ISO 8000 - Data Quality
Evidence request list. 29 controls, 29 carrying auditor artefact guidance. Generated from the compliance knowledge graph on 11 September 2026. Published by The Art of Service.
Accuracy
Define and exchange master data accuracy metadata so consumers can judge fitness for purpose.
- Accuracy measurement results
- Profiling reports
- Data quality scorecards
- Accuracy measured once, not monitored
- No fitness-for-use statement
Assessment Method
Apply the standard assessment method to score data quality maturity consistently across business units.
- Assessor qualification records
- Evidence binder
- Consolidated rating sheets
- Inconsistent scoring across assessors
- No evidence retention policy
Completeness
Measure and exchange completeness of master data attributes relative to a required attribute set.
- Required attribute lists
- Completeness reports
- Exception logs
- Required attribute set undefined
- Mandatory fields populated with placeholders
DQ Management
Implement the process reference model for data quality management covering planning, control, assurance, and improvement processes.
- DQ process map
- RACI for DQ processes
- Process performance metrics
- Processes exist informally with no documented owners
- No linkage to ISO 9001 QMS
Data Quality Management
ISO 8000-8 information and data quality dimensions. Accuracy, completeness, consistency, timeliness, accessibility, compliance. Data quality requirements specification. Quality measurement. Quality improvement.
- Data quality management plan
- Quality framework document
- Maturity assessment
- Quality dimensions catalog
- Quality not measured
- Framework not adopted across domains
- Maturity stagnant
- Dimension definitions inconsistent
ISO 8000-61 process reference model. ISO 8000-62 maturity assessment. Five maturity levels. Process capability determination. Data governance process. Data quality process. Continuous improvement.
- Data quality management plan
- Quality framework document
- Maturity assessment
- Quality dimensions catalog
- Quality not measured
- Framework not adopted across domains
- Maturity stagnant
- Dimension definitions inconsistent
ISO 8000-100 series. Master data exchange quality. Syntactic quality (ISO 8000-110). Encoding quality (ISO 8000-115). Semantic quality (ISO 8000-120). Completeness (ISO 8000-130). Provenance.
- Data quality management plan
- Quality framework document
- Maturity assessment
- Quality dimensions catalog
- Quality not measured
- Framework not adopted across domains
- Maturity stagnant
- Dimension definitions inconsistent
Establish, implement, and maintain a data quality management system. Data quality policy. Quality objectives. Roles and responsibilities. Resource allocation for data quality.
- Data quality management plan
- Quality framework document
- Maturity assessment
- Quality dimensions catalog
- Quality not measured
- Framework not adopted across domains
- Maturity stagnant
- Dimension definitions inconsistent
Measure data against quality dimensions: accuracy, completeness, consistency, timeliness, validity, uniqueness, and integrity. Define quality thresholds and metrics for each dimension.
- Data quality management plan
- Quality framework document
- Maturity assessment
- Quality dimensions catalog
- Quality not measured
- Framework not adopted across domains
- Maturity stagnant
- Dimension definitions inconsistent
Assess organisational data quality process maturity. Six maturity levels from initial to optimising. Improvement roadmap based on maturity assessment. Benchmarking capabilities.
- Data quality management plan
- Quality framework document
- Maturity assessment
- Quality dimensions catalog
- Quality not measured
- Framework not adopted across domains
- Maturity stagnant
- Dimension definitions inconsistent
Foundations
Establish shared definitions for information and data quality, including syntactic, semantic, and pragmatic dimensions used across the organisation.
- Data quality glossary
- Concept model document
- Cross-team alignment minutes
- Terminology drift across business units
- No semantic layer documented
Framework
Adopt the master data quality management framework covering data architecture, governance, operations, and assurance.
- MDM policy
- Stewardship roles
- Operating model documentation
- No executive sponsor
- Stewardship is informal
Governance
Integrate ISO 8000 requirements into data governance bodies, charters, and decision rights.
- Data governance charter
- Council minutes
- Decision logs
- Governance council inactive
- Decisions not recorded
Identifiers
Use and exchange identifiers that are unique, persistent, and resolvable across systems and trading partners.
- Identifier registry
- Resolution service logs
- Cross-reference tables
- Duplicate identifiers across systems
- No persistence policy
Apply authoritative legal entity identifiers such as LEI in master data to support unambiguous party identification.
- LEI registration records
- Vendor and customer party files
- LEI refresh procedure
- LEI not refreshed annually
- Internal IDs not mapped to LEI
Master Data
Provide overview and architecture for master data quality and exchange across organisational boundaries.
- Master data architecture document
- Exchange use cases
- Interface inventory
- No canonical master data model
- Point-to-point integrations without standards
Ensure master data messages conform to formal syntax, identifier semantics, and data dictionary references.
- XML or JSON schemas
- Data dictionary
- Conformance test reports
- Schema not versioned
- No conformance check before exchange
Master Data and Governance
Quality requirements for master data exchange. Provenance information for master data records. Syntactic and semantic quality requirements. Data portability between systems.
- Master data policy
- Quality measurement dashboard
- Data steward charter
- Improvement roadmap
- No master data ownership
- Quality measured in isolation
- Steward role unfilled
- Improvement not prioritized
Systematic measurement of data quality. Quality indicators and scoring. Automated data quality monitoring. Trend analysis and reporting. Root cause analysis for quality failures.
- Master data policy
- Quality measurement dashboard
- Data steward charter
- Improvement roadmap
- No master data ownership
- Quality measured in isolation
- Steward role unfilled
- Improvement not prioritized
Data quality improvement lifecycle: define, measure, analyse, improve, control (DMAIC). Corrective and preventive actions. Quality management reviews. Integration with business processes.
- Master data policy
- Quality measurement dashboard
- Data steward charter
- Improvement roadmap
- No master data ownership
- Quality measured in isolation
- Steward role unfilled
- Improvement not prioritized
Maturity
Use defined indicators to compute maturity levels for each data quality management process area.
- Indicator catalogue
- Scoring worksheets
- Evidence references
- Indicators chosen ad hoc
- Evidence not tied to indicators
Maturity Assessment
Assess the organisation's data quality management maturity against the defined capability levels and identify improvement actions.
- Maturity assessment report
- Capability scorecards
- Improvement roadmap
- Self-assessment only, no independent review
- No baseline to measure progress
Measurement
Measure data quality management process performance using defined indicators and report results to management.
- DQ KPI definitions
- Measurement reports
- Management review records
- Vanity metrics rather than outcome metrics
- KPIs not reviewed at management level
Monitoring
Continuously monitor data quality against thresholds and trigger remediation when defects exceed limits.
- Monitoring dashboards
- Threshold definitions
- Incident tickets
- Dashboards exist but no thresholds
- No remediation SLA
Overview
Understand the overall structure of the ISO 8000 series and the relationships between its parts.
- Series mapping document
- Applicability statement
- Training records
- Teams unaware of which parts apply
- No mapping to internal policy
Product Data
Apply product data quality requirements in manufacturing supply chains using standardised dictionaries.
- Product classification mapping
- Attribute conformance reports
- Supplier data exchange records
- Free-text descriptions instead of dictionary attributes
- No supplier onboarding checks
Provenance
Capture and exchange provenance metadata for master data, including source, responsible party, and processing history.
- Provenance metadata schema
- Lineage diagrams
- Source-to-target mappings
- Lineage stops at warehouse boundary
- No source attribution captured
Sector Application
Apply the maturity model in manufacturing contexts covering product data, supplier data, and production records.
- Manufacturing data scope statement
- Domain-specific KPIs
- Pilot assessment results
- Product master data excluded from scope
- No tie to MES or ERP records
Vocabulary
Use the standard vocabulary so terms like accuracy, completeness, and consistency carry consistent meaning.
- Internal glossary citing ISO 8000-2
- Training material
- Style guide
- Marketing redefines DQ terms loosely
- Glossary not maintained
Assembled from the framework’s own control set, so this list is regenerated rather than written and stays current as the graph does. See the ISO 8000 - Data Quality framework page.