Explore Tasks
Classify spend to the category taxonomy and maintain a trusted spend baseline
Category owners receive a reconciled, classified spend baseline with stated coverage and quality.
1job does it
2jobs help with it
51skills it needs
Does itHelps with it
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Designing and validating digital analytics event measurement
- Reading data tables: rows, fields, types and units
- Establishing and declaring data grain
- Combining tables safely: cardinality, fan-out and conservation of totals
- Reading business metrics
- Comparing periods consistently
- Assessing data source credibility and fitness for a question
- Diagnosing data quality exceptions
- Validating records by matching, recomputation and end-to-end tracing
- Reconciling conflicting reports
- Tracing data lineage upstream and downstream
- Calculating and verifying KPIs against their specification
- Assigning and operating data ownership and stewardship
- Reconciling finance subledgers and close records
- Investigating procurement spend leakage and duplicate commitments
- Explaining how business events flow into accounting records
- Evaluating supplier total cost and value
- Coordinating accountable handoffs between teams
- Interpreting supplier contract duties, terms and exposure
- Designing and applying governed tender evaluation criteria
- Classifying spend to a category taxonomy and maintaining a trusted baseline
- Analysing supply markets and choosing a sourcing route
- Recognising and resolving ethical conflicts in data and AI work
- Profiling data
- Standardising and cleaning data under agreed rules
- Governing shared data definitions
- Diagnosing duplicate records
- Writing data quality rules
- Repairing data defects reversibly under owner authority
- Monitoring data quality and freshness in production
- Designing and versioning data contracts for consumers
- Applying data classification and handling rules
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Designing enforceable data governance policies
- Managing master data identities and exceptions
- Influencing change without formal authority
- Writing evidence-led findings and auditable recommendations with their limits
- Making accountable decisions under uncertainty
- Interpreting policy scope and delegated authority limits
Paths that teach the skills it needs
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
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser