Head of Privacy
Accountable for a privacy function, including its strategy, budget and reporting of data protection risk to the highest management level.
3tasks in this job
47skills it uses
4tools named
The skills this job leans on most
- 1Writing evidence-led findings and auditable recommendations with their limitsNeeded in 1 of 3 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 3 tasks
- 3Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsMicrosoft PowerPointMicrosoft Power BIOneTrustDPOrganizer
All 3 tasks, where people get stuck and all 47 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
- Map personal data processing and maintain the record of processing activities
Where people get stuck
- Presenting and defending evidence-based recommendations to decision-makers
- Testing the completeness and accuracy of reports and data that controls rely on
All skills
- Writing evidence-led findings and auditable recommendations with their limits
- Applying data classification and handling rules
- 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 decision-focused reports and dashboards
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Designing testable data controls
- 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 responsible AI use risks
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- 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
- 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
- Monitoring and testing data control effectiveness
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- 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
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Writing data quality rules
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- 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
5tasks in this job
64skills it uses
5tools named
The skills this job leans on most
- 1Writing evidence-led findings and auditable recommendations with their limitsNeeded in 1 of 5 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 5 tasks
- 3Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsServiceNowMicrosoft Power BIMicrosoft PowerPointOneTrustDPOrganizer
All 5 tasks, where people get stuck and all 64 skills
Tasks
- Triage a suspected data security or personal data breach and preserve evidence
- 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
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Map personal data processing and maintain the record of processing activities
Where people get stuck
- Presenting and defending evidence-based recommendations to decision-makers
- Testing the completeness and accuracy of reports and data that controls rely on
All skills
- Writing evidence-led findings and auditable recommendations with their limits
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- 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
- 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
- Controlling disclosure risk in shared data and outputs
- Designing testable data controls
- 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
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Investigating anomalies
- 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
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- 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
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running incident command: severity, timeline and status updates
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Writing data quality rules
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- 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
5tasks in this job
77skills it uses
4tools named
The skills this job leans on most
- 1Writing evidence-led findings and auditable recommendations with their limitsNeeded in 2 of 5 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 2 of 5 tasks
- 3Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsServiceNowOneTrustMicrosoft PowerPointMicrosoft Power BI
All 5 tasks, where people get stuck and all 77 skills
Tasks
- Triage a suspected data security or personal data breach and preserve evidence
- 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
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
Where people get stuck
- Presenting and defending evidence-based recommendations to decision-makers
- Testing the completeness and accuracy of reports and data that controls rely on
All skills
- Presenting and defending evidence-based recommendations to decision-makers
- Writing evidence-led findings and auditable recommendations with their limits
- 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-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
- 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 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
- Designing and comparing decision scenarios
- Designing testable data controls
- 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
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Investigating anomalies
- 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
- 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 data quality and freshness in production
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- 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
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running incident command: severity, timeline and status updates
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- 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
- Writing data quality rules
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- 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
About the role
Accountable for a privacy function, including its strategy, budget and reporting of data protection risk to the highest management level. Decides how privacy risk is reported and when to raise concerns formally. Leads privacy managers and protects the Data Protection Officer's independence and direct access to the highest management level.
Also called Head of Data Protection