Explore Tasks
Investigate inconsistent BI reporting
A report consumer receives an explanation of why two reports disagree (definition, grain, filter, timing, defect) and which figure to use.
5jobs do it
3jobs help with it
49skills it needs
- Data Analyst
- BI Developer
- Marketing Analyst
- Sales Operations Analyst
- Finance Systems Analyst
- Analytics Lead
- Analytics Manager
- Revenue Operations Lead
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
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Governing shared data definitions
- Writing data quality rules
- Specifying decision-useful reports
- Specifying BI measure semantics: additivity, aggregation and time
- Designing semantic models for governed analytics
- Identifying keys and testing referential integrity
- Designing dimensional models with explicit grain
- Designing and versioning data contracts for consumers
- Mapping stakeholders' decisions, constraints and needs
- Repairing data defects reversibly under owner authority
- Investigating anomalies
- Diagnosing operational handoffs and flow
- Designing efficient and reproducible investigations
- Influencing change without formal authority
- Working through stakeholder conflict over evidence and responsibility
- Facilitating evidence-led retrospectives and corrective actions
- Recognising statutory incident reporting duties and deadlines
- 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