Roles Strategy & Transformation
Chief Strategy Officer
Holds enterprise accountability for strategy and sits on the executive team.
4tasks in this job
53skills it uses
4tools named
The skills this job leans on most
- 1Reasoning about systems, feedback and second-order effectsNeeded in 4 of 4 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 4 tasks
- 3Judging whether external evidence applies to the decision contextNeeded in 3 of 4 tasks
- 4Comparing options against explicit decision criteriaNeeded in 3 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIMicrosoft PowerPointMicrosoft ExcelQlik Sense
All 4 tasks, where people get stuck and all 53 skills
Tasks
- Evaluate strategic options with market and performance evidence
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- 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
- Reasoning about systems, feedback and second-order effects
- 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 decision-focused reports and dashboards
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing periods consistently
- Constructing clear and honest charts
- 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
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- 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
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Reconciling conflicting reports
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- 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 benefits realisation and attribution
- 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
53skills it uses
4tools named
The skills this job leans on most
- 1Reasoning about systems, feedback and second-order effectsNeeded in 4 of 4 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 4 tasks
- 3Judging whether external evidence applies to the decision contextNeeded in 3 of 4 tasks
- 4Comparing options against explicit decision criteriaNeeded in 3 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIMicrosoft PowerPointMicrosoft SharePointMicrosoft Excel
All 4 tasks, where people get stuck and all 53 skills
Tasks
- Evaluate strategic options with market and performance evidence
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- 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
- Reasoning about systems, feedback and second-order effects
- 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 decision-focused reports and dashboards
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing periods consistently
- Constructing clear and honest charts
- 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
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- 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
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Reconciling conflicting reports
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- 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 benefits realisation and attribution
- 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
4tasks in this job
53skills it uses
4tools named
The skills this job leans on most
- 1Reasoning about systems, feedback and second-order effectsNeeded in 4 of 4 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 4 tasks
- 3Judging whether external evidence applies to the decision contextNeeded in 3 of 4 tasks
- 4Comparing options against explicit decision criteriaNeeded in 3 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsMicrosoft Power BIMicrosoft PowerPointMicrosoft SharePointMicrosoft Excel
All 4 tasks, where people get stuck and all 53 skills
Tasks
- Evaluate strategic options with market and performance evidence
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
- 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
- Reasoning about systems, feedback and second-order effects
- 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 decision-focused reports and dashboards
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing periods consistently
- Constructing clear and honest charts
- 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
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- 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
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Reconciling conflicting reports
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- 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 benefits realisation and attribution
- 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
About the role
Holds enterprise accountability for strategy and sits on the executive team. Answers to the board for the enterprise's strategic choices and how strategic investment is ranked. Decides which choices reach the board and what evidence they must carry.
Also called CSO (strategy)