4tasks in this job
42skills it uses
4tools named
The skills this job leans on most
- 1Presenting and defending evidence-based recommendations to decision-makersNeeded in 3 of 4 tasks
- 2Judging whether uncertainty changes the decisionNeeded in 1 of 4 tasks
- 3Interpreting policy scope and delegated authority limitsNeeded in 1 of 4 tasks
ToolsMicrosoft 365 CopilotMicrosoft Power BIMicrosoft ExcelAnaplanMicrosoft SharePointWorkivaPosition GreenMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 42 skills
Tasks
- Produce an accountable operating budget
- Model cash needs and defend a liquidity action
- 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
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Presenting and defending evidence-based recommendations to decision-makers
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Appraising options on a common incremental basis against a do-nothing baseline
- 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
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Designing and comparing decision scenarios
- Designing auditable decision models
- 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 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 trends and seasonality
- Interpreting workforce metrics and populations
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Making accountable decisions under uncertainty
- 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 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
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
10tasks in this job
92skills it uses
8tools named
The skills this job leans on most
- 1Presenting and defending evidence-based recommendations to decision-makersNeeded in 7 of 10 tasks
- 2Judging whether and how to use AI for a taskNeeded in 4 of 10 tasks
- 3Evaluating fitness and full cost of AI-assisted workNeeded in 3 of 10 tasks
- 4Evaluating responsible AI use risksNeeded in 3 of 10 tasks
- 5Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 10 tasks
ToolsMicrosoft 365 CopilotMicrosoft Power BIMicrosoft ExcelAnaplanMicrosoft SharePointWorkiva+ 2 more
All 10 tasks, where people get stuck and all 92 skills
Tasks
- Evaluate an AI use case for suitability, value and risk before adoption
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Produce an accountable operating budget
- Model cash needs and defend a liquidity action
- 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
- Assure the data behind statutory and sustainability disclosures before sign-off
- 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
- Constructing a controlled cash-flow plan
- Analysing working-capital drivers and cash conversion
- Prioritising work by evidenced value, cost, risk and confidence
All skills
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- Maintaining a risk register with owners, triggers and responses
- Presenting and defending evidence-based recommendations to decision-makers
- 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 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
- 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
- Controlling disclosure risk in shared data and outputs
- 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 auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing testable data controls
- 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
- 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 policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- 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
- Planning delivery dependencies and the critical path
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Protecting sensitive data in AI-assisted work
- 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
- 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
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- 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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- 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
9tasks in this job
92skills it uses
8tools named
The skills this job leans on most
- 1Judging whether and how to use AI for a taskNeeded in 4 of 9 tasks
- 2Evaluating fitness and full cost of AI-assisted workNeeded in 3 of 9 tasks
- 3Evaluating responsible AI use risksNeeded in 3 of 9 tasks
- 4Constructing a controlled cash-flow planNeeded in 2 of 9 tasks
- 5Analysing working-capital drivers and cash conversionNeeded in 2 of 9 tasks
ToolsMicrosoft 365 CopilotMicrosoft ExcelAnaplanMicrosoft Power BIMicrosoft SharePointWorkiva+ 2 more
All 9 tasks, where people get stuck and all 92 skills
Tasks
- Evaluate an AI use case for suitability, value and risk before adoption
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Model cash needs and defend a liquidity action
- 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
- Assure the data behind statutory and sustainability disclosures before sign-off
- 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
- Judging whether and how to use AI for a task
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
All skills
- Constructing a controlled cash-flow plan
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Judging whether and how to use AI for a task
- 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 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
- 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 clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing testable data controls
- 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
- 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 policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- 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 personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Protecting sensitive data in AI-assisted work
- 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
- 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
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Variance analysis
- Verifying benefits realisation and attribution
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft 365 Copilot
- Microsoft Excel
- Anaplan
- Microsoft Power BI
- Microsoft SharePoint
- Workiva
- Position Green
- Microsoft PowerPoint
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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDelivering Data and AI Projects with EvidenceFinancial Modelling and Decision Support
About the role
Accountable for directing the whole finance Function or several Heads and for its budget and strategy. Sets finance strategy for the organisation and holds the Heads who run its operating model to account. Data and AI judgement sits in deciding how finance adopts automation and AI and which financial evidence leaders can trust.
Also called VP Finance