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论文编号：	14746
作者编号：	2320213943
上传时间：	2024/6/7 15:35:20
中文题目：	R化工企业数据治理项目进度管理优化研究
英文题目：	Research on Optimization of Progress Management for the Data Governance Project in R Chemical Enterprise
指导老师：	李凯
中文关键字：	项目进度管理优化；关键链技术；Project软件；煤化工行业；数据治理项目
英文关键字：	Project schedule management optimization; Key chain technology; Project software；Coal chemical industry；Data governance project
中文摘要：	数字化转型是企业保持活力、提高管理效率和市场竞争力、实现可持续发展的重要路径。数据治理作为转型过程中进行数据标准化和规范化、提高数据质量的有效手段，已经成为企业实现数字化、智能化建设的基石性工作。将数据治理的过程项目化，以标准化的项目管理理论和工具进行规划和监控，有助于提高治理效率并提高治理成功率。 针对数据治理项目进度管控难问题，本文以R化工企业数据治理项目为研究对象，采用文件研究法、案例研究法、对比研究法和定性定量研究法，对此类项目特点和进度管理的现状进行分析；然后结合关键链技术等项目进度管理的理论和软件工具Project，从项目活动的排序、持续时间估算、缓冲区设计、进度控制4个方面展开介绍进度管理的优化策略；最后结合项目执行模式的特点提出此类项目进度管理的保障性措施。 本文通过研究，主要得出以下结论：第一，数据治理项目的范围、活动持续时间与业主的企业规模、信息化建设现状高度相关，要慎重选取标杆对照、类比参考、历史项目参考等方式估算项目时间，而应细分项目活动，参考相关专家和资源的意见，自下而上估算，并采取三点估算方式，以便得出相对可靠的估算结果；第二，数据治理项目高度依赖于人力资源，具有资源柔性特点，容易出现资源紧俏问题，适用关键链技术来找出关键活动和资源紧俏问题所在点，并为此合理设计缓冲区，保证项目进度的可控性；第三，数据治理项目的需求和范围常常是渐进明细的，因此对于项目执行的控制提出动态管理的要求，要求业主深度参与，持续沟通，重视培训，要求项目经理密切关注范围、人力资源和风险问题，以及缓冲区的剩余情况，采用PDCA循环推进项目朝着数据战略目标的方向执行。
英文摘要：	Digital transformation is an important path for enterprises to maintain vitality, improve management efficiency and market competitiveness, and achieve sustainable development. Data governance, as an effective means of standardizing and improving data quality during the transformation process, has become a cornerstone work for enterprises to achieve digital and intelligent construction. Projecting the process of data governance and using standardized project management theories and tools for planning and monitoring can help improve governance efficiency and improve governance success rates. In response to the difficulty in controlling the progress of data governance projects, this thesis takes the R chemical enterprise data governance project as the research object, and uses document research, case study, comparative research, and qualitative and quantitative research methods to analyze the characteristics of such projects and the current status of progress management; Then, combined with the theory of project progress management such as critical chain technology and the software tool Project, the optimization strategies for progress management are introduced from four aspects: project activity sorting, duration estimation, buffer design, and progress control; Finally, based on the characteristics of the project execution mode, propose guarantee measures for progress management of such projects. Through research, the main conclusions drawn in this thesis are as follows: Firstly, the scope and duration of data governance projects are highly related to the scale of the owner's enterprise and the current situation of information construction. It is necessary to carefully select benchmark comparison, analogy reference, historical project reference, and other methods to estimate project time. Instead, project activities should be segmented, and the opinions of relevant experts and resources should be consulted for bottom-up estimation, And adopt a three-point estimation method to obtain relatively reliable estimation results; Secondly, data governance projects are highly dependent on human resources and have flexible characteristics, making them prone to resource shortages. Key chain technology is applied to identify key activities and resource scarcity issues, and buffer zones are designed reasonably to ensure the controllability of project progress; Thirdly, the requirements and scope of data governance projects are often gradual and detailed. Therefore, dynamic management requirements are put forward for project execution control, requiring deep participation of owners, continuous communication, and emphasis on training. Project managers are required to closely monitor scope, human resources, and risk issues, as well as the remaining situation of buffer zones, and use PDCA cycles to promote project execution towards data strategic goals.
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