1task in this job
27skills it uses
2tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 1 of 1 tasks
- 2Appraising options on a common incremental basis against a do-nothing baselineNeeded in 1 of 1 tasks
- 3Running sensitivity and break-even analysis on model assumptionsCore to this role
ToolsMicrosoft ExcelMicrosoft PowerPoint
All 1 tasks, where people get stuck and all 27 skills
Tasks
Where people get stuck
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
All skills
- Appraising options on a common incremental basis against a do-nothing baseline
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Building driver-based financial forecasts
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Eliciting the decision behind an analytical request
- Explaining how business events flow into accounting records
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- 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
- Presenting and defending evidence-based recommendations to decision-makers
- Reading business metrics
- 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
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonFinancial Modelling and Decision Support
7tasks in this job
62skills it uses
6tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 6 of 7 tasks
- 2Appraising options on a common incremental basis against a do-nothing baselineNeeded in 3 of 7 tasks
- 3Running sensitivity and break-even analysis on model assumptionsNeeded in 1 of 7 tasks
ToolsMicrosoft Power BIMicrosoft ExcelPower QueryMicrosoft ProjectMicrosoft PowerPointAnaplan
All 7 tasks, where people get stuck and all 62 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Evaluate a pricing change
- Evaluate an AI use case for suitability, value and risk before adoption
- Build a defensible business case
- Track and verify benefits realisation against the business case
- Model and recommend the annual pay and benefits review budget and guidelines
- Model the workforce plan and organisation design options with cost and people impact
Where people get stuck
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
All skills
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
- Testing investment business cases under uncertainty
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing organisation structure, spans and critical-role cover
- Analysing pay equity with raw and adjusted pay gaps
- Analysing price realisation from list to pocket price
- Analysing working-capital drivers and cash conversion
- Applying research ethics and participant consent
- Assessing data-driven fairness and bias
- Benchmarking jobs to market pay data and designing pay ranges
- Building driver-based financial forecasts
- Choosing an analytical method to fit the question and data
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Comparing pricing scenarios with demand and margin sensitivity
- Constructing a controlled cash-flow plan
- Controlling disclosure risk in shared data and outputs
- Costing a pay review budget and guideline matrix
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Eliciting the decision behind an analytical request
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating jobs with an analytical, gender-neutral scheme
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Forecasting business trends and turning points for planning
- Identifying and quantifying decision downside and exposure
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- 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
- Measuring campaign outcomes with valid denominators
- Modelling workforce demand and supply
- Monitoring and testing data control effectiveness
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Variance analysis
- Verifying benefits realisation and attribution
- Weighing conflicting evidence by design strength
- 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
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
KPI, Variance and Performance Analysis
Explain performance against plan with variance decomposition and driver trees.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
8tasks in this job
82skills it uses
6tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 6 of 8 tasks
- 2Appraising options on a common incremental basis against a do-nothing baselineNeeded in 3 of 8 tasks
- 3Running sensitivity and break-even analysis on model assumptionsNeeded in 1 of 8 tasks
ToolsSAP S/4HANAMicrosoft ExcelMicrosoft Power BIPower QueryMicrosoft ProjectAnaplan
All 8 tasks, where people get stuck and all 82 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Evaluate a pricing change
- Evaluate an AI use case for suitability, value and risk before adoption
- Build a defensible business case
- Automate a recurring reporting or data task with controls and a named owner
- Track and verify benefits realisation against the business case
- Model and recommend the annual pay and benefits review budget and guidelines
- Model the workforce plan and organisation design options with cost and people impact
Where people get stuck
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
All skills
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
- Testing investment business cases under uncertainty
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing organisation structure, spans and critical-role cover
- Analysing pay equity with raw and adjusted pay gaps
- Analysing price realisation from list to pocket price
- Analysing working-capital drivers and cash conversion
- Applying research ethics and participant consent
- Assessing data-driven fairness and bias
- Benchmarking jobs to market pay data and designing pay ranges
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Choosing an analytical method to fit the question and data
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Comparing pricing scenarios with demand and margin sensitivity
- Constructing a controlled cash-flow plan
- Controlling disclosure risk in shared data and outputs
- Costing a pay review budget and guideline matrix
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing forecast accuracy and bias and comparing methods fairly
- 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
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating jobs with an analytical, gender-neutral scheme
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Forecasting business trends and turning points for planning
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- 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 trends and seasonality
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- 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
- Making accountable decisions under uncertainty
- Measuring campaign outcomes with valid denominators
- Modelling workforce demand and supply
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Sampling and reviewing a team's work against standards
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- 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
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
KPI, Variance and Performance Analysis
Explain performance against plan with variance decomposition and driver trees.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
About the role
Accountable for briefing business leaders with financial evidence and challenging their decisions through defensible business cases. Owns the decision conversation for a business area rather than building the models behind it. Data and AI judgement sits in testing whether the evidence supports the decision being made.