2tasks in this job
49skills it uses
4tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 2 of 2 tasks
- 2Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 2 tasks
- 3Identifying and quantifying decision downside and exposureNeeded in 1 of 2 tasks
- 4Assessing supplier dependency, data-handling and exit riskNeeded in 1 of 2 tasks
- 5Judging whether uncertainty changes the decisionNeeded in 1 of 2 tasks
ToolsMicrosoft ExcelSAP S/4HANAEcoVadisWorldfavorMicrosoft Power BISAP AribaCoupaMicrosoft PowerPoint
All 2 tasks, where people get stuck and all 49 skills
Tasks
- Assess supplier risk and maintain the supplier risk register
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Identifying and quantifying decision downside and exposure
- Assessing supplier dependency, data-handling and exit risk
- Rating risks against appetite and choosing proportionate treatment
All skills
- Assessing supplier dependency, data-handling and exit risk
- Identifying and quantifying decision downside and exposure
- Presenting and defending evidence-based recommendations to decision-makers
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Constructing clear and honest charts
- Designing and comparing decision scenarios
- 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 fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- 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
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting supplier contract duties, terms and exposure
- Investigating procurement spend leakage and duplicate commitments
- 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
- Monitoring model fitness and drift
- 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
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Writing evidence-led findings and auditable recommendations with their limits
- 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
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
6tasks in this job
68skills it uses
8tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 6 of 6 tasks
- 2Identifying and quantifying decision downside and exposureNeeded in 4 of 6 tasks
- 3Assessing supplier dependency, data-handling and exit riskNeeded in 3 of 6 tasks
ToolsMicrosoft ExcelSAP S/4HANAEcoVadisWorldfavorMicrosoft Power BISAP Ariba+ 2 more
All 6 tasks, where people get stuck and all 68 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Review supplier performance against contract obligations and recommend renewal or remedy
- Assess supplier risk and maintain the supplier risk register
- Develop a category strategy from spend, market and supplier evidence
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Escalating unresolved risks with evidence and clear ownership
- Exercising human oversight of automated and AI outputs
All skills
- Assessing supplier dependency, data-handling and exit risk
- Identifying and quantifying decision downside and exposure
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Analysing supply markets and choosing a sourcing route
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and applying governed tender evaluation criteria
- Designing and comparing decision scenarios
- Designing decision-oriented research with customers, users or employees
- 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 responsible AI use risks
- Evaluating supplier total cost and value
- 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
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting supplier contract duties, terms and exposure
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- 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
- 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
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Testing competing explanations with discriminating evidence
- Variance analysis
- Verifying benefits realisation and attribution
- Writing evidence-led findings and auditable recommendations with their limits
- 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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Procurement, Spend and Supplier Analytics
Analyse spend, contract exposure and supplier value.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision Support
6tasks in this job
86skills it uses
6tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 5 of 6 tasks
- 2Rating risks against appetite and choosing proportionate treatmentNeeded in 5 of 6 tasks
- 3Assessing supplier dependency, data-handling and exit riskNeeded in 3 of 6 tasks
- 4Identifying and quantifying decision downside and exposureNeeded in 2 of 6 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsCore to this role
ToolsSAP AribaSAP S/4HANAEcoVadisMicrosoft PowerPointMicrosoft Power BICoupa
All 6 tasks, where people get stuck and all 86 skills
Tasks
- Maintain a data and AI risk register and record risk treatment and acceptance
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Assess supplier risk and maintain the supplier risk register
- Develop a category strategy from spend, market and supplier evidence
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Identifying and quantifying decision downside and exposure
- Escalating unresolved risks with evidence and clear ownership
All skills
- Assessing supplier dependency, data-handling and exit risk
- Escalating unresolved risks with evidence and clear ownership
- Identifying and quantifying decision downside and exposure
- Rating risks against appetite and choosing proportionate treatment
- Analysing error patterns and failure modes in models and AI systems
- Analysing supply markets and choosing a sourcing route
- 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 lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and applying governed tender evaluation criteria
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- Designing enforceable data governance policies
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- 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 fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- 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
- Governing shared data definitions
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting supplier contract duties, terms and exposure
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- 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
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- 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
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- 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
- 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
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision SupportMachine Learning: from Data to Validated Models and Decisions
About the role
Accountable for procurement across the whole enterprise and for its supplier risk and spend. Holds enterprise accountability for procurement direction and often reports to the CFO or COO. Data and AI judgement sits in how the enterprise governs supplier data and automated buying.
Also called CPO (procurement)