Information Technology Standards Summary – May 2025 Monthly Overview (Part 4 of 7)

Looking back at May 2025, the Information Technology and Office Equipment sector saw the publication of several influential standards shaping contemporary practice in artificial intelligence, software engineering, and immersive digital media. Five standards were released—three focusing on AI data quality, one on build and deployment tools, and another advancing immersive media scene description. For industry professionals, this overview provides retrospective analysis, contextual themes, and practical recommendations for integrating these standards into current operations. Staying informed about such developments is crucial for maintaining regulatory compliance, driving technology performance, and managing risk in an era of rapid digital transformation.

Monthly Overview: May 2025

May 2025 was marked by a cohesive shift toward data governance, automation, and interoperability across the Information Technology and Office Equipment landscape. The publication cadence aligned with increasing industry demand for reliability and transparency in artificial intelligence and analytics, as evidenced by three related standards in the ISO/IEC 5259 series. Additionally, the sector saw the introduction of comprehensive requirements for software build and deployment tools—reflecting the continuous integration and delivery trends reshaping software engineering. The release of a new part in the ISO/IEC 23090 immersive media series further signals ongoing prioritization of advanced multimedia experiences and conformance in next-generation digital content.

Compared with prior months, May's emphasis on data quality within AI is particularly notable, addressing mounting regulatory and ethical expectations. The balance of standards points to an industry grappling with both operational rigor and technological innovation.

Standards Published This Month

EN ISO/IEC 5259-2:2025 - Artificial Intelligence – Data Quality for Analytics and Machine Learning (ML) – Part 2: Data Quality Measures

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

Published as part of a coordinated initiative on AI data governance, EN ISO/IEC 5259-2:2025 defines a robust data quality model and standardized measures relevant for analytics and machine learning workflows. The standard provides organizations with tools and terminology to assess and communicate data quality across the analytics lifecycle. Key clauses address components such as accuracy, completeness, consistency, credibility, timeliness, and auditability. Reporting guidance ensures that data provenance and quality metrics are not only measured but also transparently documented for governance, compliance, and assurance.

Organizations of all types that leverage data for AI and analytics are within the scope—benefiting sectors ranging from finance and healthcare to retail and logistics. By formalizing metrics and reporting, the standard facilitates cross-industry comparability and supports audits of data-driven decision making. The inclusion of mappings and comparisons to existing standards such as ISO/IEC 25012 underlines its integrative approach.

Key highlights:

• Offers an explicit set of data quality characteristics and corresponding measures
• Provides a framework for implementing data quality assessments and reports
• Designed for universal applicability across industries and use cases

Access the full standard:View EN ISO/IEC 5259-2:2025 on iTeh Standards

EN ISO/IEC 5259-3:2025 - Artificial Intelligence – Data Quality for Analytics and Machine Learning (ML) – Part 3: Data Quality Management Requirements and Guidelines

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 3: Data quality management requirements and guidelines (ISO/IEC 5259-3:2024)

Expanding on the foundation of Part 2, EN ISO/IEC 5259-3:2025 defines management system requirements and broad-based guidance for establishing, maintaining, and improving data quality for analytics and machine learning. Where Part 2 focuses on measures, this part addresses the organizational and procedural aspects necessary to achieve data quality objectives.

The standard delineates generic, adaptable requirements suitable for varied organizational contexts. It outlines reference processes that can be tailored to sectoral or enterprise-specific needs, encompassing aspects such as data quality culture, competence management, audit, and integration with existing management systems. Notably, the guidance does not prescribe specific metrics but rather centers on process rigor, verification, and continuous improvement mechanisms—providing a foundation for sustainable data governance in AI-intensive environments.

Key highlights:

• Establishes requirements for data quality management processes
• Emphasizes auditability, competence, and integration with management systems
• Supports tailored implementation across organizational contexts

Access the full standard:View EN ISO/IEC 5259-3:2025 on iTeh Standards

EN ISO/IEC 5259-4:2025 - Artificial Intelligence – Data Quality for Analytics and Machine Learning (ML) – Part 4: Data Quality Process Framework

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 4: Data quality process framework (ISO/IEC 5259-4:2024)

Completing the triad of AI data standards published in May, EN ISO/IEC 5259-4:2025 provides a detailed process framework for achieving and sustaining high data quality across diverse machine learning paradigms—including supervised, unsupervised, semi-supervised, and reinforcement learning. The document prescribes common organizational approaches for data labelling, acquisition, preparation, evaluation, and decommissioning, enabling consistency whether organizations operate in regulated sectors or dynamic tech fields.

This process-centric perspective is particularly relevant for organizations managing complex data pipelines or sourcing data from diverse origins. While not referring to platform-specific solutions, its structured guidance helps in harmonizing quality assurance from data ingestion to deployment, bridging operational divides across the AI/ML lifecycle.

Key highlights:

• Outlines end-to-end data quality process steps for diverse ML approaches
• Focuses on roles, labelling workflows, and multi-source data integration
• Enables harmonized quality management across distributed teams

Access the full standard:View EN ISO/IEC 5259-4:2025 on iTeh Standards

ISO/IEC 20582:2025 - Software and Systems Engineering – Capabilities of Build and Deployment Tools

Software and systems engineering – Capabilities of build and deployment tools

ISO/IEC 20582:2025 addresses a growing operational need for clear and standardized evaluation criteria for software build and deployment tools—key enablers of agile, DevOps, and CI/CD practices. The standard enumerates capabilities that tools must deliver to automate, register, execute, and confirm build, packaging, and deployment steps throughout the software development lifecycle.

Key sections detail object models for entities in build and deployment, dependency management, and process registration—supporting fair and transparent evaluation of commercial and open-source toolchains. The standard is intended to be used alongside ISO/IEC 20741, providing complementary procedural guidance. Critically, it aligns with multi-methodology environments (Waterfall, Agile, DevOps), making it relevant for a wide range of IT and engineering teams seeking improved automation, traceability, and consistency in their deployment pipelines.

Key highlights:

• Defines capability requirements for selection and evaluation of build/deployment tools
• Integrates with ISO/IEC 20741 for broader tool assessment
• Facilitates interoperability in complex, multi-vendor environments

Access the full standard:View ISO/IEC 20582:2025 on iTeh Standards

ISO/IEC DIS 23090-24 - Information Technology – Coded Representation of Immersive Media – Part 24: Conformance and Reference Software for Scene Description

Information technology - Coded representation of immersive media - Part 24: Conformance and reference software for scene description

ISO/IEC DIS 23090-24 brings a practical dimension to the rapidly evolving field of immersive media, specifying conformance and providing reference software for scene description, crucial for ensuring interoperability and fidelity in virtual and augmented reality content. This standard supports implementers by clarifying and validating aspects of ISO/IEC 23090-14 (scene description) through a Python-based reference implementation (pympegsd), integration with OpenXR, support for spatial audio, video textures, and a range of validation and conformance testing procedures.

Media technology providers, VR/AR solution architects, and multimedia application developers will benefit from the conformance guidance, test vectors, reference code, and comprehensive documentation facilitating consistent scene representation across platforms and devices. As immersive applications become more pervasive, this standard is vital for ensuring user experience quality and technology harmonization.

Key highlights:

• Delivers reference software for implementing MPEG-I scene descriptions
• Provides comprehensive conformance testing and validation strategies
• Facilitates cross-platform support for immersive and interactive content

Access the full standard:View ISO/IEC DIS 23090-24 on iTeh Standards

Common Themes and Industry Trends

Analyzing the standards published in May 2025, several industry themes and patterns emerge:

• Data Quality in AI/Analytics: The trio of EN ISO/IEC 5259 standards demonstrates a focused industry effort to operationalize data quality across the analytics and machine learning value chain. With increasing AI adoption, quality management is now viewed as a foundational concern, driving the publication of harmonized, end-to-end guidance on measures, management, and processes.
• Standardized Automation Tooling: ISO/IEC 20582:2025 reflects a mature, systems engineering approach to automation infrastructure. As software delivery becomes more distributed and rapid, objective evaluation of build/deployment tools underpins stable, secure, and efficient operations.
• Immersive Media Conformance: The ongoing expansion of ISO/IEC 23090 signals both consumer and industry appetite for interoperable, high-fidelity digital environments. Conformance and validation are now non-negotiable elements as organizations scale VR/AR content, underscoring the necessity for reference implementation and comprehensive testing.

These standards are highly relevant to sectors with strong AI/ML adoption (e.g., healthcare, manufacturing, finance), software product companies, and any organization with investments in immersive digital experiences.

Compliance and Implementation Considerations

For organizations affected by these standards, the following considerations are paramount:

• Prioritize organizational alignment with the EN ISO/IEC 5259 series for all analytics/ML projects. Implement measures for data quality assessment, develop quality management processes, and embed quality practices into all operational stages.
• Review current build and deployment toolchains to benchmark capabilities against ISO/IEC 20582:2025. Use this standard to drive procurement, tool selection, and internal audits—especially when restructuring DevOps pipelines or scaling CI/CD practices.
• For media tech teams, integrate conformance testing as per ISO/IEC DIS 23090-24 to ensure that immersive scene descriptions are both standards-compliant and interoperable. Test adoption processes using the provided reference software to streamline integration with existing solutions.
• Establish cross-functional training on new requirements and foster a culture of quality and compliance. Consider leveraging existing ISO/IEC training and documentation resources as foundational materials.
• Monitor compliance timelines: As most standards allow for a grace period, organizations should assess the practical implications and set realistic milestones for phased implementation—especially for new or revised requirements.

Conclusion: Key Takeaways from May 2025

May 2025 was a transformative month for the Information Technology and Office Equipment sector, with five pivotal standards influencing the intersection of AI data governance, automation tooling, and immersive media. The EN ISO/IEC 5259 AI data quality suite stands out for its comprehensive, lifecycle-oriented approach, while the publication of ISO/IEC 20582 and ISO/IEC DIS 23090-24 demonstrate the critical role of operational excellence and interoperability in modern technology ecosystems.

For industry professionals, the ability to anticipate, interpret, and act upon these evolving standards is central to competitive advantage, regulatory preparedness, and risk mitigation. Organizations are encouraged to delve into the full texts, leverage the provided best practices, and prioritize early compliance initiatives. By doing so, they can drive innovation while ensuring robust, standardized operations in an increasingly complex technology landscape.

For direct access to all covered standards and further guidance, visit iTeh Standards—your comprehensive resource for up-to-date global standards.