Head of Data Governance
Accountable for enterprise data governance, including the policy framework, governance operating model and data risk reporting.
5tasks in this job
74skills it uses
7tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 3 of 5 tasks
- 2Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsMicrosoft PowerPointMicrosoft Power BIMicrosoft ExcelOneTrustCredo AICollibraServiceNow
All 5 tasks, where people get stuck and all 74 skills
Tasks
- Prepare a periodic performance or risk report for a management or governance committee
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assign data ownership and stewardship and decide a domain's data standards
- Set and review the organisation's AI use policy and AI literacy requirements
- Maintain the AI use register and AI management system evidence across all AI systems
Where people get stuck
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Identifying and prioritising critical data elements
All skills
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Decomposing one's own tasks for AI assistance
- Designing and validating digital analytics event measurement
- Designing decision rights and dispute routes for data and AI work
- Designing enforceable data governance policies
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting sustainability reporting standards and metric boundaries
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Protecting sensitive data in AI-assisted work
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling finance subledgers and close records
- Record-keeping requirements for processing activities and AI systems
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDelivering Data and AI Projects with Evidence
9tasks in this job
88skills it uses
9tools named
The skills this job leans on most
- 1Assigning and operating data ownership and stewardshipNeeded in 6 of 9 tasks
- 2Rating risks against appetite and choosing proportionate treatmentNeeded in 5 of 9 tasks
- 3Designing enforceable data governance policiesNeeded in 4 of 9 tasks
- 4Identifying and prioritising critical data elementsNeeded in 3 of 9 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsMicrosoft PowerPointMicrosoft PurviewMicrosoft Power BIOneTrustCredo AICollibra+ 3 more
All 9 tasks, where people get stuck and all 88 skills
Tasks
- Prepare a periodic performance or risk report for a management or governance committee
- Design and review governed data-access entitlement policy
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assign data ownership and stewardship and decide a domain's data standards
- Test the design and operating effectiveness of a data-dependent control
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Classify an AI system's risk and assure its documentation, evaluation and oversight evidence
- Set and review the organisation's AI use policy and AI literacy requirements
- Maintain the AI use register and AI management system evidence across all AI systems
Where people get stuck
All skills
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Identifying and prioritising critical data elements
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assessing the control and reliability of end-user calculation files
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Designing and appraising samples
- Designing and running evaluations of AI systems
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing role-based access entitlement models
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Protecting sensitive data in AI-assisted work
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling finance subledgers and close records
- Record-keeping requirements for processing activities and AI systems
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft PowerPoint
- Microsoft Purview
- Microsoft Power BI
- OneTrust
- Credo AI
- Collibra
- Tableau
- Microsoft Excel
- ServiceNow
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDelivering Data and AI Projects with Evidence
8tasks in this job
101skills it uses
7tools named
The skills this job leans on most
- 1Assigning and operating data ownership and stewardshipNeeded in 6 of 8 tasks
- 2Designing enforceable data governance policiesNeeded in 4 of 8 tasks
- 3Rating risks against appetite and choosing proportionate treatmentNeeded in 4 of 8 tasks
- 4Identifying and prioritising critical data elementsNeeded in 3 of 8 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsMicrosoft Power BIMicrosoft PowerPointCredo AICollibraMicrosoft PurviewMicrosoft Excel+ 1 more
All 8 tasks, where people get stuck and all 101 skills
Tasks
- Prepare a periodic performance or risk report for a management or governance committee
- Design and govern the operating model for data, analytics and AI work in a function
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assign data ownership and stewardship and decide a domain's data standards
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Classify an AI system's risk and assure its documentation, evaluation and oversight evidence
- Set and review the organisation's AI use policy and AI literacy requirements
- Maintain the AI use register and AI management system evidence across all AI systems
Where people get stuck
All skills
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Identifying and prioritising critical data elements
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assessing the control and reliability of end-user calculation files
- Bounding and securing AI systems' tool and data access
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and appraising samples
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing role-based access entitlement models
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Protecting sensitive data in AI-assisted work
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling finance subledgers and close records
- Record-keeping requirements for processing activities and AI systems
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft Power BI
- Microsoft PowerPoint
- Credo AI
- Collibra
- Microsoft Purview
- Microsoft Excel
- ServiceNow
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDelivering Data and AI Projects with Evidence
About the role
Accountable for enterprise data governance, including the policy framework, governance operating model and data risk reporting. Decides how decision rights are allocated across domains and which residual data risks are accepted. Leads governance managers and answers to a director or executive for governance across the whole organisation.