Roles Strategy & Transformation
Head of Strategy
Accountable for running the strategy discipline: the strategy process, option appraisal and the evidence behind strategic recommendations.
4tasks in this job
46skills it uses
5tools named
The skills this job leans on most
- 1Reasoning about systems, feedback and second-order effectsNeeded in 4 of 4 tasks
- 2Judging whether external evidence applies to the decision contextNeeded in 3 of 4 tasks
- 3Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 4 tasks
- 4Comparing options against explicit decision criteriaNeeded in 2 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIMicrosoft ExcelAnaplanMicrosoft PowerPointQlik Sense
All 4 tasks, where people get stuck and all 46 skills
Tasks
- Evaluate strategic options with market and performance evidence
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Size a market and assess the competitive landscape
- Translate a strategic choice into measurable objectives and targets and track strategic KPIs through the planning cycle
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Judging whether external evidence applies to the decision context
- Reasoning about systems, feedback and second-order effects
All skills
- Comparing options against explicit decision criteria
- Judging whether external evidence applies to the decision context
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- 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 cohort and retention analyses with correct denominators
- Communicating quantified uncertainty
- Comparing periods consistently
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- Designing semantic models for governed analytics
- 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
- 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 customer feedback and qualitative evidence
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping competitor positions from external evidence
- 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
- Recognising and resolving ethical conflicts in data and AI work
- Reconciling conflicting reports
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Structuring ambiguous professional problems
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- 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
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 soonDelivering Data and AI Projects with Evidence
4tasks in this job
46skills it uses
5tools named
The skills this job leans on most
- 1Reasoning about systems, feedback and second-order effectsNeeded in 4 of 4 tasks
- 2Judging whether external evidence applies to the decision contextNeeded in 3 of 4 tasks
- 3Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 4 tasks
- 4Comparing options against explicit decision criteriaNeeded in 2 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsMicrosoft ExcelMicrosoft Power BIQlik SenseMicrosoft PowerPointAnaplan
All 4 tasks, where people get stuck and all 46 skills
Tasks
- Evaluate strategic options with market and performance evidence
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Size a market and assess the competitive landscape
- Translate a strategic choice into measurable objectives and targets and track strategic KPIs through the planning cycle
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
- Comparing options against explicit decision criteria
- Judging whether external evidence applies to the decision context
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- 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 cohort and retention analyses with correct denominators
- Communicating quantified uncertainty
- Comparing periods consistently
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- Designing semantic models for governed analytics
- 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
- 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 customer feedback and qualitative evidence
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping competitor positions from external evidence
- 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
- Recognising and resolving ethical conflicts in data and AI work
- Reconciling conflicting reports
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Structuring ambiguous professional problems
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- 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
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 soonDelivering Data and AI Projects with EvidenceManaging Data and AI Products
5tasks in this job
60skills it uses
6tools named
The skills this job leans on most
- 1Reasoning about systems, feedback and second-order effectsNeeded in 5 of 5 tasks
- 2Testing investment business cases under uncertaintyNeeded in 4 of 5 tasks
- 3Judging whether external evidence applies to the decision contextNeeded in 4 of 5 tasks
- 4Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 5 tasks
- 5Comparing options against explicit decision criteriaNeeded in 3 of 5 tasks
ToolsMicrosoft Power BIMicrosoft PowerPointAnaplanQlik SenseMicrosoft ExcelMicrosoft SharePoint
All 5 tasks, where people get stuck and all 60 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
- Translate a strategic choice into measurable objectives and targets and track strategic KPIs through the planning cycle
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Judging whether external evidence applies to the decision context
- Reasoning about systems, feedback and second-order effects
All skills
- Comparing options against explicit decision criteria
- Judging whether external evidence applies to the decision context
- Prioritising work by evidenced value, cost, risk and confidence
- Reasoning about systems, feedback and second-order effects
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- 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 cohort and retention analyses with correct denominators
- Building driver-based financial forecasts
- Communicating quantified uncertainty
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing semantic models for governed analytics
- 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
- Explaining how business events flow into accounting records
- Facilitating group decisions using disputed evidence
- Forecasting business trends and turning points for planning
- 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 customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping competitor positions from external evidence
- Mapping stakeholders' decisions, constraints and needs
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Recognising and resolving ethical conflicts in data and AI work
- Reconciling conflicting reports
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Structuring ambiguous professional problems
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- 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
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 SupportDelivering Data and AI Projects with Evidence
About the role
Accountable for running the strategy discipline: the strategy process, option appraisal and the evidence behind strategic recommendations. Recommends which options go to the executive and how they compare. Manages strategy managers and decides what evidence strategic choices need and how data and AI change the options.