Real-world data (RWD) has become the pharmaceutical industry’s most promising yet challenging frontier. While companies have long recognized its potential to complement clinical trials and deepen patient outcome insights, many still struggle to fully realize its value.
We’ve seen firsthand, from early discovery to market authorization to post-launch evidence generation, that extracting meaningful value from RWD requires more than just data acquisition. It demands a consistent, scalable, and repeatable approach to data quality, as well as a strategic roadmap that moves an organization from minimal use of RWD to a position of true leadership. This journey isn’t simple, but for those committed to navigating it, the competitive advantages are substantial. And, importantly, RWD holds the potential to help bring lifesaving and life-changing medications to market faster and safer than ever before.
From Data Collection to Maturity
Pharma’s growing reliance on RWD has encouraged an influx of partnerships with data aggregators, medical institutions, and digital health platforms. Yet, not all companies are at the same level of maturity in using that data effectively. Let’s take a look at the various levels where organizations tend to live:
Level 1: Reactive
At the lowest level, companies capture RWD simply because it’s increasingly expected. Typically, data sources might include electronic health records, registries, or claims data. However, these companies often lack a robust infrastructure to integrate and interpret this information, leaving key insights buried in disparate systems.
Level 2: Opportunistic
The next step is using RWD for specific, often one-off projects such as an outcomes study to gain a competitive edge or respond to a regulatory request. These opportunistic efforts deliver limited insights: enough to demonstrate feasibility, but not enough to transform how a company makes decisions across its portfolio.
Level 3: Strategic
Moving further up the maturity curve, some organizations begin to build a strategic framework. They put in place dedicated teams, standardized processes, and clear goals. RWD is more effectively integrated with clinical data, providing a stronger evidence base for product development, market access, and patient support programs. However, even at this level, challenges around data quality, standardization, and interoperability persist.
Level 4: Transformational
At the highest level, RWD becomes integral to every stage of the drug development and commercialization process. Leading organizations establish a “single source of truth” for data, with automated pipelines that continuously ingest, clean, and analyze information. They also set up robust feedback loops with data suppliers, ensuring that inaccuracies and gaps are addressed at the source. This end-to-end approach enables real-time insights that can refine trial designs, optimize supply chains, and improve patient engagement.
Key Dimensions of RWD Maturity
So, what are the levers that allow companies to move further up the maturity curve? The first is data sourcing. A major driver in determining an organization’s RWD maturity is how it sources its data. Within data sourcing, there are three levels of sophistication:
- Basic (Ad Hoc): Data sourcing is done on an as-needed basis with no formal or consistent evaluation process. Organizations may simply buy “off-the-shelf” data products without aligning them to broader goals or scrutinizing their quality or relevance.
- Intermediate (Structured Screening): Some or most functions within the company conduct a structured landscape screening to find data that meets specific needs. However, this process is not consistently adopted across all business units. Evaluation methods vary, and certain projects may revert to ad-hoc purchasing when time is short.
- Advanced (Enterprise-Wide Evaluation): The most mature organizations use a refined, well-established process for landscape screening and data evaluation, embraced across the entire enterprise. These companies also invest in long-term partnerships to co-create data solutions, rather than relying on transactional, one-off data buys.
The second dimension to influence RWD maturity is data management and enablement. Closely tied to sourcing is how the organization manages and enables data usage across teams. From building libraries to ensuring enterprise-wide adoption of tools, these practices significantly influence the consistency and reliability of RWD insights. Here’s how the three levels shake out:
- Basic (Minimal Enablement): No formal data library exists, or it’s limited in scope. There are no recommended tools for data quality checks, and individual functions may use whatever tools they prefer. Data quality, if addressed at all, is checked on an ad-hoc basis.
- Intermediate (Partial Integration): A data library exists with some support from IT, but adoption varies by function. Data quality processes are in place for most teams, though these processes may happen in silos and lack overarching governance.
- Advanced (Enterprise-Wide Adoption): A robust data library is developed in close partnership with IT and widely adopted throughout the organization. Industry-grade tools are used consistently for data ingestion, cleaning, analytics, and governance. Comprehensive data quality processes are established at the enterprise level, ensuring standards are upheld and continuously improved.
By deliberately progressing across both these dimensions, data sourcing and data management, pharmaceutical companies can build a more resilient, future-ready data ecosystem.
The Data Quality Imperative
Regardless of where a company stands in terms of data sourcing and enablement, one principle remains paramount: the power of real-world data hinges on its quality. Inconsistent or incomplete information can derail even the most promising RWD initiatives.
As such, advanced pharma leaders invest in scalable systems and processes rather than relying on ad-hoc methods to clean and validate datasets. This includes automated checks for duplicate records, standardized coding for diagnoses and procedures, and machine learning algorithms that flag anomalies before they propagate. True consistency comes from a governance structure that ensures shared standards for data sourcing, ingestion, and analysis. Aligning vendors, internal teams, and stakeholders around a common objective:generating high-integrity data for decision-making helps maintain regulatory readiness and fosters trust in the outcomes.
But data quality efforts shouldn’t end at the borders of the organization. Pharma leaders must build continuous feedback loops with RWD suppliers, alerting them to issues like missing fields, inconsistent coding, or delayed updates. Doing so not only improves the current dataset but also elevates the quality of future data, creating a virtuous cycle of improvement.
Leadership Requires Organizational and Cultural Shifts
Becoming a leader in RWD is as much about culture and organizational buy-in as it is about technology. Executives who prioritize RWD create cross-functional teams that blend clinical, regulatory, IT, and commercial expertise. They invest in ongoing education, helping the workforce develop the analytic capabilities and critical thinking needed to turn RWD into actionable insights.
In our experience, effective leaders set a bold vision for how RWD will inform long-term decisions and work backward to define the data infrastructure, analytics tools, and organizational framework needed to get there. They craft policies to protect patient privacy and maintain high ethical standards ensuring that as RWD grows more prevalent, it also remains trustworthy.
Practical Steps to Advance Maturity
So what does this look like in practice? Here are five steps to help advance RWD maturity:
- Conduct a Candid Self-Assessment: Evaluate where your organization stands in terms of data sourcing and data management. Do you have a consistent evaluation process? Is there an enterprise-wide data library in place?
- Define a Clear Data Quality Strategy: Commit resources to scalable automation, standardization, and governance. Assign ownership for data quality both internally and with external partners.
- Invest in Feedback Mechanisms: Establish formal channels to communicate data issues to suppliers. Work collaboratively to resolve them, creating a long-term feedback loop that improves future data.
- Align Cross-Functional Teams: RWD touches many parts of the organization. Engage leaders from clinical development, regulatory, commercial, IT, and other areas to champion a unified approach.
- Aim for Repeatability and Sustainability: One-off projects can yield short-term wins, but genuine transformation arises from consistent application of best practices across portfolios and markets.
Charting the Path Forward
Pharmaceutical companies that fail to capitalize on RWD risk falling behind in a marketplace that increasingly demands faster evidence generation, patient-centric insights, and data-driven decision-making. The good news is that the journey up the RWD maturity curve, while challenging, can be navigated with a clear vision, robust data quality frameworks, and strong partnerships.
By committing to a consistent, scalable, and repeatable approach, pharma organizations can unlock RWD’s full potential and deliver more value to patients, providers, and shareholders. It’s not just about following the data, it’s about evolving with it.
Daniel Lane the Head of RWD Strategy & Operations at Bristol Myers Squibb and Viraj Narayanan is CEO of Cornerstone AI.
