Go to Playbook Main Page
Back to Design
Data Governance in MDM#
Core Data Governance Principles#
Every MDM solution created must be designed to adhere to the Core Data Governance Principles . These principles apply to all the stages of MDM solution from inception to decommission.
Data Ownership and Accountability#
- Assign clear Data Owners for each master data domain (e.g., Product, Customer, Site).
- Assign Data Maintainers who manage day-to-day data quality and coordinate changes.
- Ensure business accountability for the quality and lifecycle of master data.
Data Quality Management#
- Define data quality rules (completeness, accuracy, validity, uniqueness, timeliness).
- Establish metrics and dashboards to monitor data health.
- Implement workflows to detect, report, and remediate data issues.
- Include root-cause analysis in the remediation process.
Data Policies and Standards#
- Define and enforce naming conventions, format rules, and code lists.
- Create policies for golden record survivorship, versioning, and handling duplicates.
- Apply metadata standards to support traceability and reuse.
Lifecycle Management#
-
Govern master data across its full lifecycle:
-
Creation
- Enrichment
- Validation
- Usage
- Archival or Deletion
- Use workflows to manage lifecycle transitions and approvals.
Access Control and Security#
- Use role-based access control (RBAC) to limit who can view or modify data.
- Protect sensitive attributes (e.g., personal info, financial data).
- Audit all changes and access through trace logs.
Change Control and Governance Workflows#
-
Govern changes to attributes, mappings, and hierarchies through:
-
Change Request Forms
- Impact Assessments
- Approvals from Governance Council
- Maintain a data dictionary and mapping documents under version control.
Compliance and Regulatory Alignment#
- Ensure that MDM complies with internal policies and external regulations (e.g., GDPR, IDMP, HIPAA).
- Maintain data retention and consent tracking for regulated domains.
Reference Data Management#
- Govern code sets and reference values centrally (e.g., ISO country codes, units of measure).
- Support local vs. global values with appropriate approval mechanisms.
Stakeholder Engagement and Training#
- Establish a Data Governance Council with cross-functional representation.
- Provide training and documentation to ensure understanding of policies and responsibilities.
- Foster a culture of data ownership and literacy.
Monitoring and Continuous Improvement#
- Continuously monitor KPIs (like DQ scores, match rates, governance SLA adherence).
- Periodically review and refine governance processes.
- Incorporate feedback from data users and stakeholders.
Key Governance Focus Areas in Each Stage#
Inception#
-
Goal:
-
Establish strategic direction and governance foundation
-
Governance Activities:
-
Define MDM vision and objectives aligned with enterprise goals
- Identify data domains (e.g., Customer, Product, Site, etc.) to be governed
- Establish the Data Governance Council or steering committee
- Define initial governance framework (roles, policies, standards)
-
Assess current data maturity and pain points
-
Outcome:
-
Governance foundation and strategic buy-in
Requirements Specification#
-
Goal:
-
Define business needs and data governance requirements
Governance Activities:
- Define business rules and policies for master data (e.g., naming, formatting, mandatory attributes)
- Document data ownership for each domain
- Identify reference data needs and external standards (e.g., ISO, IDMP, EMA OMS)
- Capture data quality expectations (e.g., thresholds, golden record rules)
-
Begin RACI mapping for governance roles (Owner, Maintainer ,Steward)
-
Outcome:
-
Governance-aligned data requirements
Design#
-
Goal:
-
Architect the solution with governance in mind
Governance Activities:
- Define data models and ensure alignment with standards and policies
- Design approval workflows (e.g., create/update requests, stewardship routing)
- Create reference data models (with scope definitions: global vs. local)
- Design metadata and lineage capture mechanisms
- Create the data contract and change control model
-
Ensure compliance rules (e.g., GDPR, HIPAA) are reflected in design
-
Outcome:
-
Governance-ready data architecture and design blueprints
Implementation#
-
Goal:
-
Build with governance controls embedded
Governance Activities:
- Configure role-based access control (RBAC)
- Implement data validation and cleansing rules
- Build workflow-driven data stewardship processes
- Configure audit trails and electronic approvals
- Integrate data quality profiling and monitoring tools
-
Deploy reference data approval flows
-
Outcome:
-
Functional MDM Solution with governance mechanisms operational
Validation#
-
Goal:
-
Ensure governed data flows and rule enforcement
Governance Activities:
- Perform data quality validation tests
- Verify workflow approvals, stewardship actions, and audit trails
- Validate data lifecycle transitions (create, merge, delete, archive)
- Test compliance rules (e.g., PII redaction, Part 11 traceability)
- Review reference data control logic
-
Conduct UAT with data stewards and owners
-
Outcome:
-
Validated MDM processes with governance adherence
Deployment and Monitoring#
-
Goal:
-
Transition to production with live governance in place
Governance Activities:
- Activate ongoing stewardship workflows
- Initiate monitoring and escalation rules
- Publish governance dashboards and scorecards
- Finalize data policies and SOPs
- Begin periodic DQ audits and governance reviews
-
Communicate roles and contact points for governance issues
-
Outcome:
-
Governed MDM solution in live use
Decommission#
-
Goal:
-
Responsible retirement or transition of MDM system*
Governance Activities:
- Archive or migrate master and reference data per retention policies
- Review and revoke access privileges
- Close out data contracts and stewardship responsibilities
- Update data catalog to reflect system retirement
-
Document decommission logs and lineage traceability
-
Outcome:
-
Controlled system retirement with governed data handover