Roles Strategy & Transformation
Chief Transformation Officer
Holds enterprise accountability for transformation and sits on the executive team.
3tasks in this job
50skills it uses
3tools 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 BIMicrosoft 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
- Maintaining a risk register with owners, triggers and responses
- Rating risks against appetite and choosing proportionate treatment
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
69skills it uses
4tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 4 of 4 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 4 tasks
- 3Defining measurable outcomes and leading indicatorsNeeded in 2 of 4 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 2 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 2 of 4 tasks
ToolsMicrosoft Power BIMicrosoft ExcelQlik SenseMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 69 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Maintain a data and AI risk register and record risk treatment and acceptance
- 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
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
All skills
- Defining measurable outcomes and leading indicators
- Maintaining a risk register with owners, triggers and responses
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- 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
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- 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
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- 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 and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- 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
- 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
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 EvidenceManaging Data and AI Products
4tasks in this job
69skills it uses
3tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 4 of 4 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 3 of 4 tasks
- 3Defining measurable outcomes and leading indicatorsNeeded in 2 of 4 tasks
- 4Maintaining a risk register with owners, triggers and responsesNeeded in 2 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 2 of 4 tasks
ToolsMicrosoft Power BIMicrosoft ExcelMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 69 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Maintain a data and AI risk register and record risk treatment and acceptance
- 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
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
All skills
- Defining measurable outcomes and leading indicators
- Maintaining a risk register with owners, triggers and responses
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- 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
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- 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
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- 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 and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- 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
- 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
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 EvidenceManaging Data and AI Products
About the role
Holds enterprise accountability for transformation and sits on the executive team. Answers to the board for whether the enterprise changes as intended and realises the benefits promised. Decides enterprise investment in data, AI and technology change.