Head of Operations
Accountable for the operating model of operations across units, its investment and its fulfilment commitments.
4tasks in this job
67skills it uses
6tools named
The skills this job leans on most
- 1Writing evidence-led findings and auditable recommendations with their limitsNeeded in 4 of 4 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 3 of 4 tasks
- 3Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 4 tasks
- 4Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
- 5Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsMicrosoft Power BIMicrosoft ExcelMicrosoft PowerPointWorkdayEcoOnlineSynergi LifeIA-systemet
All 4 tasks, where people get stuck and all 67 skills
Tasks
- Build a defensible business case
- 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
- Analyse health and safety incidents and inspection data and track corrective actions to closure
Where people get stuck
- Governing shared data definitions
- Calculating and verifying KPIs against their specification
- Investigating anomalies
All skills
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Writing evidence-led findings and auditable recommendations with their limits
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- 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
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Forecasting business trends and turning points for planning
- Giving specific developmental feedback on data and AI work
- 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 customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- 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
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- 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
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
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 soonDelivering Data and AI Projects with Evidence
9tasks in this job
109skills it uses
7tools named
The skills this job leans on most
- 1Writing evidence-led findings and auditable recommendations with their limitsNeeded in 5 of 9 tasks
- 2Investigating anomaliesNeeded in 4 of 9 tasks
- 3Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 9 tasks
- 4Governing shared data definitionsNeeded in 2 of 9 tasks
- 5Calculating and verifying KPIs against their specificationNeeded in 1 of 9 tasks
ToolsMicrosoft Power BIMicrosoft ExcelMicrosoft PowerPointWorkdayEcoOnlineSynergi Life+ 1 more
All 9 tasks, where people get stuck and all 109 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Define and govern a KPI metric contract
- Build a defensible business case
- 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
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Analyse health and safety incidents and inspection data and track corrective actions to closure
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Testing competing explanations with discriminating evidence
All skills
- Calculating and verifying KPIs against their specification
- Governing shared data definitions
- Investigating anomalies
- Presenting and defending evidence-based recommendations to decision-makers
- Writing evidence-led findings and auditable recommendations with their limits
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing queue behaviour, capacity and service constraints
- 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
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building cohort and retention analyses with correct denominators
- 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
- Converting demand into capacity requirements
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting needs through interviews, workshops and observation
- 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 how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Forecasting business trends and turning points for planning
- Giving specific developmental feedback on data and AI work
- 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 budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- 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
- 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
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- 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
- 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
- Writing data quality rules
- 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
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 soonManaging Data and AI Products
9tasks in this job
111skills it uses
7tools named
The skills this job leans on most
- 1Writing evidence-led findings and auditable recommendations with their limitsNeeded in 5 of 9 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 9 tasks
- 3Governing shared data definitionsNeeded in 2 of 9 tasks
- 4Calculating and verifying KPIs against their specificationNeeded in 1 of 9 tasks
- 5Specifying BI measure semantics: additivity, aggregation and timeNeeded in 1 of 9 tasks
ToolsMicrosoft Power BIMicrosoft ExcelMicrosoft PowerPointWorkdayEcoOnlineSynergi Life+ 1 more
All 9 tasks, where people get stuck and all 111 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Define and govern a KPI metric contract
- Build a defensible business case
- 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
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Analyse health and safety incidents and inspection data and track corrective actions to closure
Where people get stuck
All skills
- Calculating and verifying KPIs against their specification
- Governing shared data definitions
- Presenting and defending evidence-based recommendations to decision-makers
- Specifying BI measure semantics: additivity, aggregation and time
- Writing evidence-led findings and auditable recommendations with their limits
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing queue behaviour, capacity and service constraints
- 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
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building cohort and retention analyses with correct denominators
- 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
- Converting demand into capacity requirements
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting needs through interviews, workshops and observation
- 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 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
- Giving specific developmental feedback on data and AI work
- 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 budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- 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
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- 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
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying AI transparency and explanation duties to affected people
- Specifying decision-useful reports
- 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
- Writing data quality rules
- 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
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 soonManaging Data and AI ProductsDelivering Data and AI Projects with Evidence
About the role
Accountable for the operating model of operations across units, its investment and its fulfilment commitments. Decides structure, controls and automation investment and reports operational performance and risk to executives. Manages the operations, service, planning and improvement managers and chooses the measures the operation is run on.