Head of Data Governance

Recruiting a Head of Data Governance requires a thorough understanding of the role. The following is a very general summary, which should be adapted to your specific context.

The Head of Data Governance reports directly to the Chief Data & AI Officer (CDAO) and is responsible for defining, implementing, and managing data governance policies and frameworks within the organization. This role ensures that data is secure, compliant with regulations, of high quality, and appropriately accessible. They collaborate closely with technical teams (Data Engineering, Data Platform), legal teams, and business units to embed governance practices into all Data & AI processes and products.

Core Responsibilities

a. Definition and Implementation of Data Governance Policies

• Data Governance Framework:
  Develop and implement policies covering data quality, security, accessibility, and metadata.
  Define roles and responsibilities (Data Owners, Data Stewards) and associated processes.

• Regulatory Compliance:
  Ensure governance practices comply with regulations (GDPR, AI Act, industry standards).
  Collaborate with legal teams to identify and mitigate risks of non-compliance.

b. Data Quality and Security Management

• Data Quality:
  Implement processes to guarantee data accuracy, consistency, and timeliness.
  Define quality metrics and performance indicators to measure and improve reliability.

• Data Security:
  Define and enforce security policies to protect sensitive data and prevent breaches.
  Work with IT to implement measures such as encryption, access control, and auditing.

c. Metadata and Data Lineage Management

• Metadata Management:
  Develop and maintain a centralized metadata catalog for discovery, understanding, and usage.
  Ensure metadata completeness, accuracy, and ongoing maintenance.

• Data Lineage:
  Implement mechanisms to track data origin, transformations, and usage.
  Collaborate with Data Engineering teams to embed lineage in data pipelines.

d. Collaboration with Technical and Business Teams

• Alignment with Technical Teams:
  Partner with Data Engineering and Data Platform to integrate governance into infrastructures and processes.
  Ensure Data & AI products comply with governance standards.

• Interface with Business Teams:
  Train and educate business teams on governance best practices.
  Gather business requirements to ensure policies align with operational needs.

e. Audit and Continuous Improvement

• Governance Audits:
  Conduct regular audits to assess compliance and identify improvement areas.
  Document findings and propose corrective actions.

• Continuous Improvement:
  Update policies and processes to reflect regulatory and technological evolutions.
  Promote a culture of governance across the organization.

f. Risk and Incident Management

• Risk Management:
  Identify and assess risks related to data security, quality, and compliance.
  Collaborate with risk management teams to embed governance in enterprise risk processes.

• Incident Management:
  Define and apply incident management procedures (e.g., security breaches, non-compliance).
  Coordinate corrective actions and capture lessons learned.

Concrete Achievements Examples

• Data Governance Framework: Designed and implemented a complete governance framework, reducing non-compliance risks by 30%.

• Data Quality Improvement: Implemented controls improving accuracy by 25% and reducing reporting errors.

• Metadata Catalog: Built and maintained a centralized catalog, making data more discoverable and usable for business teams.

• Security Incident Management: Developed procedures that reduced incident resolution time by 40%.