国际数据治理协会发布《工业企业数据治理“三区一循环”全景架构白皮书》，构建数据治理新范式

Core Insights - The industrial sector is currently experiencing a wave of digital transformation, with challenges such as data silos, quality issues, security risks, compliance pressures, and difficulties in value conversion hindering progress towards intelligent and refined operations [1][2] - The International Data Governance Association (IDGA) has released the "Three Zones and One Cycle" framework white paper, aimed at providing a comprehensive data governance framework for industrial enterprises to transform data from a "cost" to an "asset" in the digital economy [1][2][8] Three Zones and One Cycle Framework - The framework divides data governance into three main areas: Core Governance Zone, Value Output Zone, and Support Assurance Zone, with an intelligent cycle for self-optimization, creating a dynamic and continuously improving governance ecosystem [2][3] Core Governance Zone - This zone serves as the central hub for data governance, covering the entire lifecycle from data generation to application, emphasizing closed-loop management through source control, process control, and comprehensive governance [3] - Source governance focuses on ensuring data compliance at the initial stage of data entry, while end governance ensures data reliability before application [3] Value Output Zone - The Value Output Zone aims to convert high-quality data into business value, facilitating the transition from "controllable" to "usable" and then to "value-added" data [4] - It includes data application services and data knowledge management, promoting standardized data output to support decision-making and business innovation [4] Support Assurance Zone - This zone provides the necessary institutional, organizational, security, and standard support for the data governance system [5] - It recommends establishing a multi-level governance organization and developing governance charters, quality assessment standards, and asset management methods [5] Intelligent Cycle - The intelligent cycle acts as the dynamic engine of the framework, promoting the transition from static management to dynamic optimization through a closed loop of data generation, control, application, knowledge accumulation, and intelligent optimization [6][7] - AI technology plays a crucial role in this cycle, enabling automatic detection and processing of data quality issues and suggesting improvements based on process knowledge [7] Future Outlook - The release of the IDGA white paper marks a new stage in industrial data governance, characterized by systematic, intelligent, and value-driven approaches [8] - The framework aims to address data management challenges and facilitate the continuous evolution of governance systems, allowing industrial enterprises to more effectively unlock data value and gain a competitive edge in the digital landscape [8]