Roles Strategy & Transformation
Head of PMO
Heads a portfolio office and is accountable for portfolio governance, reporting and the prioritisation evidence behind investment.
6tasks in this job
73skills it uses
9tools named
The skills this job leans on most
- 1Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 6 tasks
- 2Writing evidence-led findings and auditable recommendations with their limitsNeeded in 3 of 6 tasks
- 3Testing the completeness and accuracy of reports and data that controls rely onCore to this role
ToolsPlanviewClarity PPMServiceNowMicrosoft SharePointMicrosoft ExcelMicrosoft Power BIMicrosoft PowerPointAntura ProjectsJira
All 6 tasks, where people get stuck and all 73 skills
Tasks
- Control project or programme risks, issues, dependencies and change requests
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Prepare a periodic performance or risk report for a management or governance committee
- Track and verify benefits realisation against the business case
- Assess a project or programme at a stage gate and recommend proceed, hold or stop
- Forecast delivery capacity against portfolio demand and recommend allocation of people and supplier capacity
Where people get stuck
- Defining measurable outcomes and leading indicators
- Prioritising work by evidenced value, cost, risk and confidence
- Planning delivery dependencies and the critical path
All skills
- Presenting and defending evidence-based recommendations to decision-makers
- Writing evidence-led findings and auditable recommendations with their limits
- 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
- 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
- Coordinating accountable handoffs between teams
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Explaining technical work to accountable non-specialists
- Forecasting project cost and schedule from progress evidence
- Giving specific developmental feedback on data and AI work
- 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 policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- 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
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- 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 misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Writing verifiable requirements and acceptance criteria
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
6tasks in this job
79skills it uses
9tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 5 of 6 tasks
- 2Planning delivery dependencies and the critical pathNeeded in 5 of 6 tasks
- 3Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 6 tasks
- 4Writing evidence-led findings and auditable recommendations with their limitsNeeded in 3 of 6 tasks
- 5Defining measurable outcomes and leading indicatorsNeeded in 2 of 6 tasks
ToolsPlanviewServiceNowMicrosoft SharePointMicrosoft ExcelMicrosoft Power BIMicrosoft PowerPoint+ 3 more
All 6 tasks, where people get stuck and all 79 skills
Tasks
- Control project or programme risks, issues, dependencies and change requests
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Prepare a periodic performance or risk report for a management or governance committee
- Track and verify benefits realisation against the business case
- Assess a project or programme at a stage gate and recommend proceed, hold or stop
- Forecast delivery capacity against portfolio demand and recommend allocation of people and supplier capacity
Where people get stuck
All skills
- Defining measurable outcomes and leading indicators
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Writing evidence-led findings and auditable recommendations with their limits
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- 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
- Coordinating accountable handoffs between teams
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Explaining technical work to accountable non-specialists
- Forecasting project cost and schedule from progress evidence
- Giving specific developmental feedback on data and AI work
- 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 policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- 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 entity results and eliminating intercompany balances in consolidation
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- 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
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Writing verifiable requirements and acceptance criteria
All tools
- Planview
- ServiceNow
- Microsoft SharePoint
- Microsoft Excel
- Microsoft Power BI
- Microsoft PowerPoint
- Clarity PPM
- Antura Projects
- Jira
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- 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
6tasks in this job
81skills it uses
9tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 5 of 6 tasks
- 2Planning delivery dependencies and the critical pathNeeded in 5 of 6 tasks
- 3Presenting and defending evidence-based recommendations to decision-makersNeeded in 4 of 6 tasks
- 4Writing evidence-led findings and auditable recommendations with their limitsNeeded in 3 of 6 tasks
- 5Defining measurable outcomes and leading indicatorsNeeded in 2 of 6 tasks
ToolsPlanviewServiceNowMicrosoft SharePointMicrosoft ExcelMicrosoft PowerPointMicrosoft Power BI+ 3 more
All 6 tasks, where people get stuck and all 81 skills
Tasks
- Control project or programme risks, issues, dependencies and change requests
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Prepare a periodic performance or risk report for a management or governance committee
- Track and verify benefits realisation against the business case
- Assess a project or programme at a stage gate and recommend proceed, hold or stop
- Forecast delivery capacity against portfolio demand and recommend allocation of people and supplier capacity
Where people get stuck
All skills
- Defining measurable outcomes and leading indicators
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Writing evidence-led findings and auditable recommendations with their limits
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- 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
- Coordinating accountable handoffs between teams
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Forecasting project cost and schedule from progress evidence
- Giving specific developmental feedback on data and AI work
- 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 policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Interpreting trends and seasonality
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- 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 entity results and eliminating intercompany balances in consolidation
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring and testing data control effectiveness
- 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
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Writing verifiable requirements and acceptance criteria
All tools
- Planview
- ServiceNow
- Microsoft SharePoint
- Microsoft Excel
- Microsoft PowerPoint
- Microsoft Power BI
- Clarity PPM
- Antura Projects
- Jira
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
Evidence-based Briefing and Influence
Brief and influence accountable decision-makers with evidence, its limits and unwelcome findings, and check the message landed.
- 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
About the role
Heads a portfolio office and is accountable for portfolio governance, reporting and the prioritisation evidence behind investment. Manages PMO managers and sets the standards that every programme reports against. Data judgement sits in challenging progress and benefit claims that the data does not support.
Also called Head of Portfolio Office