Data Governance Architect

Role Purpose

The Data Governance Consultant is responsible for assessing, defining, and operationalizing enterprise data governance across multiple data domains. The role emphasizes governance frameworks, policies, operating models, and adoption—ensuring data is trusted, compliant, and consistently used across the organization.

Key Responsibilities

• Current State (As Is) Governance Assessment
• • Perform a comprehensive as is assessment of data governance capabilities, including:
• o Data ownership, stewardship, and accountability models
• o Business glossary, metadata, and lineage practices
• o Data quality management approaches
• o Privacy, security, and retention controls
• • Assess maturity by comparing current practices against:
• o Industry best practices
• o Recognized governance frameworks (e.g., DAMA, federated governance models)
• o Regulatory expectations relevant to the organization
• • Identify gaps in governance coverage, consistency, and execution.

• Governance Framework & Best Practice Recommendations
• • Define and recommend a future state governance framework, including:
• o Governance operating model (central, federated, or hybrid)
• o Clear roles, RACI, and stewardship responsibilities
• o Standards for data definitions, quality, and lifecycle management
• • Propose best practice governance processes based on prior enterprise implementations, tailored to organizational culture and maturity.
• 4. Governance Technology Enablement
• •Advise on the selection and use of data governance and catalog platforms (e.g., business glossary, metadata, lineage, policy management).
• •Define how governance processes and policies should be operationalized through tools and workflows.
• •Ensure governance is embedded into day to day data and analytics activities.

• 5. Roadmap, Prioritization & Change Enablement
• • Drive the development of a long term data governance roadmap, detailing:
• o Target governance capabilities and maturity milestones
• o Prioritized initiatives with sequencing and dependencies
• o Quick win vs foundational improvements
• • Support change management and adoption by:
• o Defining rollout and enablement strategies
• o Aligning governance with business value and regulatory outcomes

• Expected Outcomes
• • Clear understanding of current governance maturity and gaps
• • Pragmatic, business aligned governance framework
• • Improved trust, consistency, and compliance across data domains
• • Scalable governance model that supports analytics and AI adoption