# UK Government Data Quality Framework — ArcKit Reference Guide > **Guide Origin**: Official | **ArcKit Version**: [VERSION] This guide maps the [Government Data Quality Framework](https://www.gov.uk/government/publications/the-government-data-quality-framework/the-government-data-quality-framework) (DQF) to ArcKit commands and artefacts. The DQF is published by the Government Data Quality Hub and provides principles, dimensions, and practical tools for managing data quality across government. --- ## Five Principles | # | DQF Principle | Description | ArcKit Evidence | |---|---------------|-------------|-----------------| | 1 | **Commit to data quality** | Establish accountability and ongoing assessment | `/arckit:data-model` (Data Quality Framework section — owners, targets, monitoring) | | 2 | **Know your users and their needs** | Research quality requirements of data consumers | `/arckit:stakeholders` (data governance roles), `/arckit:data-mesh-contract` (consumer SLAs) | | 3 | **Assess quality throughout the data lifecycle** | Monitor at every stage: collect, store, use, share, archive | `/arckit:data-model` (quality metrics per entity, lifecycle stages) | | 4 | **Communicate data quality clearly** | Transparent, plain-language quality information for consumers | `/arckit:data-mesh-contract` (quality statements, SLA targets, consumer documentation) | | 5 | **Anticipate changes affecting quality** | Plan proactively to prevent quality degradation | `/arckit:risk` (data quality risks), `/arckit:operationalize` (monitoring, alerting) | --- ## Six Quality Dimensions These dimensions are already scaffolded in the `/arckit:data-model` template with per-entity targets, validation rules, and measurement methods. | Dimension | Definition | Template Section | |-----------|-----------|-----------------| | **Completeness** | All required records and values are present | Quality Dimensions → Completeness | | **Uniqueness** | No unnecessary duplication of records | Quality Dimensions → Uniqueness | | **Consistency** | Values align across systems and don't contradict | Quality Dimensions → Consistency | | **Timeliness** | Data reflects current information, available when needed | Quality Dimensions → Timeliness | | **Validity** | Data conforms to expected formats, ranges, and rules | Quality Dimensions → Validity | | **Accuracy** | Data correctly represents real-world conditions | Quality Dimensions → Accuracy | --- ## Four Practical Tools ### 1. Data Quality Action Plans Prioritised improvement steps for critical data issues. The data-model template captures this through: - Quality targets per entity and dimension (gap = target vs current) - Issue classification (Critical/High/Medium/Low) - Resolution process with owner assignment **When to create a formal action plan**: When quality scores consistently fall below targets, or when a new data source is onboarded with unknown quality characteristics. ### 2. Root Cause Analysis Techniques for addressing underlying data quality problems rather than symptoms. | Technique | When to Use | |-----------|------------| | **5 Whys** | Simple causal chains — "why is email accuracy dropping?" | | **Fishbone (Ishikawa)** | Multiple contributing factors — people, process, technology, data sources | | **Pareto Analysis** | Prioritise — which 20% of causes drive 80% of quality issues? | **ArcKit integration**: Record root causes and remediation in `/arckit:risk` (risk register) and track actions in `/arckit:backlog`. ### 3. Metadata Guidance Minimum metadata set for documenting data characteristics. The data-model template captures this through: - Entity catalogue (definitions, data types, keys, constraints) - Data dictionary with attribute-level descriptions - Source system and refresh cadence per entity - Data steward contact per entity/domain ### 4. Data Maturity Model Self-assessment of organisational data quality capability. | Level | Description | Indicators | |-------|-------------|------------| | **Initial** | Ad hoc, reactive quality management | No formal ownership, quality issues discovered by users | | **Repeatable** | Basic processes and ownership defined | Data stewards assigned, some quality rules | | **Defined** | Standardised processes across the organisation | Quality dimensions measured, dashboards in place | | **Managed** | Quantitative quality management with targets | SLAs defined, automated monitoring, regular reporting | | **Optimising** | Continuous improvement, predictive quality | Proactive issue prevention, root cause analysis embedded | **ArcKit evidence**: The data-model template's quality metrics section (overall score, monitoring, alerting) provides evidence for Defined/Managed maturity. The issue resolution process supports Managed/Optimising. --- ## Data Lifecycle Stages The DQF expects quality assessment at every stage of the data lifecycle. | Lifecycle Stage | Quality Focus | ArcKit Artefact | |-----------------|--------------|-----------------| | **Plan** | Define quality requirements and targets | `/arckit:requirements` (DR-xxx data requirements) | | **Collect / Ingest** | Validate at point of entry | `/arckit:data-model` (validation rules, reject/accept logic) | | **Prepare / Store / Maintain** | Cleanse, deduplicate, reconcile | `/arckit:data-model` (deduplication rules, reconciliation process) | | **Use / Process** | Monitor quality during processing | `/arckit:data-model` (quality metrics, dashboards) | | **Share / Publish** | Communicate quality to consumers | `/arckit:data-mesh-contract` (SLAs, quality statements) | | **Archive / Destroy** | Maintain quality of retained data | `/arckit:data-model` (retention policy, disposal procedures) | --- ## Relationship to Other Frameworks | Framework | Relationship to DQF | |-----------|---------------------| | **National Data Strategy** | DQF implements the Data Foundations pillar (Mission 3: transforming government data use) | | **GovS 010: Analysis** | Parent functional standard for analytical quality and data management | | **ISO 8000** | International data quality standard — DQF dimensions align with ISO 8000 | | **DAMA DMBOK** | Industry data management body of knowledge — DQF covers a subset of DAMA quality domains | | **AI Readiness Guidelines** | AI-ready datasets require DQF-level quality assurance | --- ## References - [Government Data Quality Framework](https://www.gov.uk/government/publications/the-government-data-quality-framework/the-government-data-quality-framework) - [Government Data Quality Hub](https://www.gov.uk/government/organisations/government-data-quality-hub/about) - [DQF Guidance](https://www.gov.uk/government/publications/the-government-data-quality-framework/the-government-data-quality-framework-guidance) - [Making Government Datasets Ready for AI](https://www.gov.uk/government/publications/making-government-datasets-ready-for-ai/guidelines-and-best-practices-for-making-government-datasets-ready-for-ai) - [GDS Data Standards](https://www.gov.uk/government/collections/data-standards-for-government)