Standard · Parts 1–4:2024 · Part 5:2025

ISO/IEC 5259 Data Quality for Analytics and Machine Learning

Maintained by ISO/IEC JTC 1/SC 42

What it helps you do

Use ISO/IEC 5259 when you need terminology and examples, data-quality measures, management requirements, a process framework, and a governance framework for analytics and ML data.

  • AI / ML
  • Cross-cutting
PlanAcquireHarmonizeExchangeLearn + reuse

01

Where it fits—and where it doesn’t

Use these four checks before committing implementation time.

Use it when
The quality-management spine for training, validation, and evaluation data used in life-science analytics and ML.
Do not use it as
Do not treat ISO/IEC 5259 as a complete solution on its own. The normative publications are not freely available, the series is cross-domain, and it does not provide life-science thresholds, domain semantics, or regulatory approval.
Best for
Teams working with AI / ML and Cross-cutting data across Plan → Acquire → Harmonize → Exchange → Learn + reuse.
Maturity
ScalingUsable today, with adoption or tooling still scaling; pilot the exact stack you plan to run.

02

See it in the workflow

A standard creates value by changing a handoff, not by existing in a catalog.

  1. InputWhat starts

    AI / ML and Cross-cutting data, metadata, and the local decisions around them

  2. ISO/IEC 5259What changes

    ISO/IEC 5259 applies a shared standard across Plan → Acquire → Harmonize → Exchange → Learn + reuse

  3. OutputWhat becomes possible

    A more consistent, reviewable handoff for the next system or team

Readiness gateBefore scaling: The normative publications are not freely available, the series is cross-domain, and it does not provide life-science thresholds, domain semantics, or regulatory approval.

03

A concrete example

An ML program defines intended-use quality objectives, measures relevant characteristics, governs collection and labels, applies lifecycle processes, records acceptance decisions, and remediates failures.

Why it matters: Directly addresses data quality for analytics and ML across measurement, management, process, and governance rather than treating readiness as metadata completeness alone.

04

What it fits with

DQV can publish measurements; SHACL and domain validators can generate evidence; NIST AI RMF or ISO/IEC 42001 can consume the resulting controls and records.

05

Implementation starter

Start with one bounded handoff. Pin, test, and review it before scaling.

  1. Name an accountable owner and the decision ISO/IEC 5259 must support.

  2. Pin the exact version and companion artifacts: Parts 1–4:2024 · Part 5:2025.

  3. Map one representative input to the required standard artifacts.

  4. Test the result against the canonical source and record every exception.

  5. Preserve the source data, mappings, and review evidence before scaling.

06

Limitation to test first—and the tests that catch it

Risk

The normative publications are not freely available, the series is cross-domain, and it does not provide life-science thresholds, domain semantics, or regulatory approval.

Test

Run one representative end-to-end pilot and record exactly where ISO/IEC 5259 loses context, needs an extension, or depends on another standard.

Risk

A structured or machine-readable result can still be unfit for analysis or AI.

Test

Test the output for missing context, provenance, terminology alignment, time leakage, and the intended downstream decision. Directly addresses data quality for analytics and ML across measurement, management, process, and governance rather than treating readiness as metadata completeness alone.

07

Why we believe this

Checked against a canonical specification, steward registry, or official version history.

Evidence notation: E1. The code is shorthand; the plain-language statement above is the claim.

Formal status
Published International Standards: Parts 1–4:2024 and Part 5:2025
Confidence
High
Review state
Source-checked
Reviewed by
AI data-quality standards reviewer
Last verified
13 July 2026
Review again when
Any series amendment, corrigendum, revision, or sector implementation profile
How the evidence method works

08

Source shelf

Official diagrams, examples, specifications, and explainers. Nothing external loads until you choose to open it.

  • Primary sourceParts 1–4:2024 · Part 5:2025

    ISO/IEC 5259-1 series overview

    The canonical publisher or steward source used to verify this standard profile.

    Publisher
    ISO/IEC JTC 1/SC 42
    Rights
    Rights remain with the publisher; this knowledge base links to the source rather than copying it.
    Access
    Opens the publisher's source in a new tab; no external media loads on this page.
    Verified
    2026-07-13
    Open at source

Next action

Put this profile in context

Compare its role with adjacent standards or place it inside an end-to-end data pathway before choosing an implementation.