For instance, one company decoupled its sales reporting from territory alignments, a critical shift that allowed them to move away from rigid structures tied to their previous data provider. Traditionally, many biopharmas base their CRM territory assignments and sales reporting on the same “brick-based” model, which creates dependencies that hinder agility. By separating these elements, the company gained more flexibility in managing sales data without disrupting field operations. Many in the industry note that sales reporting and CRM territories are often similarly aligned and decoupling them is key to improving global data models.
Similarly, another company adjusted its approach by transitioning brick-based to zip-code-based territory assignments, ensuring a more seamless integration with its new data model. Both companies adopted an agile methodology, rolling out updates in structured sprints to continuously refine their data processes. “Changing a customer master data provider touches the heart of CRM. If sales reps can’t find customer records, they can’t do their jobs. That’s why structured processes and methodologies are crucial for success,” according to one data leader.
By defining clear structures and workflows, organizations can prevent disruptions, improve adaptability, and ensure the long-term scalability of their data harmonization efforts.
Global data harmonization is not a one-time project — it’s an ongoing process. New regulations, business shifts, and operational needs will continuously evolve, requiring companies to adapt their data models and practices.
For example, a new VAT and tax ID requirement was introduced in Italy after one company had already implemented its global data model. This necessitated enriching their data ecosystem to accommodate the new regulation.
Continuous collaboration with regional teams, data stewards, and regulatory bodies ensures that the data model stays relevant and compliant over time.
As biopharmas expand globally, data complexity will only grow, but it doesn’t have to become a barrier. Leading companies demonstrate that organizations can future-proof their data systems to remain adaptable, efficient, and AI-ready with the proper governance, structured processes, and an agile mindset.
By investing in harmonized, scalable data models, companies can reduce inefficiencies, streamline compliance, and enable real-time insights that drive more intelligent decision-making. More importantly, building a strong data foundation today ensures that as new technologies emerge, organizations are ready to leverage them, rather than being held back by outdated systems.
The key takeaway? Harmonization isn’t just about fixing today’s challenges, it’s about preparing for tomorrow’s opportunities.
Learn about Bayer AG’s initiative to harmonize customer reference data globally, enabling a true customer 360-degree view and consistent standards across over 40 markets.
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