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论文编号：	15842
作者编号：	2320234110
上传时间：	2025/12/10 20:24:16
中文题目：	ZT公司数据治理体系构建研究
英文题目：	Research on the Construction of Data Governance in ZT Company
指导老师：	李凯
中文关键字：	数据治理；数据全生命周期；DCMM；制造业企业
英文关键字：	Data Governance; Data Value; Data Lifecycle; DCMM; Manufacturing Enterprise
中文摘要：	数据作为第五大生产要素，正持续推动制造业模式创新与价值链优化，其治理水平对数字化转型成效具有决定性作用。当前全球制造业数字化转型向纵深推进，工业4.0与工业互联网深度融合背景下，制造业企业普遍面临数据治理的系统性挑战，数据标准缺失导致跨部门协同不畅、权责模糊降低协同效率、全生命周期管控不足引发数据质量波动，此类问题频发严重制约了数据资源的流动性与可利用性。 国有大型制造企业ZT公司作为我国轨道交通装备制造领域的骨干企业，积极响应国家政策号召，布局企业数字化转型战略，在数据汇聚与基础应用方面开展初步探索。但随着转型深入，数据治理与业务发展的适配性不足问题逐渐显现，由于缺乏完整的体系规划，治理工作呈碎片化特征，数据架构混乱、质量管控薄弱、资产价值挖掘不足等问题叠加，导致数据价值难以充分释放，亟需通过系统性研究构建适配企业业务特性的数据治理体系。 本研究首先通过文献研究法，系统梳理了国内外数据治理相关理论与制造业实践，明确了DCMM、DAMA等理论框架的核心应用价值。随后，研究结合问卷调研与访谈法，结合问卷调研法与访谈法覆盖ZT公司研发、生产、供应链等核心业务部门，系统评估数据治理现状与存在问题，分析问题产生的深层原因。借鉴先进理论与实践经验，结合 ZT 公司轨道交通装备制造业务特性，构建涵盖战略、保障、治理、应用四层架构的数据治理体系，在ZT公司内分阶段推进体系实施，过程中动态调整方案以适配业务实际需求。研究结论不仅为ZT公司数字化转型提供有力支撑，也为同类型制造业企业数据治理体系构建提供兼具理论与应用价值的可复制的实践路径。 关键词：数据治理；数据全生命周期；DCMM；制造业企业
英文摘要：	As the fifth major factor of production, data continues to drive model innovation and value chain optimization in the manufacturing industry, and the level of its governance exerts a crucial impact on the effectiveness of digital transformation. Currently, the digital transformation of the global manufacturing industry is advancing in depth. Against the backdrop of the deep integration of Industry 4.0 and the Industrial Internet, manufacturing enterprises generally face systemic challenges in data governance. The lack of data standards hinders cross-departmental interactions, ambiguous rights and responsibilities reduce collaboration efficiency, and insufficient full-lifecycle management triggers fluctuations in data quality. The frequent occurrence of such issues severely restricts the mobility and availability of data resources. As a backbone enterprise in China's rail transit equipment manufacturing sector, ZT Company, a large state-owned manufacturing enterprise, actively responds to the call of national policies, lays out its corporate digital transformation strategy, and has carried out preliminary explorations in data aggregation and basic applications. However, with the in-depth advancement of transformation, the problem of insufficient adaptability between data governance and business development has gradually emerged. Due to the lack of a complete system plan, governance work progresses in a fragmented manner. Problems such as chaotic data architecture, weak quality control, and insufficient exploration of asset value overlap, making it difficult to fully release the value of data. Therefore, there is an urgent need to conduct systematic research to build a data governance system that adapts to the business characteristics of the enterprise. This thesis systematically sorts out domestic and foreign theories related to data governance and industry practices in the manufacturing sector through the literature research method, and clarifies the core application value of theories such as the DCMM Data Management Maturity Model and the DAMA Data Management Knowledge System in links such as current situation analysis and system construction. Combining the questionnaire survey method and interview method, it covers core business departments of ZT Company such as R&D, production, and supply chain, systematically evaluates the current situation of data governance and existing problems, and analyzes the in-depth causes of these problems. Drawing on advanced theories and practical experience, and considering the business characteristics of ZT Company in rail transit equipment manufacturing, a data governance system with a four-tier architecture including strategy, guarantee, governance, and application is constructed. The implementation of the system is promoted in phases within ZT Company, and the plan is dynamically adjusted during the process to adapt to the actual business needs. The research conclusions not only provide strong support for the digital transformation of ZT Company but also offer a replicable practical path with both theoretical and application value for the construction of data governance systems in similar manufacturing enterprises. Keywords: Data Governance; Data Value; Data Lifecycle; DCMM; Manufacturing Enterprise
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