Health data governance: From compliance to enterprise value

By Michael Baur and Morris Hosseini

Health data is becoming one of the most valuable strategic assets in biopharma and medtech. While pharmaceutical companies have traditionally competed on molecules, clinical development, regulatory execution, and commercial reach, tomorrow's leaders will increasingly compete on their ability to generate, govern, protect, reuse, and explain high-quality health data across research, development, medical, market access, and commercial activities. In this environment, data governance is no longer a narrow compliance discipline. It is the strategic infrastructure that turns sensitive health data from a potential liability into a trusted, reusable, financeable, and partnerable asset.

This article was co-authored by Kaouthar Lbiati, Independent Board Director at Theralase Technologies.

The value of health data is realized only when organizations can govern it responsibly, protect it effectively, and reuse it transparently. This is particularly relevant in the AI era, where multimodal datasets, analytics pipelines, foundation models, platform ecosystems, and external collaborations are becoming core sources of competitive advantage. Healthcare systems are expected to benefit overall. For example, in Europe, the European Parliament estimates that improved access to, exchange, and use of health data could save the EU nearly 11 billion euros over ten years.

Six focus areas are critical to translating trusted data governance into key differentiators and business impact: value, access, transnationality, AI, trust, and strategy.

1. Value: Turn governed health data into transaction-ready enterprise value

In biopharma, value increasingly resides not only in molecules, patents, and clinical trial results but also in an organization's ability to generate, govern, reuse, and explain its health data. Robust privacy and data governance frameworks codify institutional know-how into auditable, transferable systems. By integrating data rights, lineage, quality standards, workflows, controls, and analytics pipelines, companies can transform operational expertise into scalable governance capabilities that strengthen transaction readiness and long-term shareholder value.

This is directly relevant for M&A, licensing, and strategic partnering. Clear data rights, robust lineage, auditable controls, and transparent governance simplify due diligence, support post-transaction integration, and help protect value. Mature data governance also signals operational scalability and execution capability to investors, demonstrating that an organization can reliably manage complex, sensitive, and high-value data assets at enterprise scale.

2. Access: Strengthen evidence credibility to enable and accelerate market entry

Data governance is also becoming a differentiator for market access. Payers, providers, regulators, and Health Technology Assessment bodies such as NICE (UK), IQWiG (Germany) and HAS (France) increasingly assess not only the scientific validity of evidence but also whether the underlying data has been collected, governed, shared, and reused transparently, ethically, and lawfully. Data governance maturity therefore strengthens trust in the evidence base, particularly for real-world evidence (RWE), patient registries, outcomes-based agreements, post-marketing evidence generation, and AI-enabled analytics.

By standardizing data quality, governance, and reuse across markets and indications, organizations can reduce the cost and complexity of evidence generation while increasing confidence among regulators, payers, and healthcare partners. The result is more credible evidence, more efficient reimbursement discussions, and a stronger foundation for patient access.

3. Transnationality: Use trusted data flows to compete across jurisdictions

Multinational healthcare companies operate across jurisdictions with diverging regulatory approaches, particularly between Europe and the United States. The European Health Data Space provides a relatively structured framework for primary and secondary health data use, including research, innovation, policy, and regulatory purposes. In the US, by contrast, the system remains more fragmented: The Health Insurance Portability and Accountability Act (HIPAA) is central, but many digital health, connected device, consumer diagnostics, and health-adjacent data flows sit outside traditional HIPAA-covered entities.

At the same time, biological and health data are becoming strategic assets linked to AI competitiveness, industrial policy, national security, and health system sovereignty. Cross-border data flows should therefore be treated not only as a compliance topic but as a strategic capability that influences where clinical research is conducted, where AI models are trained, and where companies invest.

Enterprise-wide governance enables companies to compete across jurisdictions by combining data lineage, purpose limitation, responsible secondary use, auditable controls, and transparent communication. Organizations that can demonstrate trusted data flows will be better positioned to collaborate with health systems, research institutions, technology partners, and regulators while also turning data sovereignty from a constraint into a source of competitive advantage.

4. AI-enabled technology: Follow this innovation roadmap

For AI innovators, three rules are critical. First, embed governance early in the AI development lifecycle rather than adding it later as a remediation exercise. This helps accelerate innovation, reduce delays, and avoid costly redesigns. Second, build AI on governed data. Genomic databases, electronic health records, medical imaging repositories, RWE, and other multimodal datasets are essential for developing next-generation AI solutions, but their value depends on provenance, consent, bias controls, validation, explainability, continuous monitoring, and accountability.

Third, demonstrate responsible AI governance to build confidence among regulators, clinicians, payers, and partners. Companies that can clearly explain how AI models are trained, validated, governed, and monitored are more likely to gain regulatory acceptance, strengthen clinician and payer trust, improve partnership prospects, and support market adoption while at the same time mitigating legal, ethical, regulatory, and reputational risks.

5. Trust: Unlock access to high-quality datasets, partnerships, and innovation

Trust is the currency of data collaboration. Organizations trusted to steward sensitive health data responsibly gain broader access to high-value datasets across studies, care settings, platforms, and geographies. This trust depends on transparent communication about the purpose of data use, the safeguards in place, secondary use policies, and the expected benefits for patients, providers, health systems, and society.

Strong governance makes responsible data sharing easier across partners and jurisdictions. Reusable, well-governed datasets strengthen RWE, AI development, market access, patient engagement, and organizational resilience. In this sense, trust is not a soft reputational concept; it is a strategic asset that expands the permission space for innovation and collaboration.

6. Strategy: Elevate data governance from compliance to a strategic capability

As data governance becomes central to enterprise value, boards and executive teams need to expand oversight beyond compliance. Governance now sits at the intersection of cyber resilience, AI governance, regulatory risk, innovation strategy, partnership readiness, and valuation. Effective oversight requires leaders to understand how responsible data governance supports long-term value creation, protects sensitive assets, and enables scalable collaboration across the life sciences ecosystem.

For CEOs and boards, the implication is clear: Data governance should be treated as a corporate capability, not as a back-office control function. Companies that link responsible governance to strategy, capital allocation, product development, market access, and external partnering will be better positioned to create enterprise value and compete in an AI-enabled healthcare environment.

In the AI economy, data governance is a key element of the strategic infrastructure. The health organizations that earn and sustain trust through responsible data governance will be best positioned to lead the next generation of AI-enabled healthcare innovation, create long-term enterprise value, and strengthen their country's competitiveness in the global AI race.

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