AS ISO/IEC 5259-5:2025 identically adopts ISO/IEC 5259-5:2025, which provide a data quality governance framework for analytics and machine learning (ML) to enable governing bodies of organizations to direct and oversee the implementation and operation of data quality measures, management, and related processes with adequate controls throughout the data life cycle (DLC) model according to AS ISO/IEC 5259.1:2024.
Table of contents
Header
About this publication
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Abbreviated terms
5 Data quality governance in the context of analytics and ML
5.1 Foundation
5.2 Ambiguous responsibilities for data
5.3 Purpose and justification
6 Data quality governance framework
6.1 General
6.2 DQ guiding principles
6.3 Strategies and policies for DQ
6.4 Business planning for DQ
6.5 DQ accountabilities
6.6 DQ risk management
6.7 Management processes for DQ
7 Responsibilities of governing body
7.1 Understand the strategic importance of data quality
7.2 Establish enabling environment for data quality governance
7.3 Formulate data quality strategies
7.4 Business planning for data quality
7.5 Data quality risk management capability
7.6 Set policies to ensure data quality
7.7 Establish oversight mechanisms
8 Responsibilities of management
8.1 Implement data quality strategies
8.2 Establish and enforce comprehensive data quality policies
8.3 Implement data quality management processes
8.4 Establishing internal risk control as part of management process
