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论文编号：	16005
作者编号：	2320233848
上传时间：	2026/6/2 15:36:52
中文题目：	基于DAMA体系的Q能源公司数据治理研究
英文题目：	Research on Data Governance of Q Energy Company Based on the DAMA Framework
指导老师：	张国萍
中文关键字：	数据治理；数字化转型；能源行业
英文关键字：	Data Governance; Digital Transformation; Energy Industry
中文摘要：	为响应国家数字化转型战略与国资委对国有企业数据治理体系建设的政策要求，Q公司基于经营管理实际，启动了以数据治理为核心的系统性工程。该项目旨在构建一套权责清晰、制度完备、流程规范的数据治理框架，重点解决因治理机制缺失导致的“数据权责不明、标准不一、质量参差、安全薄弱”等根本性问题，推动企业从传统数据管理向体系化数据治理转型，实现数据资产在合规管控下的有序流动与价值深化。 本研究综合运用问卷调查、面对面访谈、资料研读、案例分析等方法，组织座谈会、基础培训、定制化培训，系统调研Q公司在人力资源、财务、供应链等六大业务域的数据治理现状与核心痛点。通过对比国内外石油巨头企业的治理实践，重点借鉴其在数据治理组织架构、政策体系、管控流程等方面的成熟模式。在此基础上，结合Q公司实际情况，设计了涵盖数据资产目录与权责设计、数据架构制定、数据标准制定、数据质量管控和数据安全防护的数据全生命周期运营管理模式，以确保治理要求能够有效嵌入各业务域的日常运作之中。 实践表明，通过该治理体系的实施，Q公司初步建立了跨部门的数据责权机制与协同规范，实现了关键数据标准的统一与质量闭环管控，显著提升了数据的可信度、一致性及合规水平。在战略层面，公司依托治理基础逐步形成了数据驱动的决策支持能力，增强了业务响应敏捷度与风险防控能力，为企业在能源行业数字化竞争中构建了制度性优势。 本研究所形成的数据治理框架与分阶段实施路径，在Q公司实践中已初见成效，不仅为其建立了权责清晰、流程闭环、持续迭代的治理运营机制，也为同类国有企业，特别是在数据要素市场化配置与数字化转型双重背景下的能源企业提供了具备可操作性的方法论参考。未来，Q公司将在现有基础上，进一步深化治理成效的量化评估与动态优化机制，通过构建治理成熟度度量体系与价值贡献分析模型，实现治理投入与业务收益的可视化关联，并将治理体系拓展至生产、销售、研发等更多业务领域，以治理赋能数据价值向资产化、资本化阶段跃升。
英文摘要：	In response to the national digital transformation strategy and the policy requirements of the State-owned Assets Supervision and Administration Commission regarding the construction of data governance systems in state-owned enterprises, Q Company, based on its actual operational and management conditions, initiated a systematic project centered on data governance. This project aims to establish a data governance framework characterized by clear responsibilities, comprehensive regulations, and standardized processes. It focuses on addressing fundamental issues arising from the lack of governance mechanisms, such as “unclear data ownership, inconsistent standards, uneven quality, and weak security,” thereby promoting the enterprise’s transition from traditional data management to systematic data governance and enabling the orderly flow and value enhancement of data assets under compliant control. This study comprehensively employs methods such as questionnaire surveys, face-to-face interviews, literature review, and case analysis, along with organizing discussion sessions, basic training, and customized training, to systematically investigate the current state and core challenges of data governance across six major business domains of Q Company, including human resources, finance, and supply chain. By comparing the governance practices of industry benchmarks, the study draws key insights from their mature models in areas such as data governance organizational structures, policy systems, and control processes. Building on this, and tailored to the actual situation of Q Company, a data lifecycle operation management model is designed, covering data asset catalog and responsibility design, data architecture formulation, data standard establishment, data quality control, and data security protection, ensuring that governance requirements are effectively embedded into the daily operations of each business domain. Practice shows that through the implementation of this governance system, Q Company has initially established cross-departmental data accountability mechanisms and collaborative norms, achieving unified key data standards and closed-loop quality control. This has significantly improved data reliability, consistency, and compliance. At the strategic level, leveraging the governance foundation, the company has gradually developed data-driven decision-support capabilities, enhanced business response agility and risk prevention capabilities, and built institutional advantages for the enterprise in the digital competition within the energy industry. The data governance framework and phased implementation pathway developed in this study have demonstrated initial effectiveness in Q Company’s practice. They have not only established a clear, closed-loop, and iterative governance operation mechanism for the company but also provided actionable methodological references for similar state-owned enterprises, particularly in the energy sector under the dual context of data element marketization and digital transformation. In the future, Q Company will further deepen the quantitative evaluation and dynamic optimization mechanisms for governance effectiveness based on existing foundations. Plans include constructing a governance maturity measurement system and a value contribution analysis model to visualize the correlation between governance investments and business benefits. Additionally, the governance system will be extended to more business areas such as production and research and development, empowering the leap of data value from assetization to capitalization through governance.
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