5tasks in this job
65skills it uses
7tools named
The skills this job leans on most
- 1Evaluating supplier total cost and valueNeeded in 4 of 5 tasks
- 2Interpreting supplier contract duties, terms and exposureNeeded in 4 of 5 tasks
- 3Designing and applying governed tender evaluation criteriaNeeded in 2 of 5 tasks
- 4Assessing supplier dependency, data-handling and exit riskNeeded in 2 of 5 tasks
- 5Analysing supply markets and choosing a sourcing routeNeeded in 2 of 5 tasks
ToolsSAP AribaMicrosoft Power BISAP S/4HANACoupaJaggaerMercellMicrosoft Excel
All 5 tasks, where people get stuck and all 65 skills
Tasks
- Review supplier performance against contract obligations and recommend renewal or remedy
- Run controlled competitive-sourcing evaluation and award handoff
- Assess supplier risk and maintain the supplier risk register
- Track and verify benefits realisation against the business case
- Classify spend to the category taxonomy and maintain a trusted spend baseline
Where people get stuck
- Identifying and quantifying decision downside and exposure
- Rating risks against appetite and choosing proportionate treatment
All skills
- Analysing supply markets and choosing a sourcing route
- Assessing supplier dependency, data-handling and exit risk
- Designing and applying governed tender evaluation criteria
- Evaluating supplier total cost and value
- Interpreting supplier contract duties, terms and exposure
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assigning and operating data ownership and stewardship
- Calculating and verifying KPIs against their specification
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Defining measurable outcomes and leading indicators
- Designing and validating digital analytics event measurement
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Making accountable decisions under uncertainty
- Measuring campaign outcomes with valid denominators
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Standardising and cleaning data under agreed rules
- 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 reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- 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
Procurement, Spend and Supplier Analytics
Analyse spend, contract exposure and supplier value.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- Modules
- 5 open
- Practice data
- Raket Parts
8tasks in this job
80skills it uses
6tools named
The skills this job leans on most
- 1Evaluating supplier total cost and valueNeeded in 6 of 8 tasks
- 2Interpreting supplier contract duties, terms and exposureNeeded in 6 of 8 tasks
- 3Identifying and quantifying decision downside and exposureNeeded in 3 of 8 tasks
- 4Designing and applying governed tender evaluation criteriaNeeded in 3 of 8 tasks
- 5Assessing supplier dependency, data-handling and exit riskNeeded in 3 of 8 tasks
ToolsSAP S/4HANACoupaMicrosoft Power BISAP AribaMercellMicrosoft Excel
All 8 tasks, where people get stuck and all 80 skills
Tasks
- Review supplier performance against contract obligations and recommend renewal or remedy
- Run controlled competitive-sourcing evaluation and award handoff
- Investigate procurement spend leakage and duplicate commitments
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Assess supplier risk and maintain the supplier risk register
- Develop a category strategy from spend, market and supplier evidence
- Track and verify benefits realisation against the business case
- Classify spend to the category taxonomy and maintain a trusted spend baseline
Where people get stuck
All skills
- Assessing supplier dependency, data-handling and exit risk
- Designing and applying governed tender evaluation criteria
- Evaluating supplier total cost and value
- Interpreting supplier contract duties, terms and exposure
- Analysing supply markets and choosing a sourcing route
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- 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
- Calculating and verifying KPIs against their specification
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Coordinating accountable handoffs between teams
- Defining measurable outcomes and leading indicators
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- 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 duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Facilitating evidence-led retrospectives and corrective actions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recognising and resolving ethical conflicts in data and AI work
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Sampling and reviewing a team's work against standards
- Standardising and cleaning data under agreed rules
- 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 reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Working through stakeholder conflict over evidence and responsibility
- 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
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
6tasks in this job
80skills it uses
9tools named
The skills this job leans on most
- 1Evaluating supplier total cost and valueNeeded in 6 of 6 tasks
- 2Interpreting supplier contract duties, terms and exposureNeeded in 6 of 6 tasks
- 3Designing and applying governed tender evaluation criteriaNeeded in 3 of 6 tasks
- 4Assessing supplier dependency, data-handling and exit riskNeeded in 3 of 6 tasks
- 5Identifying and quantifying decision downside and exposureNeeded in 2 of 6 tasks
ToolsSAP AribaCoupaSAP FieldglassMercellSAP S/4HANAMicrosoft Power BI+ 3 more
All 6 tasks, where people get stuck and all 80 skills
Tasks
- Review supplier performance against contract obligations and recommend renewal or remedy
- Run controlled competitive-sourcing evaluation and award handoff
- Investigate procurement spend leakage and duplicate commitments
- Assess supplier risk and maintain the supplier risk register
- Develop a category strategy from spend, market and supplier evidence
- Classify spend to the category taxonomy and maintain a trusted spend baseline
Where people get stuck
- Identifying and quantifying decision downside and exposure
- Evaluating supplier total cost and value
- Assessing supplier dependency, data-handling and exit risk
All skills
- Assessing supplier dependency, data-handling and exit risk
- Designing and applying governed tender evaluation criteria
- Evaluating supplier total cost and value
- Interpreting supplier contract duties, terms and exposure
- Analysing supply markets and choosing a sourcing route
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- 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
- Calculating and verifying KPIs against their specification
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Coordinating accountable handoffs between teams
- Defining measurable outcomes and leading indicators
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- 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 duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Facilitating evidence-led retrospectives and corrective actions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recognising and resolving ethical conflicts in data and AI work
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Sampling and reviewing a team's work against standards
- Standardising and cleaning data under agreed rules
- 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 reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- SAP Ariba
- Coupa
- SAP Fieldglass
- Mercell
- SAP S/4HANA
- Microsoft Power BI
- EcoVadis
- Microsoft Excel
- Worldfavor
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
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
About the role
Accountable for supplier contract performance, sourcing governance and a procurement team. Decides sourcing approaches, contract terms and when supplier performance needs action. Data judgement sits in reading supplier performance and spend data to decide where to act.
Also called Purchasing Manager