How Data Control Varies Across Standard Setters

Standard setters differ in how directly they oversee their programs. These differences are often reflected in the level of data control they maintain, ranging from minimal oversight to full operational involvement. Understanding these levels helps clarify how responsibilities, visibility, and accountability shift across different approaches to program governance.  

• Low control: Certification bodies (CBs) and licensees manage their data independently, often keeping it in separate systems or even on paper. While this approach minimizes infrastructure and data management for the standard setter, it makes it difficult to access audit information, establish uniform processes, or ensure data consistency. 
• High control: This level takes responsibility for managing key data and processes within their own environment. This results in higher data quality and more timely access to audit-related information. However, the added oversight also means the program owner must maintain these processes, which increases operational responsibility and requires more resources for data management and system upkeep.  
• Maximum control: At the highest level, all processes are integrated within a single ecosystem. Standards, entities, and audit data are managed end to end using one digital platform. This allows for full traceability, top-tier data quality, and consistent workflows across all partners. While this model offers the greatest level of insight and control, it also places full responsibility on the standard setter to own and maintain the ecosystem.