Chief Data & AI Officer
Accountable at enterprise level for data and AI strategy, value and risk and for the policies every function follows.
5tasks in this job
73skills it uses
7tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 4 of 5 tasks
- 2Assigning and operating data ownership and stewardshipNeeded in 2 of 5 tasks
- 3Designing decision rights and dispute routes for data and AI workNeeded in 2 of 5 tasks
- 4Defining measurable outcomes and leading indicatorsNeeded in 1 of 5 tasks
- 5Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 5 tasks
ToolsJiraMicrosoft ExcelMicrosoft Power BIMicrosoft PowerPointOneTrustServiceNowMicrosoft Purview
All 5 tasks, where people get stuck and all 73 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- 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
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- Set and review the organisation's AI use policy and AI literacy requirements
Where people get stuck
- Enforcing and auditing data access
- Designing enforceable data governance policies
- Prioritising work by evidenced value, cost, risk and confidence
All skills
- Assigning and operating data ownership and stewardship
- Defining measurable outcomes and leading indicators
- Designing decision rights and dispute routes for data and AI work
- Maintaining a risk register with owners, triggers and responses
- 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 privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building cohort and retention analyses with correct denominators
- 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
- Comparing periods consistently
- Constructing clear and honest charts
- Decomposing one's own tasks for AI assistance
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- Designing enforceable data governance policies
- 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
- 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 customer feedback and qualitative evidence
- 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
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Protecting sensitive data in AI-assisted work
- Reading business metrics
- 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
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- Working through stakeholder conflict over evidence and responsibility
- 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
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
7tasks in this job
96skills it uses
10tools named
The skills this job leans on most
- 1Enforcing and auditing data accessNeeded in 3 of 7 tasks
- 2Assigning and operating data ownership and stewardshipNeeded in 3 of 7 tasks
- 3Designing enforceable data governance policiesNeeded in 3 of 7 tasks
- 4Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 7 tasks
- 5Defining measurable outcomes and leading indicatorsNeeded in 2 of 7 tasks
ToolsWorkdaySympa HRMicrosoft ExcelMicrosoft Power BIDatabricksMicrosoft Purview+ 4 more
All 7 tasks, where people get stuck and all 96 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Design and review governed data-access entitlement policy
- 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
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- Set and review the organisation's AI use policy and AI literacy requirements
Where people get stuck
- Presenting and defending evidence-based recommendations to decision-makers
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Assigning and operating data ownership and stewardship
- Defining measurable outcomes and leading indicators
- Designing enforceable data governance policies
- Enforcing and auditing data access
- Prioritising work by evidenced value, cost, risk and confidence
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Applying research ethics and participant consent
- 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 privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Decomposing one's own tasks for AI assistance
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing experiments
- Designing implementable change plans
- Designing role-based access entitlement models
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- 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
- 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 customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- 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
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Variance analysis
- Verifying benefits realisation and attribution
- Weighing conflicting evidence by design strength
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Workday
- Sympa HR
- Microsoft Excel
- Microsoft Power BI
- Databricks
- Microsoft Purview
- Microsoft PowerPoint
- OneTrust
- ServiceNow
- Microsoft 365 Copilot
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
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDelivering Data and AI Projects with Evidence
6tasks in this job
96skills it uses
7tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 5 of 6 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 6 tasks
- 3Assigning and operating data ownership and stewardshipNeeded in 3 of 6 tasks
- 4Defining measurable outcomes and leading indicatorsNeeded in 1 of 6 tasks
- 5Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 6 tasks
ToolsMicrosoft Power BIJiraMicrosoft PurviewMicrosoft ExcelMicrosoft PowerPointOneTrust+ 1 more
All 6 tasks, where people get stuck and all 96 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Design and review governed data-access entitlement policy
- 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
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- Set and review the organisation's AI use policy and AI literacy requirements
Where people get stuck
- Rating risks against appetite and choosing proportionate treatment
- Judging whether uncertainty changes the decision
- Interpreting policy scope and delegated authority limits
All skills
- Assigning and operating data ownership and stewardship
- Defining measurable outcomes and leading indicators
- Maintaining a risk register with owners, triggers and responses
- Presenting and defending evidence-based recommendations to decision-makers
- 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
- Applying research ethics and participant consent
- 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 privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Decomposing one's own tasks for AI assistance
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing enforceable data governance policies
- Designing experiments
- Designing implementable change plans
- Designing role-based access entitlement models
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- 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 and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- 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
- 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 customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- 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 personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Protecting sensitive data in AI-assisted work
- Reading business metrics
- 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
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Variance analysis
- Verifying benefits realisation and attribution
- Weighing conflicting evidence by design strength
- 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
- Jira
- Microsoft Purview
- Microsoft Excel
- Microsoft PowerPoint
- OneTrust
- ServiceNow
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
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Accountable at enterprise level for data and AI strategy, value and risk and for the policies every function follows. Answers to the chief executive or board for data and AI across the organisation and sits on the executive team. Decides major data and AI investments and which enterprise risks are accepted within board appetite.
Also called Chief Data Officer, CDO (data), CDAO, Chief AI Officer