WEBINAR on Data Quality Metrics for AI-Ready Data.

webinar

Understanding and using ETSI report (TR 104 180:) on data quality metrics and exploring use of associated open-source validation tool.

#data #quality #Standard #AI
08 July 2026 09:30 to 11:00 (Paris time)
Online

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 Program
Welcome & introductions — Context of the event and the EU–Korea Digital Partnership
  • ETSI 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 
  • Deep Dive: Six Core Metrics. Completeness, Accuracy, Consistency, Timeliness, Reliability, Uniqueness. Définitions, formulas, and practical examples
  • PoC Tool Live Demo. Upload a dataset, configure metrics, run validation, interpret results
  • The AI Ready Data Challenge (Seoul, September 2026), how to Participate. Submission process, expected outcomes, standardisation impact
  • Q&A Session — Open discussion with speakers
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What You Will Learn
After attending this webinar, participants will be able to:
  • Describe the 18 Data Quality Metrics defined in ETSI TR 104 180 and their mathematical measurement formulas.
  • Explain the distinction between intrinsic metrics (e.g. Accuracy, Completeness, Uniqueness) and contextual/ethical metrics (e.g. Representation Bias, Anonymity, Label Quality).
  • Understand domain-specific applicability — which metrics matter most for Industrial IoT vs. demographic/social data vs. AI/ML training sets.
  • Use the validation tool to upload a dataset and compute quality scores for the six currently implemented metrics (Completeness, Accuracy, Consistency, Timeliness, Reliability, Uniqueness).
  • Prepare a dataset and a configuration to participate in the AI Ready Data Challenge in Seoul.

Who Should Attend?
This webinar is open to and relevant for:
  • Dataset owners and data engineers who wish to understand data quality requirements for AI and data exchange use cases
  • AI researchers and practitioners working with training datasets and concerned with bias, label quality, and representational fairness
  • Standards experts and participants in ETSI, TTA, ITU-T, ISO/IEC JTC 1, or other standardisation bodies working on data quality
  • Data space and platform operators interested in data quality as a trust and interoperability enabler
  • Policy and research professionals active in the EU–Korea Digital Partnership or Horizon Europe