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

In the rapidly evolving world of artificial intelligence and machine learning, the quality of data is paramount. Introducing the BS EN ISO/IEC 5259-4:2025, a comprehensive standard that provides a robust framework for ensuring data quality in analytics and machine learning processes. This standard is an essential resource for professionals and organizations striving to maintain high standards of data integrity and reliability.

Key Features of the Standard

• Standard Number: BS EN ISO/IEC 5259-4:2025
• Pages: 38
• Release Date: June 9, 2025
• ISBN: 978 0 539 34304 5
• Status: Standard

Why Data Quality Matters

Data quality is the cornerstone of effective analytics and machine learning. Poor data quality can lead to inaccurate insights, flawed decision-making, and ultimately, a loss of trust in AI systems. The BS EN ISO/IEC 5259-4:2025 standard addresses these challenges by providing a structured approach to managing and improving data quality.

Benefits of Implementing the Standard

By adopting this standard, organizations can:

• Enhance Data Integrity: Ensure that data is accurate, complete, and reliable, which is crucial for generating trustworthy insights.
• Improve Decision-Making: High-quality data leads to better analytics, enabling more informed and effective decision-making.
• Boost Efficiency: Streamline data management processes, reducing the time and resources required to clean and prepare data.
• Increase Trust: Build confidence in AI systems by ensuring that they are based on high-quality data.

Comprehensive Framework for Data Quality

The BS EN ISO/IEC 5259-4:2025 standard outlines a detailed process framework for managing data quality. This framework includes:

1. Data Quality Assessment

Evaluate the current state of data quality within your organization. Identify areas for improvement and establish benchmarks for data quality metrics.

2. Data Quality Improvement

Implement strategies and techniques to enhance data quality. This includes data cleansing, validation, and enrichment processes.

3. Data Quality Monitoring

Continuously monitor data quality to ensure ongoing compliance with established standards. Use automated tools and techniques to detect and address data quality issues in real-time.

4. Data Quality Governance

Establish governance structures and policies to oversee data quality initiatives. Define roles and responsibilities for data quality management within the organization.

Who Should Use This Standard?

The BS EN ISO/IEC 5259-4:2025 standard is designed for a wide range of professionals, including:

• Data Scientists: Ensure that the data used for machine learning models is of the highest quality.
• Data Analysts: Improve the accuracy and reliability of analytics by using high-quality data.
• IT Managers: Implement effective data management practices to support organizational goals.
• Quality Assurance Professionals: Establish and maintain data quality standards across the organization.

Conclusion

In an era where data is a critical asset, maintaining high data quality is essential for the success of any AI or machine learning initiative. The BS EN ISO/IEC 5259-4:2025 standard provides a comprehensive framework for ensuring data quality, helping organizations to achieve better outcomes and build trust in their AI systems. By implementing this standard, you can enhance data integrity, improve decision-making, and drive efficiency across your data-driven processes.

Invest in the future of your data quality management with the BS EN ISO/IEC 5259-4:2025 standard and set your organization on the path to success in the world of artificial intelligence and machine learning.

BS EN ISO/IEC 5259-4:2025

This standard BS EN ISO/IEC 5259-4:2025 Artificial intelligence. Data quality for analytics and machine learning (ML) is classified in these ICS categories:

• 35.020 Information technology (IT) in general