In today’s rapidly evolving pharmaceutical landscape, creating an effective commercial data strategy is critical for achieving success. Industry leaders at the Access Insights Conference 2024 explored how data governance, contracting, and the application of artificial intelligenceI and machine learning (AI/ML) are transforming the way organizations gather, process, and leverage data to drive commercial excellence. Their insights shed light on key challenges and actionable approaches for building robust, future-ready data frameworks.

Aligning Data Strategies with Business Needs

An effective data strategy starts with alignment to core business objectives. Rather than letting data assets dictate the approach, organizations should design strategies that address critical commercial priorities and brand goals. This ensures that data acquisition and analysis are intentional, leading to meaningful and actionable decisions instead of superficial insights.

Governance is the foundation of this alignment. Establishing structured frameworks, such as data enablement committees, helps organizations evaluate the necessity of datasets, determine how they integrate into broader systems, and assess their overall value. Clear governance mitigates data gaps that could hinder effective decision-making.

Contracting is another critical element of data strategy. By setting clear requirements during negotiations—such as specifying mandatory fields in datasets—companies can prevent costly errors. This precision ensures the data collected is relevant, aligned with business goals, and operationally efficient.

Maximizing Data Utility with AI and Machine Learning

The integration of AI/ML into commercial data strategies offers transformative potential, but organizations must carefully choose their approach: build, buy, or partner. Each path has unique advantages and challenges. Building internal capabilities requires substantial investment in talent and infrastructure, while partnerships allow companies to benefit from external expertise and established solutions. Regardless of the choice, the goal remains the same—developing scalable, effective solutions that align with business objectives and enhance patient outcomes.

Adopting AI/ML also requires a cultural shift across the organization. Companies must evolve from being merely data-informed to becoming fully data-driven, where decisions are guided and validated by advanced analytics. Achieving this shift involves educating stakeholders—from sales teams to senior leadership—about the potential benefits and real-world applications of AI/ML technologies.

Data silos present a significant barrier to effective AI/ML integration. Breaking down these silos and fostering cross-functional collaboration enables organizations to unlock comprehensive insights. Centralizing data sources into unified platforms, such as master data models, ensures consistency and opens the door to more effective machine learning applications.

Looking Ahead: The Future of Commercial Data Strategies

As the commercial data landscape grows more complex, organizations must refine their ability to connect diverse datasets—spanning patient journeys, provider engagement metrics, and beyond. In the coming years, the ability to integrate diagnostic, treatment, and behavioral data will be key to generating richer insights that improve patient care and optimize business outcomes.

However, this increased complexity also brings greater scrutiny of data spending and utilization. Organizations will need to prioritize datasets with clear value and explore innovative approaches like synthetic data to address gaps in traditional sources.

The rise of specialty products, which account for a growing share of new drug approvals, underscores the importance of tailoring data strategies to unique therapeutic areas. Balancing personalized patient engagement with operational efficiencies is essential for success in this space.

Regulatory considerations will also play a central role. Compliance with data privacy laws and ethical AI standards requires proactive collaboration between data, legal, and compliance teams. By embedding these considerations into their processes, organizations can mitigate risks while fostering innovation.

The pharmaceutical industry is undergoing a profound transformation driven by advancements in data governance, AI/ML, and contracting strategies. By aligning data strategies with business needs, breaking down silos, and fostering cross-functional collaboration, companies can navigate this complex landscape effectively and build a future that is both data-driven and patient-centered.