Chief Operating Officer
Holds enterprise accountability for operations and sits on the executive team.
2tasks in this job
20skills it uses
4tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 2 of 2 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 2 tasks
- 3Judging whether uncertainty changes the decisionNeeded in 1 of 2 tasks
- 4Maintaining a risk register with owners, triggers and responsesCore to this role
- 5Interpreting policy scope and delegated authority limitsCore to this role
ToolsMicrosoft SharePointSynergi LifeMicrosoft 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
- Prioritising work by evidenced value, cost, risk and confidence
- Planning delivery dependencies and the critical path
- Verifying benefits realisation and attribution
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
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
4tasks in this job
64skills it uses
5tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 4 of 4 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 2 of 4 tasks
- 3Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 4 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIMicrosoft ExcelMicrosoft PowerPointMicrosoft SharePointSynergi Life
All 4 tasks, where people get stuck and all 64 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- 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
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Judging whether uncertainty changes the decision
All skills
- Maintaining a risk register with owners, triggers and responses
- 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
- 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
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- 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
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- 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
- 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
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- 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
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- 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
- 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 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 soonDelivering Data and AI Projects with Evidence
5tasks in this job
82skills it uses
5tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 4 of 5 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 2 of 5 tasks
- 3Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 5 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 5 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 5 tasks
ToolsMicrosoft Power BIMicrosoft ExcelMicrosoft PowerPointMicrosoft SharePointSynergi Life
All 5 tasks, where people get stuck and all 82 skills
Tasks
- Define and govern a KPI metric contract
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- 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
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Judging whether uncertainty changes the decision
All skills
- Maintaining a risk register with owners, triggers and responses
- 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
- 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
- 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
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- 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
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- 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 the decision behind an analytical request
- 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
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- 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
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- 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
- 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 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 soonDelivering Data and AI Projects with Evidence
About the role
Holds enterprise accountability for operations and sits on the executive team. Answers to the chief executive or board for how the whole enterprise operates and owns operational risk at board level. Decides enterprise investment in automation and data and the resilience the business needs.
Also called COO