Roles Strategy & Transformation
Transformation Director
Directs the whole transformation Function or several Heads and owns its budget and strategy.
3tasks in this job
50skills it uses
4tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 3 tasks
- 2Defining measurable outcomes and leading indicatorsNeeded in 2 of 3 tasks
- 3Planning delivery dependencies and the critical pathNeeded in 2 of 3 tasks
- 4Verifying benefits realisation and attributionNeeded in 2 of 3 tasks
ToolsMicrosoft Power BIPlanviewMicrosoft ExcelMicrosoft PowerPoint
All 3 tasks, where people get stuck and all 50 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Track and verify benefits realisation against the business case
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
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
- Defining measurable outcomes and leading indicators
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Choosing an analytical method to fit the question and data
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- 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
- Evaluating learning transfer and performance outcomes
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Presenting and defending evidence-based recommendations to decision-makers
- 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 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
- Testing, implementing and sustaining measured improvements
- 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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDelivering Data and AI Projects with Evidence
4tasks in this job
62skills it uses
4tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 4 of 4 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 4 of 4 tasks
- 3Defining measurable outcomes and leading indicatorsNeeded in 3 of 4 tasks
- 4Verifying benefits realisation and attributionNeeded in 3 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 2 of 4 tasks
ToolsMicrosoft ExcelPlanviewMicrosoft Power BIMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 62 skills
Tasks
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Track and verify benefits realisation against the business case
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
All skills
- Defining measurable outcomes and leading indicators
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Testing investment business cases under uncertainty
- Verifying benefits realisation and attribution
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Choosing an analytical method to fit the question and data
- 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
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- 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
- Evaluating learning transfer and performance outcomes
- 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 trends and seasonality
- Interpreting workforce metrics and populations
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Presenting and defending evidence-based recommendations to decision-makers
- 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 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
- Testing, implementing and sustaining measured improvements
- 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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision SupportDelivering Data and AI Projects with Evidence
4tasks in this job
62skills it uses
6tools named
The skills this job leans on most
- 1Testing investment business cases under uncertaintyNeeded in 4 of 4 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 4 of 4 tasks
- 3Defining measurable outcomes and leading indicatorsNeeded in 3 of 4 tasks
- 4Verifying benefits realisation and attributionNeeded in 3 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 2 of 4 tasks
ToolsMicrosoft ExcelServiceNowMicrosoft Power BIPlanviewMicrosoft PowerPointQlik Sense
All 4 tasks, where people get stuck and all 62 skills
Tasks
- Build a defensible business case
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Track and verify benefits realisation against the business case
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
All skills
- Defining measurable outcomes and leading indicators
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Testing investment business cases under uncertainty
- Verifying benefits realisation and attribution
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Choosing an analytical method to fit the question and data
- 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
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- 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
- Evaluating learning transfer and performance outcomes
- 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 trends and seasonality
- Interpreting workforce metrics and populations
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Presenting and defending evidence-based recommendations to decision-makers
- 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 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
- Testing, implementing and sustaining measured improvements
- 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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision SupportDelivering Data and AI Projects with Evidence
About the role
Directs the whole transformation Function or several Heads and owns its budget and strategy. Sets transformation direction and funding across portfolios and answers to the executive for realised benefits. Decides which major data, technology and process investments continue and stops those that are failing.
Also called VP Transformation