For more than a decade, we have framed real-world evidence (RWE) challenges as a data problem. Not enough access. Not enough scale. Not enough linkage. Not enough quality. However, this narrative has become increasingly difficult to sustain. Across major markets, data availability has expanded, platforms have matured, and analytical methods have become more standardized and sophisticated. What has not kept pace is how organizations are structured to use this evidence coherently.
In many companies, data maturity has advanced faster than organizational maturity. This gap is not marginal, and it is not accidental. It is structural.
Most large pharma organizations now have more real-world data (RWD) than they can meaningfully absorb. Multiple business units contract overlapping datasets. Central teams negotiate enterprise agreements that local or therapeutic teams barely use. Safety, Medical, HEOR, R&D, and Commercial all generate RWE in parallel, often with different objectives, timelines, and success metrics. The result is not a lack of potential evidence generation tools, but an excess of disconnected activity.
Industry analyses have been pointing to this for years. The IQVIA Institute has repeatedly documented inefficiencies in evidence generation driven by fragmented operating models and uneven capabilities across regions and functions (1, 2). McKinsey has been even more explicit, arguing that the primary barrier to translating RWE into impact is not analytics or technology, but organizational design (3, 4). Regulatory bodies observe the same pattern from a different vantage point. Publications from the EMA Big Data Steering Group consistently highlight internal governance immaturity within companies as a bottleneck, even when external data infrastructures and regulatory frameworks are advancing (5, 6).
Despite this, the default response inside organizations remains technological. New platforms are procured. New vendors are onboarded. New dashboards are built. Each intervention promises integration, visibility, and alignment; however, the underlying incentives and decision rights remain unchanged. Data access is centralized, but accountability is not. Strategy is global, but execution is local. Budgets are fragmented, but expectations are enterprise-wide.
Compounding this structural fragility is the pace of organizational turnover. RWE, data science, and evidence strategy functions are experiencing some of the fastest turnover rates in the enterprise. At the same time, business needs are shifting rapidly, new indications, compressed development timelines, increased regulatory scrutiny, without a corresponding investment in rebuilding competencies or preserving institutional memory. Decisions about why certain data sources were selected, how governance trade-offs were made, or what “good” looks like in a given context often disappear within a couple of years. RWE, which should be cumulative by nature, becomes episodic. Governance is then asked to compensate for capability gaps and continuity loss it was never designed to solve.
What is often missing is an explicit acknowledgment that RWE is not just a scientific or analytical capability, but an organizational one. It requires clarity on who decides what data is strategic versus tactical, who pays for it, who maintains it, and who is accountable for reuse. It requires alignment between global ambition and local reality, not through mandates, but through incentives and practical workflows. Most importantly, it requires treating governance not as a control mechanism, but as an enabling structure that survives personnel change. Without a clearly mandated data governance or data strategy function to centralize access, steward enterprise data assets, and facilitate reuse, governance remains dependent on individuals rather than embedded in the organization. Investing in a dedicated data governance department therefore becomes essential.
This is where the conversation becomes uncomfortable. Organizational design is harder to fix than data access. It touches power, budgets, visibility, and legacy ways of working. It forces difficult questions about duplication, ownership, and trade-offs. It exposes the fact that many RWE activities persist not because they are strategically essential, but because no mechanism exists to challenge or coordinate them.
Until this is addressed, the industry will continue to overestimate the role of data scarcity and underestimate the cost of internal fragmentation and turnover. The next phase of RWE maturity will not be defined by better datasets or more advanced analytics. It will be defined by whether organizations are willing to redesign themselves to use what they already have: consistently, transparently, and sustainably.
In that sense, the real transformation required in RWE is not digital. It is structural.
References
- IQVIA Institute for Human Data Science. Global trends in R&D 2023: Progress in biopharma innovation. Durham (NC): IQVIA Institute; 2023.
- IQVIA Institute for Human Data Science. The role of real-world evidence in advancing patient care. Durham (NC): IQVIA Institute; 2017.
- McKinsey & Company. Real-world evidence: From data to impact. McKinsey & Company; 2021.
- McKinsey & Company. Scaling real-world evidence in pharma: Why operating models matter. McKinsey & Company; 2022.
- European Medicines Agency. Big Data Steering Group workplan 2020–2022. Amsterdam: EMA; 2020.
- European Medicines Agency. Big Data Steering Group progress report. Amsterdam: EMA; 2023.
