The quality of data used to train ML models significantly affects the quality of analytics and ML-based products and services. Therefore, data quality management is essential to the success of analytics and ML technology.
ISO/IEC 5259-3 specifies the requirements and guidance for establishing, implementing, maintaining and continually improving data quality for analytics and ML. This global standard specifies quality management process requirements and guidance, as well as the reference process and methods that can be tailored to meet ISO/IEC 5259-3 requirements.
It enables organizations to implement and operate data quality measures, management and related processes with sufficient controls throughout the data life cycle.
The requirements and recommendations are generic, to apply to all organizations, whatever their type, size or nature. ISO/IEC 5259-3 does not define detailed processes, methods or measurements.
To achieve high-quality data for analytics and ML, you will need a solid understanding of ISO/IEC 5259-3's requirements. We offer training to provide the required knowledge.
Once the system is in place, we can provide a gap assessment. After the successful completion of an audit, we will issue your certificate, confirming the effective implementation of the standard's requirements.
Our Digital Trust Assurance portfolio also includes comprehensive ISO/IEC 42001 services.
The ISO/IEC 5259-3 certification standard sits alongside the following AI – Data Quality for Analytics and ML standards:
Machine learning (ML) is a field of artificial intelligence (AI) that enables computers to learn from and make predictions or decisions based on data. It involves developing algorithms that identify patterns within datasets and improve their performance over time, without explicit programming for specific tasks. ML spans various domains, including healthcare, finance and autonomous systems.
