Core Viewpoint - The "2025 Mining Industry Data Governance White Paper" released by Tencent Cloud and Yunding Technology provides a comprehensive analysis of the current state, challenges, strategies, trends, and case studies in data governance within the mining industry, aiming to guide digital transformation in the sector [1][2]. Current State of Data Governance - The mining industry faces a complex data governance environment characterized by a vast amount of heterogeneous data generated from smart mining initiatives, with varying levels of data governance maturity among companies [1][2]. - Data resources are abundant, but the conversion into usable resources is limited, leading to underutilization of data value and prevalent data silos [1][2]. - There is a significant shortage of data governance talent, and the uncertainty of funding investment returns hampers the progress of data governance initiatives [1][2]. Strategies to Address Challenges - Promote digital organizational transformation by establishing a multi-level data governance structure with clear responsibilities [1][2]. - Cultivate a digital talent pool focusing on recruitment, retention, and utilization across four dimensions [1][2]. - Build a unified data foundation to integrate data and provide services, addressing data processing challenges with Tencent's products [1][2]. - Accelerate the establishment of data standards covering various business models and lifecycle management [1][2]. - Implement strict data quality controls and establish a quality control system and verification rules [1][2]. - Break down data supply and demand barriers by creating resource directories and expanding application scenarios [1][2]. - Empower data elements to drive business decisions and enhance production efficiency [1][2]. Development Trends - Traditional miners are transitioning to "new-type miners" with multi-disciplinary knowledge [2]. - Data governance is shifting from independent construction to multi-faceted collaboration involving various stakeholders [2]. - Data weaving technology is reshaping data architecture for unified management [2]. - DataOps is creating a new paradigm for data governance, improving the efficiency of data product delivery [2]. - Deep integration of data and AI is enhancing the data governance framework and evaluation system [2]. - Establishing data operation centers to accelerate value release [2]. - Creating trusted data spaces to ensure data security and promote efficient data circulation [2]. Typical Case Studies - Shandong Energy Group's comprehensive safety production technology management platform has achieved data sharing, improving data quality and decision-making levels [2]. - The coal mine dynamic pressure big data analysis platform has reduced personnel workload and enhanced safety levels in the coal industry [2]. - Yunnan Energy Investment's safety production operation monitoring and emergency command center project has transitioned data online, empowering business development and driving model innovation [2].