Roles Marketing & Communications
Head of Marketing Operations
Accountable for the marketing operations discipline and for the systems, data flows and consent controls that marketing runs on.
2tasks in this job
33skills it uses
5tools named
The skills this job leans on most
- 1Evaluating fitness and full cost of AI-assisted workNeeded in 1 of 2 tasks
- 2Comparing options against explicit decision criteriaNeeded in 1 of 2 tasks
- 3Appraising options on a common incremental basis against a do-nothing baselineNeeded in 1 of 2 tasks
ToolsSalesforceSalesforce Marketing CloudHubSpotOneTrustMicrosoft Power BIMicrosoft PowerPointMicrosoft ExcelMicrosoft PurviewCoupaAdobe Marketo EngageJira
All 2 tasks, where people get stuck and all 33 skills
Tasks
- Maintain a data and AI risk register and record risk treatment and acceptance
- Evaluate and recommend a data, analytics or AI product or vendor
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Planning delivery dependencies and the critical path
- Verifying benefits realisation and attribution
All skills
- Appraising options on a common incremental basis against a do-nothing baseline
- Comparing options against explicit decision criteria
- Applying data classification and handling rules
- Assessing supplier dependency, data-handling and exit risk
- Communicating quantified uncertainty
- Designing and comparing decision scenarios
- 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
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Explaining control types, dependencies and compensating controls
- Giving specific developmental feedback on data and AI work
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting supplier contract duties, terms and exposure
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- 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
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
Paths to start
Coming soonFinancial Modelling and Decision SupportDesigning Controlled Automations and AI-assisted Workflows
10tasks in this job
122skills it uses
11tools named
The skills this job leans on most
- 1Evaluating fitness and full cost of AI-assisted workNeeded in 3 of 10 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 10 tasks
- 3Comparing options against explicit decision criteriaNeeded in 2 of 10 tasks
- 4Planning delivery dependencies and the critical pathNeeded in 1 of 10 tasks
- 5Verifying benefits realisation and attributionNeeded in 1 of 10 tasks
ToolsSalesforceSalesforce Marketing CloudHubSpotOneTrustMicrosoft Power BIMicrosoft PowerPoint+ 5 more
All 10 tasks, where people get stuck and all 122 skills
Tasks
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- 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 review governed data-access entitlement policy
- 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
- Evaluate and recommend a data, analytics or AI product or vendor
- Configure, test and release a change to a business system of record under change control
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Maintaining a risk register with owners, triggers and responses
All skills
- Comparing options against explicit decision criteria
- Evaluating fitness and full cost of AI-assisted work
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Analysing error patterns and failure modes in models and AI systems
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing change risk, collisions and go or no-go
- 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
- Assigning and operating data ownership and stewardship
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- 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 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 enforceable data governance policies
- Designing role-based access entitlement models
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- 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
- Establishing and declaring data grain
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- 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 evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- 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
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- 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
- 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 customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Preparing change records with impact assessment and rollback
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- 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
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying AI transparency and explanation duties to affected people
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- 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 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
- Writing verifiable requirements and acceptance criteria
Paths to start
Coming soonDelivering Data and AI Projects with EvidenceFinancial Modelling and Decision SupportManaging Data and AI Products
11tasks in this job
125skills it uses
11tools named
The skills this job leans on most
- 1Assigning and operating data ownership and stewardshipNeeded in 5 of 11 tasks
- 2Evaluating fitness and full cost of AI-assisted workNeeded in 4 of 11 tasks
- 3Designing enforceable data governance policiesNeeded in 4 of 11 tasks
- 4Comparing options against explicit decision criteriaNeeded in 2 of 11 tasks
- 5Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 11 tasks
ToolsSalesforceSalesforce Marketing CloudHubSpotOneTrustMicrosoft PurviewMicrosoft Power BI+ 5 more
All 11 tasks, where people get stuck and all 125 skills
Tasks
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Evaluate an AI use case for suitability, value and risk before adoption
- Triage a suspected data security or personal data breach and preserve evidence
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Design and review governed data-access entitlement policy
- 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
- Evaluate and recommend a data, analytics or AI product or vendor
- Configure, test and release a change to a business system of record under change control
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
- Evaluating fitness and full cost of AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
All skills
- Assigning and operating data ownership and stewardship
- Comparing options against explicit decision criteria
- Designing enforceable data governance policies
- Evaluating fitness and full cost of AI-assisted work
- Maintaining a risk register with owners, triggers and responses
- Analysing error patterns and failure modes in models and AI systems
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing change risk, collisions and go or no-go
- 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
- Bounding and securing AI systems' tool and data access
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- 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 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 role-based access entitlement models
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- 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
- Establishing and declaring data grain
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- 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 evidence-led retrospectives and corrective actions
- Facilitating group decisions using disputed evidence
- Gating releases on evidence with rollback ready
- 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
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- 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
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- 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
- Preparing change records with impact assessment and rollback
- 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
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- 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
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying AI transparency and explanation duties to affected people
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- 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
- Writing verifiable requirements and acceptance criteria
All tools
- Salesforce
- Salesforce Marketing Cloud
- HubSpot
- OneTrust
- Microsoft Purview
- Microsoft Power BI
- Microsoft PowerPoint
- Microsoft Excel
- Coupa
- Adobe Marketo Engage
- Jira
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
Coming soonFinancial Modelling and Decision SupportDesigning Controlled Automations and AI-assisted Workflows
About the role
Accountable for the marketing operations discipline and for the systems, data flows and consent controls that marketing runs on. Heads the marketing operations managers and decides technology investment, integration standards and marketing data rules. Data and AI judgement sits in which tools and automated decisions the business can trust with customer data.
Also called Head of CRM, Head of Marketing Technology