{"id":435,"count":6,"description":"Data quality is measured by accuracy, completeness, consistency, timeliness, validity, uniqueness, and integrity, and weakness in any one of them undermines every report and model built on top. Governance is the other half: the rules, consent management, and privacy practices that keep data compliant with regulations like the GDPR. Together they decide whether people trust the numbers enough to act on them. These articles cover data cleaning in the pipeline, testing and validating transformations, preventing unlawful or privacy-sensitive data collection, and the ethical foundation behind responsible data and AI. The goal throughout is data that is reliable, compliant, and safe to build on.","link":"https:\/\/i-spark.nl\/en\/blog\/category\/data-quality-governance\/","name":"Data quality & governance","slug":"data-quality-governance","taxonomy":"category","parent":0,"meta":[],"acf":[],"_links":{"self":[{"href":"https:\/\/i-spark.nl\/en\/wp-json\/wp\/v2\/categories\/435","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/i-spark.nl\/en\/wp-json\/wp\/v2\/categories"}],"about":[{"href":"https:\/\/i-spark.nl\/en\/wp-json\/wp\/v2\/taxonomies\/category"}],"wp:post_type":[{"href":"https:\/\/i-spark.nl\/en\/wp-json\/wp\/v2\/posts?categories=435"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}