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SUMMARY:Webinar on Data Quality Metrics for AI-Ready Data
DESCRIPTION:Webinar on Data Quality Metrics for AI-Ready Data \nUnderstanding and using ETSI report (TR 104 180:) on data quality metrics and exploring use of associated open-source validation tool. \nProgram\nWelcome & introductions — Context of the event and the EU–Korea Digital Partnership\n\nETSI TR 104 180 “The Data Quality Metrics Framework”. Scope and objectives; General Principles (Purpose\, Measurable\, Scalable\, Interpretable\, Metadata-aware\, Ethical\, Evolvable); Overview of all 18 metrics\nDeep Dive: Six Core Metrics. Completeness\, Accuracy\, Consistency\, Timeliness\, Reliability\, Uniqueness. Définitions\, formulas\, and practical examples\nPoC Tool Live Demo. Upload a dataset\, configure metrics\, run validation\, interpret results\nThe AI Ready Data Challenge (Seoul\, September 2026)\, how to Participate. Submission process\, expected outcomes\, standardisation impact\nQ&A Session — Open discussion with speakers\n\nWhat You Will Learn\nAfter attending this webinar\, participants will be able to:\n\nDescribe the 18 Data Quality Metrics defined in ETSI TR 104 180 and their mathematical measurement formulas.\nExplain the distinction between intrinsic metrics (e.g. Accuracy\, Completeness\, Uniqueness) and contextual/ethical metrics (e.g. Representation Bias\, Anonymity\, Label Quality).\nUnderstand domain-specific applicability — which metrics matter most for Industrial IoT vs. demographic/social data vs. AI/ML training sets.\nUse the validation tool to upload a dataset and compute quality scores for the six currently implemented metrics (Completeness\, Accuracy\, Consistency\, Timeliness\, Reliability\, Uniqueness).\nPrepare a dataset and a configuration to participate in the AI Ready Data Challenge in Seoul.\n\nWho Should Attend?\nThis webinar is open to and relevant for:\n\nDataset owners and data engineers who wish to understand data quality requirements for AI and data exchange use cases\nAI researchers and practitioners working with training datasets and concerned with bias\, label quality\, and representational fairness\nStandards experts and participants in ETSI\, TTA\, ITU-T\, ISO/IEC JTC 1\, or other standardisation bodies working on data quality\nData space and platform operators interested in data quality as a trust and interoperability enabler\nPolicy and research professionals active in the EU–Korea Digital Partnership or Horizon Europe\n\nREGISTER NOW!
URL:https://inpacehub.eu/ja/event/webinar-on-data-quality-metrics-for-ai-ready-data/
LOCATION:Online
ATTACH;FMTTYPE=image/png:https://inpacehub.eu/wp-content/uploads/2026/07/Webinar-Online.png
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