Explore Tasks
Monitor and correct data quality in a domain or system of record and drive remediation at source
The data owner and data consumers of a domain (customer, supplier, product, employee) receive a quality scorecard against agreed rules and a managed issue log with root causes, fixes, owners and trend, with recurring issues proposed for permanent fixes.
7jobs do it
9jobs help with it
39skills it needs
- Data Steward
- Procurement Specialist
- People Operations Lead
- Marketing Operations Lead
- Sales Operations Analyst
- Revenue Operations Lead
- HRIS Analyst
- Data Governance Lead
- Data Governance Manager
- Accounts Payable Specialist
- Category Lead
- Procurement Manager
- HR Advisor
- People Operations Manager
- Revenue Operations Manager
- Finance Systems Analyst
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
- Diagnosing data quality exceptions
- Validating records by matching, recomputation and end-to-end tracing
- Reconciling conflicting reports
- Tracing data lineage upstream and downstream
- Repairing data defects reversibly under owner authority
- Investigating anomalies
- Assigning and operating data ownership and stewardship
- 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
- Designing and validating digital analytics event measurement
- Assessing data source credibility and fitness for a question
- Profiling data
- Standardising and cleaning data under agreed rules
- Governing shared data definitions
- Diagnosing duplicate records
- Writing data quality rules
- 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
- Investigating procurement spend leakage and duplicate commitments
Paths that teach the skills it needs
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
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
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFraming Questions and Sourcing Evidence for Investigations