5tasks in this job
51skills it uses
7tools named
The skills this job leans on most
- 1Designing and comparing decision scenariosNeeded in 2 of 5 tasks
- 2Building driver-based financial forecastsNeeded in 2 of 5 tasks
- 3Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 2 of 5 tasks
ToolsAnaplanJedoxMicrosoft ExcelVenaMicrosoft PowerPointPower QueryMicrosoft Power BI
All 5 tasks, where people get stuck and all 51 skills
Tasks
- Build a scenario-based financial forecast
- Produce an accountable operating budget
- Prepare a periodic performance or risk report for a management or governance committee
- Automate a recurring reporting or data task with controls and a named owner
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Sampling and reviewing a team's work against standards
- Forecasting business trends and turning points for planning
- Giving specific developmental feedback on data and AI work
All skills
- Building driver-based financial forecasts
- Designing and comparing decision scenarios
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Assigning and operating data ownership and stewardship
- Building decision-focused reports and dashboards
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- 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
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying keys and testing referential integrity
- 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 and how to use AI for a task
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Presenting and defending evidence-based recommendations to decision-makers
- Reading charts and dashboards correctly, including their state
- Recognising misleading numerical claims
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Variance analysis
- Writing verifiable requirements and acceptance criteria
Paths to start
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
Forecasting and Scenario Planning
Build and challenge planning forecasts and scenarios.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
KPI, Variance and Performance Analysis
Explain performance against plan with variance decomposition and driver trees.
- Modules
- 5 open
- Practice data
- Raket Parts
8tasks in this job
101skills it uses
10tools named
The skills this job leans on most
- 1Sampling and reviewing a team's work against standardsNeeded in 8 of 8 tasks
- 2Forecasting business trends and turning points for planningNeeded in 5 of 8 tasks
- 3Building driver-based financial forecastsNeeded in 5 of 8 tasks
- 4Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 5 of 8 tasks
- 5Designing and comparing decision scenariosNeeded in 4 of 8 tasks
ToolsMicrosoft Power BIExcel VBA and macrosMicrosoft ExcelAnaplanJedoxVena+ 4 more
All 8 tasks, where people get stuck and all 101 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Build a scenario-based financial forecast
- Produce an accountable operating budget
- Model cash needs and defend a liquidity action
- Prepare a periodic performance or risk report for a management or governance committee
- Automate a recurring reporting or data task with controls and a named owner
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Giving specific developmental feedback on data and AI work
- Writing testable work standards and review checklists
All skills
- Building driver-based financial forecasts
- Designing and comparing decision scenarios
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Forecasting business trends and turning points for planning
- Sampling and reviewing a team's work against standards
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- 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
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- 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 the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing incorrect query and calculation results
- 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
- Establishing and declaring data grain
- 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
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- 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
- Influencing change without formal authority
- 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
- 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading and tracing unfamiliar code
- 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
- Selecting charts for evidence and decisions
- 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 investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Power BI
- Excel VBA and macros
- Microsoft Excel
- Anaplan
- Jedox
- Vena
- SAP S/4HANA
- Microsoft PowerPoint
- Power Query
- Oracle Hyperion and Oracle EPM Cloud (Planning, HFM, FCCS, EPBCS)
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
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
8tasks in this job
101skills it uses
9tools named
The skills this job leans on most
- 1Sampling and reviewing a team's work against standardsNeeded in 8 of 8 tasks
- 2Forecasting business trends and turning points for planningNeeded in 5 of 8 tasks
- 3Building driver-based financial forecastsNeeded in 5 of 8 tasks
- 4Diagnosing forecast accuracy and bias and comparing methods fairlyNeeded in 5 of 8 tasks
- 5Designing and comparing decision scenariosNeeded in 4 of 8 tasks
ToolsSAP S/4HANAOracle NetSuiteAnaplanJedoxMicrosoft ExcelVena+ 3 more
All 8 tasks, where people get stuck and all 101 skills
Tasks
- Investigate variance of actuals against budget or forecast
- Build a scenario-based financial forecast
- Produce an accountable operating budget
- Model cash needs and defend a liquidity action
- Prepare a periodic performance or risk report for a management or governance committee
- Automate a recurring reporting or data task with controls and a named owner
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Giving specific developmental feedback on data and AI work
- Writing testable work standards and review checklists
All skills
- Building driver-based financial forecasts
- Designing and comparing decision scenarios
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Forecasting business trends and turning points for planning
- Sampling and reviewing a team's work against standards
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- 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
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- 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 the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing incorrect query and calculation results
- 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
- Establishing and declaring data grain
- 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
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- 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
- Influencing change without formal authority
- 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
- 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading and tracing unfamiliar code
- 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
- Selecting charts for evidence and decisions
- 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 investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- SAP S/4HANA
- Oracle NetSuite
- Anaplan
- Jedox
- Microsoft Excel
- Vena
- Microsoft PowerPoint
- Power Query
- Microsoft Power BI
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
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
About the role
Accountable for one forecast or budget cycle and its quality without managing a team. Runs the cycle and governs its assumptions but does not set the planning calendar for the function. Data and AI judgement sits in deciding which models and data the forecast can rely on.