Core Insights - The release of the "Industrial Enterprises Secondary Data Governance Practice Guide White Paper" by the International Data Governance Association (IDGA) marks a new phase in data governance within China's industrial sector [1][3] - The white paper consolidates insights from over twenty leading industry companies and more than forty experts, providing a systematic methodology and practical pathways for secondary data governance in industrial enterprises [1][3] Challenges in Data Governance - Industrial enterprises face three core challenges in data governance: lack of a cohesive governance framework, persistent data quality issues, and insufficient coverage of core business data resources [3][4] - The IDGA expert committee emphasizes that data governance is a long-term endeavor, requiring strategic approaches to address complex data issues accumulated over years [3] Four-Dimensional Governance System - The white paper proposes an innovative "strategy-standard-technology-application" four-dimensional governance system to address existing pain points [3][4] - It recommends a five-year strategic plan with annual rolling planning to translate strategy into actionable tasks, alongside a unified standard system for comprehensive lifecycle management [3][4] Technological Support - The white paper highlights the introduction of an external data quality automatic verification platform, which employs a dual-layer architecture for comprehensive data lifecycle management [4] - This platform utilizes an intelligent routing mechanism to dynamically apply differentiated verification rules based on data types and business scenarios [4] Innovative Governance Framework - The most innovative contribution of the white paper is the "three zones and one loop" panoramic governance framework, which systematically illustrates the entire process from data generation to value release [5][6] - This framework aims to achieve the goals of "controllable, usable, and value-added" data [5] Optimization Strategies - For enterprises with existing master data management platforms, the white paper provides specific optimization strategies, including establishing cross-departmental governance committees and enhancing data quality management [6] - It introduces a data quality scoring system that incorporates key performance indicators (KPIs) related to master data completeness and accuracy [6] Enhancing Data Value - The white paper advocates for a shift from merely visualizing data metrics to enhancing data application value through comprehensive management practices [7] - It proposes a five-level directory structure for data resource inventory, combining top-down and bottom-up approaches [7] Organizational and Talent Support - The white paper emphasizes the critical role of organization and talent in data governance, recommending the establishment of a data governance office led by the Chief Information Officer [9] - It suggests a three-dimensional training system for talent development, including internal and external training, and collaboration with academic institutions [9] Expected Outcomes - The white paper anticipates significant improvements in data standardization, quality enhancement, business process optimization, and data-driven decision-making through systematic implementation of secondary data governance [10] - Specific targets include achieving over 98% accuracy in data consistency checks and significantly reducing approval cycles in core business processes [10]