Director of Data & Analytics
Accountable for directing the whole data and analytics Function or several Heads and for owning its budget and strategy.
4tasks in this job
71skills it uses
7tools named
The skills this job leans on most
- 1Assigning and operating data ownership and stewardshipNeeded in 1 of 4 tasks
- 2Defining measurable outcomes and leading indicatorsNeeded in 1 of 4 tasks
- 3Designing decision rights and dispute routes for data and AI workNeeded in 1 of 4 tasks
ToolsJiraMicrosoft Power BIMicrosoft ExcelMicrosoft PowerPointOneTrustServiceNowMicrosoft Purview
All 4 tasks, where people get stuck and all 71 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
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Maintaining a risk register with owners, triggers and responses
- Planning delivery dependencies and the critical path
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
- 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
- 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
- Maintaining a risk register with owners, triggers and responses
- 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
- 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
- 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
4tasks in this job
73skills it uses
8tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 4 tasks
- 2Assigning and operating data ownership and stewardshipNeeded in 1 of 4 tasks
- 3Defining measurable outcomes and leading indicatorsNeeded in 1 of 4 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsMicrosoft ExcelMicrosoft Power BIJiraMicrosoft PowerPointWorkdayOneTrust+ 2 more
All 4 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
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
- 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
- Designing and comparing decision scenarios
- 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
- 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
- Judging whether uncertainty changes the decision
- 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
- 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
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- 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
All tools
- Microsoft Excel
- Microsoft Power BI
- Jira
- Microsoft PowerPoint
- Workday
- OneTrust
- 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
4tasks in this job
73skills it uses
7tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 4 tasks
- 2Assigning and operating data ownership and stewardshipNeeded in 1 of 4 tasks
- 3Defining measurable outcomes and leading indicatorsNeeded in 1 of 4 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIJiraMicrosoft ExcelMicrosoft PowerPointServiceNowMicrosoft Purview+ 1 more
All 4 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
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
- 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
- Designing and comparing decision scenarios
- 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
- 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
- Judging whether uncertainty changes the decision
- 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
- 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
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- 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
All tools
- Microsoft Power BI
- Jira
- Microsoft Excel
- Microsoft PowerPoint
- ServiceNow
- Microsoft Purview
- OneTrust
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 directing the whole data and analytics Function or several Heads and for owning its budget and strategy. Decides the operating model for data, analytics and AI and accepts major residual risks within delegated authority. Answers to an executive for the Function's total investment, where a Head runs one discipline.
Also called Data Director, VP Data, VP Data & Analytics