Definition

Data governance defines how an organization manages data as a controlled quality asset. In life sciences, governance connects data integrity expectations to day-to-day ownership, system controls, procedures, training, review, and continuous improvement.

A practical governance program addresses who owns data, where data are created and stored, which records are GxP, how data are protected, how records are reviewed, and how data remain complete and available throughout their lifecycle.

Why Is Data Governance Important?

Data governance helps ensure that regulated data provide documented evidence that products were developed and manufactured according to approved procedures.

• Establishes clear accountability for regulated data and records.
• Supports ALCOA+ expectations across the data lifecycle.
• Improves consistency across systems, departments, sites, and suppliers.
• Helps identify and remediate data integrity risks before inspection findings occur.
• Connects technology controls, quality procedures, and human workflows.

What Does Data Governance Include?

A data-governance program defines the following ownership, policy, control, monitoring, and oversight elements across the regulated data lifecycle.

Ownership and accountability

Governance should define data owners, system owners, process owners, quality oversight, and responsibilities for review and retention.

Policies and standards

Procedures should address data creation, processing, review, correction, reporting, archival, retention, and disposal.

Controls and monitoring

Controls may include access management, audit trails, backup and restore, metadata protection, review workflows, training, and periodic data integrity checks.

Metrics and oversight

Governance should include mechanisms to monitor issues, trends, deviations, remediation progress, and program effectiveness.

Common Data Governance Challenges

• Data ownership is unclear between IT, quality, operations, laboratories, and vendors.
• Data are distributed across multiple independent systems with inconsistent controls.
• Legacy systems lack controls or documentation needed for current governance expectations.
• Metadata, audit trails, and derived reports are not governed consistently.
• Data integrity risk assessments are performed once and not maintained.
• Governance decisions are not linked to training, system controls, or remediation.

Best Practices for Data Governance

• Create a practical data inventory that identifies GxP data, systems, owners, and retention needs.
• Align governance with ALCOA+ principles, Part 11 expectations, and site quality procedures.
• Assign accountable roles for data governance, including business owners, system owners, data stewards, and Quality oversight.
• Use metrics and periodic review to monitor governance effectiveness.
• Adopt risk-based governance aligned with applicable Computer Software Assurance principles.
• Include suppliers and cloud systems in data governance expectations.

How Mangan Biopharm Supports Data Governance

Mangan Biopharm supports data governance framework development, data integrity assessments, data lifecycle mapping, CSV alignment, remediation planning, and validation data management for regulated organizations.