Data Quality Framework
Measure, improve and sustain the fitness of data for its intended use.
Methodology
Dimensional quality model (accuracy, completeness, consistency, timeliness, validity, uniqueness) with rules, profiling, scoring and remediation.
Components
Quality dimensions; Data profiling; Quality rules & controls; Quality scoring; Remediation workflow; Quality monitoring.
Governance
Data owners accountable for quality; stewards remediate; data office sets standards and monitors. L1 Initial L2 Developing L3 Defined L4 Managed L5 Optimised Ad hoc Basic, siloed Standardised & Quantified & integrated Predictive & embedded governed
Maturity levels
Implementation roadmap
Diagnose (assess maturity) → Design (tailor framework & governance) → Build (policies, standards, controls, pipelines) → Embed (training, culture, adoption) → Assure (test, benchmark, re-score).
Deliverables
Framework document, governance & RACI, policy/standard templates, maturity score & roadmap, board reporting pack.
Advisory opportunities
Data quality diagnostic; Remediation programme; Quality monitoring build.
Across the Data & AI ecosystem
Knowledge graph · 10 relations