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

Standard number: BS ISO/IEC 5259-4:2024

Pages: 38

Released: 2024-07-24

ISBN: 978 0 539 14984 5

Name: Artificial intelligence. Data quality for analytics and machine learning (ML) Data quality process framework

Status: Standard

Overview

In the rapidly evolving world of artificial intelligence (AI) and machine learning (ML), the quality of data is paramount. The BS ISO/IEC 5259-4:2024 standard provides a comprehensive framework for ensuring data quality in analytics and machine learning processes. This standard is essential for organizations looking to leverage AI and ML technologies effectively and responsibly.

Why Data Quality Matters

Data is the backbone of AI and ML systems. High-quality data ensures that these systems can learn accurately, make reliable predictions, and provide valuable insights. Poor data quality can lead to incorrect conclusions, biased models, and ultimately, flawed decision-making. The BS ISO/IEC 5259-4:2024 standard addresses these challenges by providing a structured approach to managing and improving data quality.

Key Features

• Comprehensive Framework: The standard outlines a detailed process framework for managing data quality in AI and ML applications.
• Best Practices: It includes best practices for data collection, processing, and validation to ensure the highest quality of data.
• Scalability: The framework is designed to be scalable, making it suitable for organizations of all sizes and across various industries.
• Compliance: Adhering to this standard helps organizations comply with regulatory requirements and industry standards.
• Risk Mitigation: By following the guidelines, organizations can mitigate risks associated with poor data quality, such as biased models and inaccurate predictions.

Who Should Use This Standard?

This standard is ideal for:

• Data Scientists: Professionals who develop and train machine learning models will benefit from the structured approach to data quality.
• Data Engineers: Those responsible for data collection, processing, and storage will find the best practices invaluable.
• Compliance Officers: Ensuring that data practices meet regulatory standards is crucial for compliance officers.
• Business Leaders: Executives and managers who rely on data-driven decision-making will appreciate the reliability and accuracy that high-quality data provides.

Benefits of Implementing BS ISO/IEC 5259-4:2024

Implementing the BS ISO/IEC 5259-4:2024 standard offers numerous benefits, including:

• Improved Decision-Making: High-quality data leads to more accurate models and better business decisions.
• Enhanced Trust: Reliable data builds trust among stakeholders, including customers, partners, and regulators.
• Operational Efficiency: Streamlined data processes reduce errors and improve efficiency.
• Competitive Advantage: Organizations that prioritize data quality can gain a competitive edge in the market.
• Risk Reduction: Mitigating risks associated with poor data quality protects the organization from potential financial and reputational damage.

Structure of the Standard

The BS ISO/IEC 5259-4:2024 standard is structured to provide a clear and actionable framework for data quality management. It includes the following sections:

• Introduction: An overview of the importance of data quality in AI and ML.
• Scope: Defines the scope and applicability of the standard.
• Normative References: Lists the references that are essential for the application of the standard.
• Terms and Definitions: Provides clear definitions of key terms used throughout the standard.
• Data Quality Management Process: Outlines the process framework for managing data quality, including data collection, processing, validation, and monitoring.
• Best Practices: Details best practices for ensuring data quality at each stage of the data lifecycle.
• Compliance and Risk Management: Provides guidelines for ensuring compliance with regulatory requirements and managing risks associated with data quality.

Conclusion

The BS ISO/IEC 5259-4:2024 standard is an essential resource for any organization looking to harness the power of AI and ML responsibly and effectively. By providing a comprehensive framework for data quality management, this standard helps organizations ensure that their data is accurate, reliable, and fit for purpose. Whether you are a data scientist, data engineer, compliance officer, or business leader, implementing this standard will help you achieve better outcomes and drive success in your AI and ML initiatives.

BS ISO/IEC 5259-4:2024

This standard BS ISO/IEC 5259-4:2024 Artificial intelligence. Data quality for analytics and machine learning (ML) is classified in these ICS categories:

• 35.020 Information technology (IT) in general