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Data Governance

Abstract

  • Data Governance ensures trusted, secure, and high-quality data across business units.
  • The primary objective is to establish roles, policies, and tools that drive regulatory compliance, operational efficiency, and data-driven innovation.
  • Key benefits include improved data quality, reduced silos, regulatory alignment (e.g., GDPR), and readiness for AI/analytics initiatives.
  • Execution follows a phased 24-month roadmap: strategy, framework design, pilot, and enterprise rollout.
  • A business-led Governance Committee of 3–5 members will drive cross-functional decision-making and ensure alignment with customer, legal, and operational priorities.
  • Initial core team of 6–8 FTEs supported by specialized tools for cataloging, quality, and stewardship.
  • Estimated 2-year cost: $3M–$4.4M including staffing, software licenses, training, and consulting support.

Objective of the Data Governance Program

To establish clear policies, roles, responsibilities, and processes that ensure high-quality, secure, compliant, and accessible data throughout its lifecycle, supporting strategic, operational, and regulatory needs of the organization.

Benefits

  1. Improved Data Quality – Accurate, consistent, and reliable data across departments.
  2. Regulatory Compliance – Align with GDPR, CCPA, ISO 27001, etc.
  3. Operational Efficiency – Reduce data silos, duplication, and manual reconciliations.
  4. Trust and Accountability – Clear ownership and stewardship foster a culture of responsible data use.
  5. Faster Decision-Making – Better data lineage and cataloging improve time-to-insight.
  6. Enabling AI & Analytics – Trusted data enables scalable use of ML models and business intelligence.
  7. Business Alignment – Cross-functional governance ensures data policies reflect legal, customer, and operational needs.

Realistic Execution Plan (Phased Approach)

Phase 1: Assessment & Strategy (0–3 months)

  • Inventory critical data assets (starting with customer, vehicle, manufacturing data).
  • Conduct maturity assessment.
  • Identify regulatory gaps and pain points.
  • Develop a business-aligned data governance strategy and roadmap.
  • Define success KPIs.
  • Identify candidates and confirm charter for Governance Committee.

Phase 2: Framework Design (3–6 months)

  • Define policies: data quality, classification, retention, privacy, access control.
  • Create governance operating model (e.g., Data Owners, Stewards, Council).
  • Formally launch Governance Committee and begin regular cadence of decision-making.
  • Establish metadata management and data lineage practices.
  • Choose pilot domain (e.g., Product Lifecycle Data or After-Sales).

Phase 3: Pilot Implementation (6–12 months)

  • Implement governance policies in selected domains.
  • Use tools (see below) for cataloging, quality checks, and lineage.
  • Train data owners/stewards and hold governance councils.
  • Collect feedback, refine policies.
  • Governance Committee to review pilot results and refine standards.

Phase 4: Enterprise Rollout (12–24 months)

  • Extend policies, roles, and tooling to other data domains.
  • Embed governance into workflows (e.g., PLM, ERP, IoT platforms).
  • Automate monitoring, audit logging, and remediation processes.
  • Continuous communication and change management.
  • Governance Committee to oversee enterprise-wide adoption and policy enforcement.

Governance Committee: Structure and Role

Purpose:

To ensure the Data Governance program is business-led, decision-capable, and aligned with enterprise-wide priorities, the organization will establish a Data Governance Committee empowered to approve policies, enforce standards, and prioritize governance activities.

Composition:

Suggested Governance Organization
Suggested Governance Organization
  • 3–5 business representatives from key functions (e.g., legal, operations, customer, compliance)
  • 2 technical representatives (e.g., Data Architect, Metadata/Lineage Specialist)
  • Chaired by a business representative to reinforce operational alignment
  • Odd-member structure to enable tie-breaking and decisive action

Responsibilities:

  • Approve and publish enterprise-wide governance policies
  • Maintain visibility of data policies across the organization
  • Oversee mandatory documentation of datasets, schemas, and transformations
  • Ensure standardized testing for:
    • Column integrity
    • Null enforcement
    • Business/domain rules
  • Champion data quality as a shared responsibility between business and IT
  • Align governance activities with corporate initiatives (e.g., digital transformation, regulatory audits)

Operating Model:

  • Meets monthly, with ad hoc reviews for major policy changes
  • Reports quarterly to CDO and CIO/CFO steering group
  • Coordinates with Data Stewards, Council, and technical working groups

Ideal Resources

1. Staffing (Core Data Governance Team - Initial 6–8 FTEs)

Role Count Description
Chief Data Officer / Head of Data Governance 1 Leadership, strategy, executive alignment
Data Governance Manager 1 Program execution, stakeholder coordination
Data Stewards 2–3 Domain-specific data management, quality control
Metadata/Lineage Specialist 1 Tool config, metadata & cataloging
Data Architect 1 Integration with enterprise architecture
Compliance & Privacy Officer 1 (fractional) Align with legal and data privacy frameworks

Expand with federated Data Stewards embedded in business functions.

2. Tools/Environment

  • Data Catalog & Lineage: Collibra, Alation, Atlan, Informatica
  • Data Quality: Talend, Informatica Data Quality, Great Expectations
  • Master Data Management: Informatica MDM, Reltio, SAP MDG
  • Collaboration/Documentation: Confluence, Microsoft Teams
  • Cloud Integration: AWS Glue Data Catalog, Azure Purview, GCP Data Catalog
  • Security & Access Control: Integration with IAM tools (Okta, Azure AD)

3. Environment

  • Executive support from CFO/CIO (depending on what organization structure is choosen)
  • Federated governance model supported by business units
  • Agile or SAFe-aligned delivery process

Realistic Timeline Overview (24-Month Plan)

Timeframe Milestones
0–3 mo. Assessment, strategy, roadmap
3–6 mo. Operating model, policies, governance body
6–12 mo. Pilot domain live, feedback loop
12–18 mo. Expand to additional domains, tool scaling
18–24 mo. Embed across enterprise, automate reporting, continuous improvement

Estimated Cost (Year 1–2)

Category Year 1 Year 2
Staffing (6–8 FTEs) $900K–$1.2M $1M–$1.5M
Tools & Licenses $300K–$500K $400K–$600K
Training & Change Mgmt $100K $150K
Professional Services (Consulting, Tool Setup) $250K $100K
Total Estimate $1.55M–$2.05M $1.65M–$2.35M

Optional cost savings: Use open-source (OpenMetadata, Amundsen, DataHub) or native cloud tools if applicable.

Risks & Remedies

Risks:

  • Lack of executive sponsorship or prioritization
  • Fragmented ownership or passive governance councils
  • Misalignment between governance policies and real-world business use cases

Remedies:

  • Regular steering committee reviews with executive attendance
  • Assign clear data stewardship roles at both strategic and operational levels
  • Ensure governance policies are business-informed and reviewed biannually