Federated Governance Recommendation

As Life Sciences companies face growing data complexity and faster decision cycles, traditional centralised models are struggling to keep pace. To remain competitive and compliant, organisations must adopt scalable, modern data strategies that balance innovation with regulatory discipline. Among these, the data mesh paradigm has emerged as a compelling solution. One such strategy gaining traction is data mesh—a socio-technical architecture that enables domain-driven ownership and trusted access to data at scale. This model supports not only operational agility but also the reliable foundations required for AI and advanced analytics.

At MIGx, we’ve seen how data mesh, particularly its federated governance model, can help Life Sciences companies move away from centralised bottlenecks without compromising compliance.

Today, many Life Sciences organisations are taking concrete steps towards implementing data mesh. However, success depends on more than architecture. Federated governance is often the most complex and critical component of a data mesh initiative. It shapes how decisions are made, how risk is managed, and how autonomy and alignment are balanced across teams.

The Four Principles of Data Mesh

At its core, data mesh is built on four key principles:

• Decentralised Ownership: Data is owned and managed by the business domains that generate it, ensuring deep context and accountability.
• Data as a Product: Data is no longer a by-product of systems, but a product with consumers, service levels, and tangible value.
• Federated Governance: Governance is shared across domains, balancing autonomy with compliance and consistency.
• Self-Service Infrastructure: Platforms and tooling support domain teams in creating and consuming data products in a standardised way.

These principles promise increased agility, scalability, and data quality—but implementing them in a Life Sciences environment requires additional layers of nuance.

In a previous post, we explored how federated governance is often the most challenging aspect of data mesh implementation. At MIGx, we define governance across seven practical dimensions. This framework helps organisations manage decentralisation in a structured, risk-aware way while providing the appropriate level of documentation for a well-regulated industry:

• Team structure – Clear roles, responsibilities, and collaboration models
• Standards – Common practices for modelling, documentation, and operations
• Metadata requirements – Standardised tagging, lineage, and discoverability
• Compatibility – Ensuring data interoperability across products and platforms
• Meeting structure – Defined cadence for decision-making and escalation
• Security policy – Role-based access, encryption, and compliance standards
• Data lineage – Visibility into the origin and transformation of data

While each organisation must tailor governance to its unique context, Life Sciences introduces industry-specific constraints that demand careful consideration.

Key Considerations for Life Sciences

1. Country-Specific Maturity and Ownership

Life Sciences data often reflects national regulations, such as transparency laws or reimbursement systems. This reality means many data domains will need to be owned locally. However, local ownership requires local capability.

We recommend establishing a tiered model of country maturity, where governance responsibilities are assigned based on each country’s operational and regulatory readiness. This ensures the governance framework remains functional while empowering mature markets to take greater ownership.

2. Risk Appetite and Central Oversight

Not all data can or should be decentralised. Some elements—particularly those related to pharmacovigilance, such as adverse events—carry high risk. These data types must be centrally owned to ensure consistent interpretation and regulatory reporting across the enterprise.

This doesn’t imply the central data team must manage the data directly, but ownership should reside with a specialised central function, safeguarding against misclassification (e.g. confusing a product complaint with a medical enquiry or adverse event).

3. Data Sensitivity and Security

The more sensitive the data, the more stringent its governance must be. Accuracy becomes non-negotiable, and access must be tightly controlled. In these cases, the domain owner should be as close to the point of data creation as possible—ideally embedded in operational processes.

The security policy must support fine-grained access controls, robust audit trails, and clear accountability—especially for personal health information, clinical trial data, and patient-reported outcomes.

We believe that applying the MIGx governance framework—adapted for local maturity, risk level, and data sensitivity—offers a structured path to implement data mesh responsibly and effectively in Life Sciences.

If your organisation is exploring a data mesh approach, we recommend starting with a clear assessment of:

• Which data domains can be decentralised
• What local capabilities exist across countries
• Which data elements require central stewardship
• How your security policy can support dynamic, risk-adjusted access

At MIGx, we specialise in helping Life Sciences companies operationalise modern data strategies like data mesh—without compromising on compliance, transparency, or scientific integrity.