Roles Marketing & Communications
Head of Marketing
Accountable for the marketing discipline's operating model, investment and customer-data and consent risk.
3tasks in this job
34skills it uses
6tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 2 of 3 tasks
- 2Measuring campaign outcomes with valid denominatorsNeeded in 1 of 3 tasks
- 3Critically evaluating marketing attribution claimsNeeded in 1 of 3 tasks
- 4Allocating spend across channels by marginal returnNeeded in 1 of 3 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsMicrosoft Power BIOneTrustMicrosoft ExcelMicrosoft PowerPointWorkdaySalesforceHubSpotGoogle AdsMeta Ads Manager
All 3 tasks, where people get stuck and all 34 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
- Plan and reallocate marketing spend across channels on measured return
Where people get stuck
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Identifying and prioritising critical data elements
All skills
- Rating risks against appetite and choosing proportionate treatment
- Analysing customer lifetime value and unit economics
- Applying data classification and handling rules
- Assessing supplier dependency, data-handling and exit risk
- Building decision-focused reports and dashboards
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Designing and validating digital analytics event measurement
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- 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
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- 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 uncertainty changes the decision
- Preserving incident evidence and timeline
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
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
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- 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
9tasks in this job
112skills it uses
9tools named
The skills this job leans on most
- 1Assigning and operating data ownership and stewardshipNeeded in 4 of 9 tasks
- 2Designing enforceable data governance policiesNeeded in 3 of 9 tasks
- 3Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 9 tasks
- 4Measuring campaign outcomes with valid denominatorsNeeded in 1 of 9 tasks
- 5Critically evaluating marketing attribution claimsNeeded in 1 of 9 tasks
ToolsMicrosoft Power BIOneTrustMicrosoft ExcelMicrosoft PowerPointWorkdaySalesforce+ 3 more
All 9 tasks, where people get stuck and all 112 skills
Tasks
- Define and govern a KPI metric contract
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Prioritise and rebalance an investment portfolio against value, capacity and benefit 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
- Assign data ownership and stewardship and decide a domain's data standards
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Plan and reallocate marketing spend across channels on measured return
Where people get stuck
- Critically evaluating marketing attribution claims
- Rating risks against appetite and choosing proportionate treatment
All skills
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Maintaining a risk register with owners, triggers and responses
- 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 source credibility and fitness for a question
- 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
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and verifying KPIs against their specification
- 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
- Controlling disclosure risk in shared data and outputs
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- 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
- Establishing and declaring data grain
- 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
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- 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
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- 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
- 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
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- 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 conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- 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
9tasks in this job
114skills it uses
9tools named
The skills this job leans on most
- 1Assigning and operating data ownership and stewardshipNeeded in 4 of 9 tasks
- 2Designing enforceable data governance policiesNeeded in 3 of 9 tasks
- 3Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 9 tasks
- 4Measuring campaign outcomes with valid denominatorsNeeded in 1 of 9 tasks
- 5Critically evaluating marketing attribution claimsNeeded in 1 of 9 tasks
ToolsMicrosoft Power BIOneTrustMicrosoft ExcelMicrosoft PowerPointWorkdaySalesforce+ 3 more
All 9 tasks, where people get stuck and all 114 skills
Tasks
- Define and govern a KPI metric contract
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Prioritise and rebalance an investment portfolio against value, capacity and benefit 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
- Assign data ownership and stewardship and decide a domain's data standards
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Plan and reallocate marketing spend across channels on measured return
Where people get stuck
- Critically evaluating marketing attribution claims
- Rating risks against appetite and choosing proportionate treatment
All skills
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Maintaining a risk register with owners, triggers and responses
- 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 source credibility and fitness for a question
- 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
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and verifying KPIs against their specification
- 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
- Controlling disclosure risk in shared data and outputs
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- 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
- Establishing and declaring data grain
- 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
- 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
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- 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
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- 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
- 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
- Profiling data
- 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
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- 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
About the role
Accountable for the marketing discipline's operating model, investment and customer-data and consent risk. Heads the marketing managers and decides how the budget is allocated and what measurement evidence it rests on. Accepts residual brand and data risk within enterprise direction.
Also called Head of Digital Marketing