Roles Strategy & Transformation
Strategy Lead
Accountable for leading strategic workstreams and the business cases that support enterprise decisions.
2tasks in this job
30skills it uses
6tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 2 of 2 tasks
- 2Appraising options on a common incremental basis against a do-nothing baselineNeeded in 2 of 2 tasks
- 3Running sensitivity and break-even analysis on model assumptionsNeeded in 1 of 2 tasks
ToolsMicrosoft ExcelMicrosoft Power BIQlik SenseMicrosoft PowerPointAnaplanMicrosoft PurviewNielsenIQ (incl. GfK)
All 2 tasks, where people get stuck and all 30 skills
Tasks
Where people get stuck
- Judging whether external evidence applies to the decision context
- Comparing options against explicit decision criteria
- Reasoning about systems, feedback and second-order effects
All skills
- Appraising options on a common incremental basis against a do-nothing baseline
- Running sensitivity and break-even analysis on model assumptions
- 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
- Mapping competitor positions from external evidence
- Presenting and defending evidence-based recommendations to decision-makers
- Reading business metrics
- Structuring ambiguous professional problems
- 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
4tasks in this job
73skills it uses
7tools named
The skills this job leans on most
- 1Judging whether external evidence applies to the decision contextNeeded in 3 of 4 tasks
- 2Reasoning about systems, feedback and second-order effectsNeeded in 3 of 4 tasks
- 3Testing investment business cases under uncertaintyNeeded in 2 of 4 tasks
- 4Comparing options against explicit decision criteriaNeeded in 2 of 4 tasks
- 5Appraising options on a common incremental basis against a do-nothing baselineNeeded in 2 of 4 tasks
ToolsMicrosoft ExcelMicrosoft Power BIQlik SenseMicrosoft PowerPointAnaplanMicrosoft Purview+ 1 more
All 4 tasks, where people get stuck and all 73 skills
Tasks
- Evaluate strategic options with market and performance evidence
- Build a defensible business case
- Answer a commissioned business question with reproducible query-based analysis
- Size a market and assess the competitive landscape
Where people get stuck
- Judging whether external evidence applies to the decision context
- Reasoning about systems, feedback and second-order effects
All skills
- Appraising options on a common incremental basis against a do-nothing baseline
- Comparing options against explicit decision criteria
- Judging whether external evidence applies to the decision context
- Reasoning about systems, feedback and second-order effects
- Testing investment business cases under uncertainty
- 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
- Assessing data source credibility and fitness for a question
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing auditable decision models
- Designing efficient and reproducible investigations
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- 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 market size by triangulating top-down and bottom-up methods
- Estimating quantities and differences with intervals
- Explaining how business events flow into accounting records
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- 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 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
- Making accountable decisions under uncertainty
- Mapping competitor positions from external evidence
- 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 business metrics
- Reading charts and dashboards correctly, including their state
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Structuring ambiguous professional problems
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- 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
Strategic and Commercial Decisions: Options, Trade-offs and Priorities
Frame and decide commercial and strategic options, prioritise portfolios under capacity and review decisions afterwards.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
4tasks in this job
74skills it uses
7tools named
The skills this job leans on most
- 1Judging whether external evidence applies to the decision contextNeeded in 4 of 4 tasks
- 2Reasoning about systems, feedback and second-order effectsNeeded in 4 of 4 tasks
- 3Testing investment business cases under uncertaintyNeeded in 3 of 4 tasks
- 4Comparing options against explicit decision criteriaNeeded in 3 of 4 tasks
- 5Appraising options on a common incremental basis against a do-nothing baselineNeeded in 3 of 4 tasks
ToolsMicrosoft ExcelMicrosoft Power BIQlik SenseMicrosoft PowerPointMicrosoft PurviewAnaplan+ 1 more
All 4 tasks, where people get stuck and all 74 skills
Tasks
- Evaluate strategic options with market and performance evidence
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Size a market and assess the competitive landscape
Where people get stuck
- Judging whether external evidence applies to the decision context
- Reasoning about systems, feedback and second-order effects
All skills
- Appraising options on a common incremental basis against a do-nothing baseline
- Comparing options against explicit decision criteria
- Judging whether external evidence applies to the decision context
- Reasoning about systems, feedback and second-order effects
- Testing investment business cases under uncertainty
- 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
- Assessing data source credibility and fitness for a question
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing auditable decision models
- Designing efficient and reproducible investigations
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- 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 market size by triangulating top-down and bottom-up methods
- Estimating quantities and differences with intervals
- Explaining how business events flow into accounting records
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- 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 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
- Making accountable decisions under uncertainty
- Mapping competitor positions from external evidence
- 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 business metrics
- Reading charts and dashboards correctly, including their state
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Structuring ambiguous professional problems
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft Excel
- Microsoft Power BI
- Qlik Sense
- Microsoft PowerPoint
- Microsoft Purview
- Anaplan
- NielsenIQ (incl. GfK)
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
Strategic and Commercial Decisions: Options, Trade-offs and Priorities
Frame and decide commercial and strategic options, prioritise portfolios under capacity and review decisions afterwards.
- 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
Coming soonFinancial Modelling and Decision Support
About the role
Accountable for leading strategic workstreams and the business cases that support enterprise decisions. Frames questions with executives, directs the analysis and recommends options. Data judgement sits in deciding whether a business case's evidence is strong enough to act on.
Also called Corporate Strategy Lead, Strategy Manager (when the role has no team)