ISO/IEC TR 5259-6:2026 PDF

Name: ISO/IEC TR 5259-6:2026 — Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 6: Visualization framework for data quality
SKU: ISO/IEC TR 5259-6:2026
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Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 6: Visualization framework for data quality

Название (английский):

ISO/IEC TR 5259-6:2026

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 6: Visualization framework for data quality

Статус документа:: Действующий
Формат:: Электронный (PDF)
Количество страниц:: 19
Дата публикации:: 4 мая 2026 г.
Издание:: ISO/IEC TR 5259 edition 1 version 1
ICS:: 35.020

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Описание (английский):

This document describes a visualization framework for data quality in analytics and machine learning (ML). The aim is to enable stakeholders using visualization methods to assess the results of data quality measures. This visualization framework supports data quality goals.

Abstract

Похожие стандарты

Стандарты, упомянутые в описании

Overview

ISO/IEC TR 5259-6:2026 sets a global framework for the visualization of data quality within analytics and machine learning (ML) applications. Published by ISO and developed by ISO/IEC JTC 1/SC 42, this technical report provides practical guidance on leveraging visualization methods to effectively assess and communicate data quality measures. The framework is designed to aid a diverse range of stakeholders - including AI producers, providers, developers, users, and regulators - in understanding, evaluating, and improving data quality across an AI or ML data management life cycle.

The standard enhances trust and transparency in artificial intelligence by making data quality results tangible, actionable, and accessible. By integrating visualization into data quality management processes, organizations can drive clearer insights, identify data issues, and make informed decisions that elevate the performance and reliability of AI and ML systems.

Key Topics

Data Quality Management Life Cycle Aligns visualization practices with every stage of the data quality management life cycle (DQMLC), supporting continuous validation and verification.

Stakeholder Perspectives Supports the different viewpoints of AI system stakeholders, including those involved in the creation, deployment, use, and regulation of AI applications.

Dataset Properties and Context Emphasizes understanding dataset characteristics-statistical properties, source, structure-which influence the choice and impact of quality visualizations.

Data Quality Models and Characteristics Outlines common data quality characteristics such as accuracy, completeness, consistency, relevance, and timeliness, based on ISO/IEC 25024 and other standards.

Data Quality Measures and Assessment Encourages quantifying and visualizing measures such as semantic accuracy, attribute completeness, and risk of inaccuracy to support informed evaluation.

Visualization Methods and Considerations Discusses practical visualization techniques (e.g., bar charts, box plots, radar charts, dashboards) and best practices for presenting data quality.

Applications

AI and ML Development Visualization frameworks are critical for AI/ML teams to communicate data quality findings throughout the development cycle, from data preparation to model validation.

Regulatory Compliance Helps organizations demonstrate data quality controls, supporting compliance with industry regulations and guidelines.

Stakeholder Communication Provides stakeholders-including technical, business, and regulatory audiences-with clear, graphical insights into data quality, supporting trust and transparency.

Data Quality Reporting Streamlines the documentation of data quality management processes by integrating visual summaries, making reporting more engaging and effective.

Exploratory Data Analysis Enables identification of common data quality issues, such as missing values, outliers, or inconsistencies, facilitating faster and deeper exploratory analysis.

Risk Assessment and Decision Support Allows users to visualize and act upon data quality risks, supporting more robust, evidence-based decision-making in AI deployment.

Related Standards

The visualization framework in ISO/IEC TR 5259-6:2026 is closely connected to other core standards within the ISO/IEC 5259 series and supporting standards in AI and data quality:

ISO/IEC 5259-1:2024: Defines general concepts, terminology, and examples for data quality in analytics and ML.

ISO/IEC 5259-2:2024: Details data quality models and characteristics.

ISO/IEC 5259-3:2024: Outlines the data quality management life cycle, synchronizing with visualization processes.

ISO/IEC 22989:2022: Provides foundational concepts and terminology for artificial intelligence.

ISO/IEC 25024 and ISO 8000 series: Specify detailed data quality characteristics and assessment methods.

ISO/IEC 5339:2024: Describes stakeholder roles and perspectives in artificial intelligence applications.

ISO/IEC 23751: Covers dataset properties and data sharing in cloud computing and distributed platforms.

Adhering to these standards ensures that organizations can implement best practices in data quality visualization, supporting innovation and enhancing the trustworthiness of AI and ML solutions.

ISO/IEC TR 5259-6:2026 PDF

Abstract

Похожие стандарты

Overview

Key Topics

Applications

Related Standards

Технические детали

ISO/IEC 25024:2015

Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Measur…

SIST EN ISO/IEC 5259-4:2025

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 4: Data quality process…

ISO 8000-51:2023

Data quality — Part 51: Data governance: Exchange of data policy statements

BS ISO/IEC 23751:2022

Information technology. Cloud computing and distributed platforms. Data sharing agreement (DSA) framework.