Roles Marketing & Communications
Head of Insight
Accountable for the insight discipline and for the customer evidence that marketing and the wider business rely on.
3tasks in this job
57skills it uses
6tools named
The skills this job leans on most
- 1Interpreting customer feedback and qualitative evidenceNeeded in 1 of 3 tasks
- 2Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsOneTrustMicrosoft Power BIAmplitudeNielsenIQ (incl. GfK)Microsoft PowerPointWorkdayCoupaMicrosoft Excel
All 3 tasks, where people get stuck and all 57 skills
Tasks
- Investigate customer-experience evidence
- Prepare a periodic performance or risk report for a management or governance committee
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
Where people get stuck
All skills
- Interpreting customer feedback and qualitative evidence
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing supplier dependency, data-handling and exit risk
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and appraising samples
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing testable data controls
- Diagnosing incorrect query and calculation results
- 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 employee experience evidence responsibly
- Evaluating responsible AI use risks
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Investigating anomalies
- 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
- Locating and acting on personal data for individual rights requests
- Mapping personal data processing and flows
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- 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
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonProduct and Digital Behaviour Analytics
9tasks in this job
98skills it uses
9tools named
The skills this job leans on most
- 1Interpreting customer feedback and qualitative evidenceNeeded in 3 of 9 tasks
- 2Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsOneTrustMicrosoft Power BIAmplitudeNielsenIQ (incl. GfK)Microsoft PowerPoint+ 3 more
All 9 tasks, where people get stuck and all 98 skills
Tasks
- Design a user research investigation
- Evaluate an AI use case for suitability, value and risk before adoption
- Investigate customer-experience evidence
- Design, administer and quality-check a professional survey
- 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
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
All skills
- Interpreting customer feedback and qualitative evidence
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- 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 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 data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and appraising samples
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and running evaluations of AI systems
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing testable data controls
- Diagnosing incorrect query and calculation results
- 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 employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating fitness and full cost of AI-assisted work
- 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
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- 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 correlation without inferring cause
- 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 personal data processing and flows
- 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
- Profiling data
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- 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
- 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
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
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
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour Analytics
9tasks in this job
100skills it uses
9tools named
The skills this job leans on most
- 1Interpreting customer feedback and qualitative evidenceNeeded in 3 of 9 tasks
- 2Mapping customer journeys and evidence gapsCore to this role
ToolsQualtricsOneTrustMicrosoft Power BIAmplitudeNielsenIQ (incl. GfK)Microsoft PowerPoint+ 3 more
All 9 tasks, where people get stuck and all 100 skills
Tasks
- Design a user research investigation
- Evaluate an AI use case for suitability, value and risk before adoption
- Investigate customer-experience evidence
- Design, administer and quality-check a professional survey
- 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
- Conduct or review a data protection impact assessment for a data, analytics or AI processing
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
Where people get stuck
All skills
- Interpreting customer feedback and qualitative evidence
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- 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 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 data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and appraising samples
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and running evaluations of AI systems
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing testable data controls
- Diagnosing incorrect query and calculation results
- 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 employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating fitness and full cost of AI-assisted work
- 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
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- 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 correlation without inferring cause
- 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 personal data processing and flows
- 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
- Profiling data
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- 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
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
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
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonProduct and Digital Behaviour Analytics
About the role
Accountable for the insight discipline and for the customer evidence that marketing and the wider business rely on. Heads the insight managers and decides research investment, method standards and which suppliers and AI-assisted tools are acceptable. Differs from the Head of Marketing, who owns channel and brand investment, by owning the evidence that steers it.
Also called Head of Customer Insight, Head of Research (customer or market research)