BS ISO/IEC 5259-1:2024 Artificial intelligence. Data quality for analytics and machine learning (ML) Overview, terminology, and examples

Standard number: BS ISO/IEC 5259-1:2024

Pages: 28

Released: 2024-07-08

ISBN: 978 0 539 14982 1

Status: Standard

Overview

In the rapidly evolving field of artificial intelligence (AI) and machine learning (ML), the quality of data is paramount. The BS ISO/IEC 5259-1:2024 standard provides a comprehensive overview of data quality specifically tailored for analytics and machine learning applications. This standard is an essential resource for professionals and organizations aiming to ensure the integrity, accuracy, and reliability of their data.

Key Features

• Comprehensive Terminology: Understand the key terms and definitions related to data quality in the context of AI and ML.
• Detailed Examples: Gain insights through practical examples that illustrate the application of data quality principles in real-world scenarios.
• Structured Overview: Benefit from a well-organized structure that covers all critical aspects of data quality, from data collection to data processing and analysis.

Why Data Quality Matters

Data quality is a critical factor that can significantly impact the performance and outcomes of AI and ML models. Poor data quality can lead to inaccurate predictions, biased results, and ultimately, flawed decision-making. The BS ISO/IEC 5259-1:2024 standard addresses these challenges by providing guidelines and best practices to ensure high-quality data.

Who Should Use This Standard?

This standard is designed for a wide range of professionals, including:

• Data Scientists
• Machine Learning Engineers
• Data Analysts
• AI Researchers
• IT Managers
• Quality Assurance Specialists

Benefits of Implementing BS ISO/IEC 5259-1:2024

By adhering to the guidelines and principles outlined in this standard, organizations can achieve several key benefits:

• Enhanced Data Integrity: Ensure that your data is accurate, complete, and reliable.
• Improved Model Performance: High-quality data leads to more accurate and reliable AI and ML models.
• Reduced Bias: Minimize the risk of biased outcomes by maintaining high data quality standards.
• Regulatory Compliance: Meet industry standards and regulatory requirements related to data quality.
• Informed Decision-Making: Make better business decisions based on reliable and accurate data.

Content Highlights

The BS ISO/IEC 5259-1:2024 standard is divided into several key sections, each focusing on different aspects of data quality:

• Introduction: An overview of the importance of data quality in AI and ML.
• Terminology: Definitions of key terms and concepts related to data quality.
• Data Quality Dimensions: Detailed descriptions of various dimensions of data quality, such as accuracy, completeness, consistency, and timeliness.
• Data Quality Assessment: Guidelines for assessing and measuring data quality.
• Data Quality Management: Best practices for managing and maintaining high data quality standards.
• Case Studies: Real-world examples and case studies that illustrate the application of data quality principles.

Conclusion

The BS ISO/IEC 5259-1:2024 standard is an invaluable resource for anyone involved in the field of artificial intelligence and machine learning. By providing a comprehensive overview of data quality, along with practical examples and detailed terminology, this standard helps ensure that your data is of the highest quality, leading to more accurate and reliable AI and ML models.

Invest in the BS ISO/IEC 5259-1:2024 standard today and take the first step towards achieving excellence in data quality for your AI and ML projects.

BS ISO/IEC 5259-1:2024

This standard BS ISO/IEC 5259-1:2024 Artificial intelligence. Data quality for analytics and machine learning (ML) is classified in these ICS categories:

• 35.020 Information technology (IT) in general
• 01.040.35 Information technology (Vocabularies)