{"id":3441,"count":18,"description":"Data is only useful when it is understood. This category covers the full spectrum of working with data in a professional digital context: collection, cleaning, transformation, analysis, visualisation, and interpretation. Content draws on practical experience with tools such as Python (pandas, NumPy, SQLite), Google Analytics 4, Streamlit dashboards, and FAISS vector stores. Articles address both the technical mechanics \u2014 writing efficient queries, building data pipelines, structuring outputs for downstream use \u2014 and the interpretive challenge of turning numbers into decisions. A recurring theme is the gap between data availability and data literacy: organisations often have more data than they know what to do with, and the real skill lies in asking the right questions. Topics include GA4 event tracking, AI feature adoption measurement, procurement scoring systems, semantic enrichment with Wikidata, and the design of review interfaces that make complex data accessible to non-technical collaborators.","link":"https:\/\/flaven.fr\/category\/data-analytics\/","name":"Data & Analytics","slug":"data-analytics","taxonomy":"category","parent":0,"meta":[],"_links":{"self":[{"href":"https:\/\/flaven.fr\/happy-api\/wp\/v2\/categories\/3441","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/flaven.fr\/happy-api\/wp\/v2\/categories"}],"about":[{"href":"https:\/\/flaven.fr\/happy-api\/wp\/v2\/taxonomies\/category"}],"wp:post_type":[{"href":"https:\/\/flaven.fr\/happy-api\/wp\/v2\/posts?categories=3441"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}