Roles Marketing & Communications
Chief Marketing Officer
Accountable for marketing and brand across the whole enterprise as a member of the executive team.
2tasks in this job
20skills it uses
3tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 2 of 2 tasks
- 2Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsOneTrustMicrosoft PowerPointMicrosoft Power BI
All 2 tasks, where people get stuck and all 20 skills
Tasks
- 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
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
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-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
- Classifying AI uses against regulatory obligations
- 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
- Judging whether and how to use AI for a task
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
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
7tasks in this job
85skills it uses
4tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 6 of 7 tasks
- 2Judging whether and how to use AI for a taskNeeded in 3 of 7 tasks
- 3Evaluating fitness and full cost of AI-assisted workNeeded in 3 of 7 tasks
- 4Evaluating responsible AI use risksNeeded in 3 of 7 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsOneTrustMicrosoft ExcelMicrosoft Power BIMicrosoft PowerPoint
All 7 tasks, where people get stuck and all 85 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Evaluate an AI use case for suitability, value and risk before adoption
- Build a defensible business case
- Maintain a data and AI risk register and record risk treatment and acceptance
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Plan and reallocate marketing spend across channels on measured return
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
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
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- Rating risks against appetite and choosing proportionate treatment
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing customer lifetime value and unit economics
- 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
- 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
- Building driver-based financial forecasts
- 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 a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- Designing and validating digital analytics event measurement
- Designing auditable decision models
- 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
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- 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
- 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
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- 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 soonApplied AI at WorkDesigning Controlled Automations and AI-assisted Workflows
7tasks in this job
85skills it uses
4tools named
The skills this job leans on most
- 1Presenting and defending evidence-based recommendations to decision-makersNeeded in 3 of 7 tasks
- 2Judging whether and how to use AI for a taskNeeded in 3 of 7 tasks
- 3Evaluating fitness and full cost of AI-assisted workNeeded in 3 of 7 tasks
- 4Evaluating responsible AI use risksNeeded in 3 of 7 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsOneTrustMicrosoft ExcelMicrosoft Power BIMicrosoft PowerPoint
All 7 tasks, where people get stuck and all 85 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Evaluate an AI use case for suitability, value and risk before adoption
- Build a defensible business case
- Maintain a data and AI risk register and record risk treatment and acceptance
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Plan and reallocate marketing spend across channels on measured return
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- Presenting and defending evidence-based recommendations to decision-makers
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing customer lifetime value and unit economics
- 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
- 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
- Building driver-based financial forecasts
- 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 a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and comparing decision scenarios
- Designing and running evaluations of AI systems
- Designing and validating digital analytics event measurement
- Designing auditable decision models
- 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
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Monitoring model fitness and drift
- 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
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- 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
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- 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
Coming soonApplied AI at Work
About the role
Accountable for marketing and brand across the whole enterprise as a member of the executive team. Answers to the board for growth, brand and customer strategy. Data and AI judgement sits in how the organisation governs customer data and AI in marketing.
Also called CMO