Head of Data & Analytics
Accountable for a data and analytics function's operating model, portfolio, investment and data and AI risk.
3tasks in this job
52skills it uses
7tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 3 tasks
- 2Rating risks against appetite and choosing proportionate treatmentNeeded in 2 of 3 tasks
- 3Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 3 tasks
- 4Planning delivery dependencies and the critical pathNeeded in 1 of 3 tasks
- 5Verifying benefits realisation and attributionNeeded in 1 of 3 tasks
ToolsMicrosoft ExcelJiraMicrosoft Power BIMicrosoft PowerPointWorkdayServiceNowMicrosoft Purview
All 3 tasks, where people get stuck and all 52 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
Where people get stuck
- Assigning and operating data ownership and stewardship
- Defining measurable outcomes and leading indicators
- Designing decision rights and dispute routes for data and AI work
All skills
- Maintaining a risk register with owners, triggers and responses
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Verifying benefits realisation and attribution
- 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
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- 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
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- 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
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Reading business metrics
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Revising prior decisions when evidence changes
- Testing competing explanations with discriminating evidence
- Variance analysis
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonDelivering Data and AI Projects with EvidenceManaging Data and AI Products
6tasks in this job
81skills it uses
10tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 6 tasks
- 2Assigning and operating data ownership and stewardshipNeeded in 2 of 6 tasks
- 3Defining measurable outcomes and leading indicatorsNeeded in 2 of 6 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 6 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 6 tasks
ToolsWorkdaySympa HRMicrosoft ExcelMicrosoft Power BIDatabricksJira+ 4 more
All 6 tasks, where people get stuck and all 81 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 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
- Evaluate and recommend a data, analytics or AI product or vendor
- Set and review the organisation's AI use policy and AI literacy requirements
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
All skills
- Assigning and operating data ownership and stewardship
- Defining measurable outcomes and leading indicators
- Maintaining a risk register with owners, triggers and responses
- Planning delivery dependencies and the critical path
- 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
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- 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
- Diagnosing capability gaps from work evidence and planning development
- 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
- Evaluating fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating procurement spend leakage and duplicate commitments
- 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
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- 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
- 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 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
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- 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 evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Workday
- Sympa HR
- Microsoft Excel
- Microsoft Power BI
- Databricks
- Jira
- Microsoft PowerPoint
- Microsoft Purview
- ServiceNow
- Microsoft Fabric (incl. OneLake)
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
Coming soonDelivering Data and AI Projects with Evidence
6tasks in this job
93skills it uses
9tools named
The skills this job leans on most
- 1Assigning and operating data ownership and stewardshipNeeded in 2 of 6 tasks
- 2Defining measurable outcomes and leading indicatorsNeeded in 2 of 6 tasks
- 3Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 6 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 6 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 6 tasks
ToolsWorkdaySympa HRMicrosoft SharePointMicrosoft PowerPointMicrosoft Power BIJira+ 3 more
All 6 tasks, where people get stuck and all 93 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- Evaluate an AI use case for suitability, value and risk before adoption
- 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
- Set and review the organisation's AI use policy and AI literacy requirements
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
All skills
- Assigning and operating data ownership and stewardship
- Defining measurable outcomes and leading indicators
- Maintaining a risk register with owners, triggers and responses
- Planning delivery dependencies and the critical path
- 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
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- 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 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
- Evaluating fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Facilitating group decisions using disputed evidence
- 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
- Independently checking analytical conclusions and claims
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- 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 personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- 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
- 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 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
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- 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
- Writing verifiable requirements and acceptance criteria
All tools
- Workday
- Sympa HR
- Microsoft SharePoint
- Microsoft PowerPoint
- Microsoft Power BI
- Jira
- Microsoft Excel
- ServiceNow
- Microsoft Purview
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
Coming soonDelivering Data and AI Projects with Evidence
About the role
Accountable for a data and analytics function's operating model, portfolio, investment and data and AI risk. Decides which capabilities to build, how teams are organised and which residual risks are accepted. Advises executives on data and AI use while each Analytics Manager runs a team.
Also called Head of Data, Head of Data & AI, Head of AI, Head of Analytics, Head of Data Science