Kvaliteediandmete

Kvaliteediandmete refers to metadata that documents the quality characteristics of data assets. It provides structured information about how data meets quality requirements, including attributes such as accuracy, completeness, timeliness, validity, consistency, and integrity. It may also cover data lineage, provenance, confidence, and the methods used to measure or compute quality, as well as quality rules, thresholds, and remediation actions.

Purpose and use: The metadata supports data governance, data catalogs, and data quality workflows by enabling data producers and data consumers to understand the reliability and fitness for use of data assets. It helps with risk assessment, data quality scoring, impact analysis, and decision-making.

Components: Core quality attributes (accuracy, completeness, timeliness, validity, consistency, integrity); quality metrics and scores; measurement methods and sampling plans; data lineage and provenance; data sources and data producers; quality rules, thresholds, alerts, and exception records; remediation and improvement history; access controls and privacy considerations.

Lifecycle: Quality metadata is created or captured during data ingestion or data quality assessments, then updated through ongoing monitoring and remediation, and retired when data assets are deprecated.

Standards and practices: Organizations may align to standards such as ISO 8000 (data quality), ISO 25012/25024 (quality characteristics and measurement), or other domain-specific guidelines, and maintain internal taxonomies within data catalogs.

Impact and limitations: Quality metadata enhances transparency and trust, but requires ongoing maintenance, interoperability across tools, and governance to avoid outdated or incomplete information.