GOVERNANCE & ACCOUNTABILITY

Clinical Meaning & Intended Purpose

What do the data represent, and why are they needed?

Data Stewardship & Access Control

Who is responsible for the data, and who can use them?

Quality, Traceability & Interoperability

Can the data be understood, verified and connected?

Trusted & Responsible Reuse

Evidence, research and informed decisions

HEALTH DATA GOVERNANCE

Health data generate value when they can be trusted, understood and responsibly reused.

Connecting clinical meaning, data quality and accountability.

The value of health data depends on their clinical meaning, quality, provenance and fitness for purpose. Understanding how data are generated, transformed and interpreted is essential to establishing the conditions for their trustworthy use across different systems, organisations and research contexts.

Health Data Governance connects responsibilities, policies and operational controls with data quality, traceability, interoperability and responsible reuse.

My approach builds on hands-on experience in clinical data management, real-world evidence and healthcare data integration, bringing a clinical and operational perspective to broader data governance challenges.

THE GOVERNANCE CHALLENGE

Data can be accurate and still be difficult to trust, connect and reuse.

Healthcare data are generated across clinical care, research, registries and digital health technologies. Each environment has its own objectives, workflows, definitions and constraints. [1]

When data move beyond their original context, differences in meaning, documentation, quality and responsibilities can compromise their interpretation and fitness for purpose. [1,2]

Fragmented data sources

Clinical information is distributed across systems and organisations, making integration, continuity and cross-source analysis difficult.

Unclear responsibilities

Data stewardship, access permissions and decision-making authority may be insufficiently defined across stakeholders. [1,6]

Inconsistent meaning

Differences in terminology, definitions, units and clinical context can undermine comparability, even when individual datasets are accurate.

Limited reuse readiness

Incomplete metadata, uncertain provenance and restrictions on data use can limit the suitability of existing datasets for new research purposes. [1,6]

THE FOUNDATIONS OF HEALTH DATA GOVERNANCE

Turning governance principles into operational capabilities.

Effective health data governance requires coordinated decisions, clearly assigned responsibilities and practical controls across the data lifecycle.

Six interconnected capabilities help ensure that health data remain understandable, reliable, protected and suitable for their intended uses. [1,5]

Governance & Accountability

Define decision-making authority, data stewardship responsibilities and policies that guide how health data are managed and used. [1]

Interoperability & Standardisation

Align data structures, terminology and exchange processes to support consistent interpretation across systems and organisations. [1]

Data Quality & Fitness for Purpose

Establish quality requirements and controls based on clinical meaning, intended use and the consequences of unreliable data. [1, 2, 5]

Privacy, Security & Access

Apply appropriate access controls, safeguards and risk assessments to protect health data throughout their lifecycle. [1, 6]

Metadata & Traceability

Document definitions, provenance, transformations and data lineage so information remains interpretable and verifiable. [1, 5]

Responsible Data Reuse

Assess legal conditions, data suitability, permitted purposes and safeguards before enabling secondary use for research and innovation. [1, 6,7]

GOVERNANCE ACROSS THE DATA LIFECYCLE

Governance is continuous. Every data transition matters.

Health data evolve as they are collected, validated, transformed, integrated and reused. Each transition can affect their meaning, quality, traceability and suitability for future purposes.

Effective governance establishes responsibilities and controls throughout this lifecycle, ensuring that decisions remain documented, risks are addressed, and data can be interpreted within their clinical and operational context. [1, 5]

Define

Purpose & rules

Acquire

Sources & context

Validate

Quality & integrity

Maintain

Changes & lineage

Reuse

Suitability & access

Continuous governance: accountability · quality monitoring · security · documentation

A practical lesson: detect issues while they can still be resolved.

In clinical research, early data validation and continuous cleaning help identify systematic errors, missing information and inconsistencies while study sites are still active.

The same principle extends beyond clinical trials: governance is more effective when quality issues, unclear definitions and data limitations are addressed close to their origin, rather than discovered during downstream integration or reuse. [5]

Retention, archiving and disposal

Throughout the lifecycle, retention periods, archiving requirements and secure disposal must be governed according to applicable legal obligations, documented policies and the purposes for which data are processed. [1, 6]

Adapted from my teaching materials on Clinical Data Validation (2026).

MY PERSPECTIVE

From clinical understanding to data governance decisions.

My experience across clinical data management, real-world evidence and healthcare data integration has shaped a practical approach to health data governance.

I start by understanding the clinical context in which data originate, translating requirements into operational controls and considering how information may need to be interpreted, integrated, and reused.

01

Start from clinical meaning

Understand the clinical question, data origin and operational context before defining requirements or selecting technical solutions.

02

Translate requirements into controls

Connect clinical, operational, and regulatory needs to documented specifications, responsibilities, and verifiable quality checks.

03

Design for downstream use

Anticipate integration, interpretation and potential reuse by considering data definitions, provenance and limitations from the outset.

IN PRACTICE · HOSPITAL DATA INTEGRATION

Making heterogeneous clinical data usable across systems.

In a hospital data integration project, information from different clinical databases had to be brought into a coherent structure for cohort identification and analysis.

The challenge extended beyond technical integration: understanding the original clinical workflows, reconciling data definitions and identifying inconsistencies were essential to preserving the meaning of the information.

This experience reinforced my approach to governance: reliable integration starts with clinical understanding and requires documented decisions about data interpretation, transformation and quality.

THE REGULATORY LANDSCAPE

From regulatory requirements to practical governance.

Health data governance operates within a complex regulatory environment. Different frameworks address clinical research, data protection, secondary use and the trustworthiness of data and processes.

The practical challenge is translating these requirements into coherent responsibilities, documented decisions and controls that work across the data lifecycle.

Clinical research

ICH E6(R3) · ICH E8(R1) · EU 536/2014

Data integrity, quality by design, oversight and reliable clinical evidence.

Health data access & reuse

EHDS · Regulation (EU) 2025/327

A European framework for health data access, interoperability and secondary use.

Personal Data Protection

GDPR · Regulation (EU) 2016/679

Lawful processing, accountability, data minimisation and appropriate safeguards.

Operational practices

DAMA-DMBOK · SCDM GCDMP

Data stewardship, metadata management, data quality and documented processes.

FROM GOVERNANCE TO VALUE

Governance is not the final objective. Trustworthy data use is.

Health data governance creates the conditions for clinical information to be interpreted, connected and responsibly reused. Its value emerges when those conditions enable reliable research, meaningful evidence generation and informed decisions.

01 · Trust

Data that can be understood, verified and interpreted.

02 · Connect

Information that moves across systems without losing its meaning.

03 · Reuse

Data that supports research and evidence generation under appropriate safeguards.

The goal is not simply to govern data. It is to make their responsible use possible.

[1] DAMA International. DAMA-DMBOK: Data Management Body of Knowledge. 2nd ed. Technics Publications; 2017.

[2] International Council for Harmonisation (ICH). E8(R1): General Considerations for Clinical Studies. 2021.

[3] International Council for Harmonisation (ICH). E6(R3): Guideline for Good Clinical Practice. Principles and Annex 1, 2025.

[4] European Parliament and Council. Regulation (EU) No 536/2014 on clinical trials on medicinal products for human use. Article 56.

[6] Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), 2016

[7] Regulation (EU) 2025/327 of the European Parliament and of the Council of 11 February 2025 on the European Health Data Space and amending Directive 2011/24/EU and Regulation (EU) 2024/2847