ISO/IEC 5259-2:2024 PDF

Name: ISO/IEC 5259-2:2024 — Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures
SKU: ISO/IEC 5259-2:2024
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Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures

Название (английский):

ISO/IEC 5259-2:2024

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures

Статус документа:: Действующий
Формат:: Электронный (PDF)
Количество страниц:: 38
Дата публикации:: 5 ноября 2024 г.
Издание:: ISO/IEC IS 5259 edition 1 version 1
ICS:: 35.020

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Описание (английский):

This document specifies a data quality model, data quality measures and guidance on reporting data quality in the context of analytics and machine learning (ML). This document is applicable to all types of organizations who want to achieve their data quality objectives.

Abstract

Похожие стандарты

Стандарты, упомянутые в описании

Overview

ISO/IEC 5259-2:2024 - "Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 2: Data quality measures" defines a standardized data quality model, a catalog of data quality measures, and guidance for reporting data quality specifically in analytics and ML contexts. Applicable to all organization types, it supports consistent assessment and communication of data fitness for ML pipelines, analytics tasks, and data‑driven decision making.

Key topics

Data quality model: structured components and how data quality fits into the analytics and ML data life cycle.

Data quality characteristics: definitions and measures for inherent, system‑dependent, and additional characteristics such as:

Inherent: Accuracy, Completeness, Consistency, Credibility, Currentness
Inherent & system‑dependent: Accessibility, Compliance, Efficiency, Precision, Traceability, Understandability
System‑dependent: Availability, Portability, Recoverability
Additional: Auditability, Balance, Diversity, Effectiveness, Identifiability, Relevance, Representativeness, Similarity, Timeliness

Measurement approach: definition of quality measure elements (property + measurement method) and guidance for designing measurement functions (informative annex).

Implementation and reporting: practical guidance to implement a data quality model for a specific analytics or ML task and a reporting framework to communicate measure results.

Supporting artifacts: UML model, characteristic overviews, and comparison with prior standards (e.g., ISO/IEC 25012).

Practical applications

Establishing repeatable data quality assessments for training/validation data, feature stores, and production inference data.

Defining measurable SLAs and quality gates for data ingestion, labeling, and pre‑processing in ML pipelines.

Supporting model governance, auditing, and regulatory compliance by documenting measures like traceability, auditability, and representativeness.

Informing dataset selection, augmentation, and bias mitigation via measures such as balance, diversity, and representativeness.

Enabling cross‑team or cross‑organization data quality reporting to stakeholders, auditors, and regulators.

Who should use this standard

Data scientists, ML engineers, and data engineers creating, validating, or deploying ML models

Data stewards, data governance teams, and compliance officers

AI assurance/audit teams and organizations seeking standardized data quality benchmarks

Platform and MLOps teams implementing monitoring, data quality gates, and reporting dashboards

ISO/IEC 5259-2:2024 PDF

Abstract

Похожие стандарты

Overview

Key topics

Practical applications

Who should use this standard

Related standards

Технические детали

ISO/IEC 25012:2008

Software engineering — Software product Quality Requirements and Evaluation (SQuaRE) — Data quality model

SIST EN ISO/IEC 5259-1:2025

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 1: Overview, terminolog…

ISO/IEC 25024:2015

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

BS EN ISO/IEC 22989:2023

Information technology. Artificial intelligence. Artificial intelligence concepts and terminology.