大连理工大学专业学位硕士学位论文
摘
要
随着“大数据”时代的到来，数据在企业运营、决策指导和战略规划中的重要性日
益提升，企业数据的挖掘、清洗及分析等数据处理能力也是评估企业管理水平的参考依
据，所以数据质量的管控是统计工作的核心，是数据分析的基石，其重要程度不言而喻，
所以数据质量管理是企业从人工管理到智能化化管理的模式变革中不可忽视的重要环
节。UTS公司是汽车行业全球 500强企业的下属分公司，在国内处于技术领先的地位，
是多家知名汽车制造厂商的合作伙伴。近年来，因中国汽车市场的发展向好，UTS公司
运营规模随之逐步提升，总部的管理模式由早期的“自治管理”逐步向“集权管理”发
展。因此，数据作为 UTS公司业务流程和绩效评判最直观的评价指标，成为了集团内部
管理质量提升的主要管控方向。
本文通过对国内外数据质量管理现状及理论研究，发现了质量管理办法在数据质量
管理中的可行性，同时在对 UTS公司的数据质量问题的收集和整理中，发现 UTS公司
的运营数据中质量问题众多，作者提取了 UTS公司常见的数据质量问题，进而对其进
行详细剖析，发现数据质量问题主要来源于数据方面的管理漏洞，分别是信息系统必填
字段缺少核查和更新的管理、系统外数据缺乏统一的管理要求、主数据变更流程不完善、
缺乏数据质量监控和整改工作。论文综合目前国家的统计质量保证框架和其他发达国家
常用的数据质量管理方法，有针对性的对 UTS公司的数据质量问题提出改善意见，从
信息系统设置、编制系统外数据管理要求、完善主数据变更流程、增加数据质量监控和
整改工作等方面，对 UTS公司的数据质量管理工作提供改善思路，同时提出了改善方
案所需的保障需求，以保证改善方案可以有效实施。
此次数据质量管理改善工作的推进，预示着 UTS公司开始着手于数据质量的管理
工作，强调了数据质量在公司管理中的重要性，是 UTS公司向数字化管理迈进的重要
里程碑。希望可以通过此论文的研究，有效的改善 UTS公司的数据质量管理问题，同时
也希望论文的研究成果能够为行业内同样被数据质量问题困扰的企业提供指导思路。
关键词：数据质量管理；质量管理体系；数据管理
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UTS公司运营数据质量管理问题研究
Research on UTS's Operational Data Quality Management Problem
Abstract
With the advent of the era of "big data", data is becoming more and more important in
enterprise operation, decision guidance and strategic planning. Data processing capabilities
such as enterprise data mining, cleaning and analysis are also the reference basis for evaluating
enterprise management level. Therefore, the control of data quality is the core of statistical work
and the cornerstone of data analysis, and its importance is self-evident. Data quality
management is an important link in the mode transformation from manual management to
intelligent management. UTS company is a subsidiary of the global top 500 enterprises in the
automotive industry. It is in a leading position in technology in China and a partner of many
well-known automobile manufacturers. In recent years, due to the development of China's
automobile market, the operation scale of UTS company has gradually increased, and the
management mode of the headquarters has gradually optimized from early "autonomous
management" to "centralized management". Therefore, as the most intuitive evaluation index
for the business process and performance evaluation of UTS company, data has become the
main control direction for the improvement of the group's internal management quality.
Based on the current situation and theoretical research of data quality management at home
and abroad, this thesis finds the feasibility of quality management methods in data quality
management. At the same time, in the collection and sorting of data quality problems of UTS
company, it is found that there are many quality problems in the operation data of UTS company.
The author extracts the common data quality problems of UTS company, and then analyzes
them in detail, it is found that the data quality problems mainly come from the data management
loopholes, which are the lack of verification and update management of mandatory fields in the
information system, the lack of unified management requirements for data outside the system,
the imperfect master data change process, and the lack of data quality monitoring and
rectification. Based on the current national statistical quality assurance framework and the data
quality management methods commonly used in other developed countries, this thesis puts
forward targeted improvement suggestions on the data quality problems of UTS company, from
the aspects of information system setting, compiling data management requirements outside the
system, improving master data change process, increasing data quality monitoring and
rectification, etc, Provide improvement ideas for the data quality management of UTS company,
and put forward the guarantee requirements for the improvement scheme to ensure the effective
implementation of the improvement scheme.
The promotion of data quality management improvement indicates that UTS company
starts to work on data quality management, emphasizes the importance of data quality in
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大连理工大学专业学位硕士学位论文
company management, and is an important milestone for UTS company to move towards digital
management. At the same time, it also hopes to provide guidance for other enterprises that are
also troubled by data quality problems on the road of digital transformation.
Key Words： Data Quality Management; Quality Management System; Data
Management
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