Operations Director
Directs the whole Operations Function or several Heads and owns its budget and strategy.
2tasks in this job
43skills it uses
3tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 2 tasks
- 2Planning delivery dependencies and the critical pathNeeded in 1 of 2 tasks
- 3Verifying benefits realisation and attributionNeeded in 1 of 2 tasks
ToolsMicrosoft ExcelEsri ArcGIS (ArcGIS Pro, ArcGIS Online)Paragon routing and schedulingPosition GreenMicrosoft Power BIMicrosoft PowerPointMicrosoft SharePointSynergi Life
All 2 tasks, where people get stuck and all 43 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Testing investment business cases under uncertainty
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
All skills
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- 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
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- 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
- Presenting and defending evidence-based recommendations to decision-makers
- 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 resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Variance analysis
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonDelivering Data and AI Projects with EvidenceManaging Data and AI Products
5tasks in this job
77skills it uses
8tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 3 of 5 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 5 tasks
- 3Appraising options on a common incremental basis against a do-nothing baselineNeeded in 3 of 5 tasks
- 4Verifying benefits realisation and attributionNeeded in 2 of 5 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 5 tasks
ToolsMicrosoft ExcelEsri ArcGIS (ArcGIS Pro, ArcGIS Online)Paragon routing and schedulingPosition GreenMicrosoft Power BIMicrosoft PowerPoint+ 2 more
All 5 tasks, where people get stuck and all 77 skills
Tasks
- Build a defensible business case
- 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
All skills
- Appraising options on a common incremental basis against a do-nothing baseline
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Testing investment business cases under uncertainty
- Verifying benefits realisation and attribution
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- 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
- 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
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- 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 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
- 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 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- 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
- 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
- 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
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonFinancial Modelling and Decision SupportDelivering Data and AI Projects with EvidenceManaging Data and AI Products
6tasks in this job
95skills it uses
8tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 6 tasks
- 2Verifying benefits realisation and attributionNeeded in 2 of 6 tasks
- 3Planning delivery dependencies and the critical pathNeeded in 1 of 6 tasks
ToolsMicrosoft Power BIMicrosoft ExcelEsri ArcGIS (ArcGIS Pro, ArcGIS Online)Paragon routing and schedulingPosition GreenMicrosoft PowerPoint+ 2 more
All 6 tasks, where people get stuck and all 95 skills
Tasks
- Define and govern a KPI metric contract
- Build a defensible business case
- 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
All skills
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- 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
- Building driver-based financial forecasts
- 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 a controlled cash-flow plan
- Constructing clear and honest charts
- 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 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 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
- Governing shared data definitions
- 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 trends and seasonality
- Interpreting workforce metrics and populations
- 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
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- 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
- 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
- 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
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft Power BI
- Microsoft Excel
- Esri ArcGIS (ArcGIS Pro, ArcGIS Online)
- Paragon routing and scheduling
- Position Green
- Microsoft PowerPoint
- Microsoft SharePoint
- Synergi Life
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
Coming soonDelivering Data and AI Projects with EvidenceManaging Data and AI Products
About the role
Directs the whole Operations Function or several Heads and owns its budget and strategy. Sets operating strategy and investment across units and answers to the executive for operational performance. Decides major automation and data investments and the controls every unit runs on.
Also called VP Operations