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论文编号：	14281
作者编号：	2320213859
上传时间：	2023/12/11 11:46:41
中文题目：	数据治理体系的数据认责机制研究 ——以A银行为例
英文题目：	Research on Data Responsibility Management of Data Governance System ——Take Bank A as an Example
指导老师：	樊振佳
中文关键字：	数据治理，数据认责，数据生命周期，数据管理成熟度，数据治理组织架构
英文关键字：	Data governance, Data responsibility management, Data lifecycle, Data management maturity, Data governance organizational structure
中文摘要：	数据作为重要的资产和生产要素，已经引起国家、各行业和各企业的广泛关注和重视。我国在《“十三五”国家信息化规划》《关于构建更加完善的要素市场化配置体制机制的意见》《关于构建数据基础制度更好发挥数据要素作用的意见》等多份政策文件中，已经将督促和指导政府和各行业、企业构建起基础的数据治理体系和机制，激活数据要素潜能，放在了极其重要的位置。在金融行业内，由于信息化程度高、数据密集的性质，已早早启动数据治理相关工作。金融监管部门发布《银行业金融机构数据治理指引》等文件，从数据治理组织架构、数据管理、数据质量控制和数据价值实现等方面进一步给出明确的指导意见。各家银行近年来也在试水数字化转型工作，在推进银行业数字化转型工作的同时，健全数据治理体系、增强数据管理能力、加强数据质量控制、提高数据应用能力，以推动金融高质量发展，更好地服务实体经济和满足人民群众需要。 然而，在各个企业纷纷开展数字化转型，以数据赋能业务发展的当下，往往面临着企业内部数据质量不高、数据权责不清、数据难以共享等问题，致使企业用数难、用好数更难。为此，迫切需要企业建立起权责清晰、相互协同的数据治理体系，建立起数据治理的组织架构，明确利益相关方的职责，协同开展数据治理工作，提升企业内的数据质量和数据管理效率，促进数据发挥其重要价值。 建设数据治理体系是一个系统化的、庞大的工程，涉及企业的各个部门、各项业务或管理活动，往往需要较长的建设周期、投入较多的资源参与数据治理体系建设工作。虽然银行企业由于其信息化程度高、业务高度数据化、数据治理工作开展相对较早等特点，使其在开展数字化转型、建设企业级数据治理体系等方面工作具备一定的先天优势，但是由于业务种类繁多、系统庞杂，同样为开展数据治理工作带来了不少的障碍，或多或少地存在缺少数据战略指引、数据治理体系框架不完整、数据治理职责不清、数据治理工作协作不畅、数据质量责任不清、数据质量问题无法推动整改、数据无法充分共享、数据价值难以发挥等问题。 本文以某银行企业为例，研究在数据治理的框架体系下，构建企业数据治理体系的数据认责机制，以数据认责为基础，厘清企业内数据治理的组织架构和数据责任，建立数据管理的机制，优化企业建设数据治理体系、开展数据治理相关工作的效率，确保数据生命周期内各项数据职责能够被有效落实，以解决企业在新的数字化转型的形势下，面临的数据治理机制不健全、数据质量问题突出、数据共享存在“壁垒”等问题。研究过程综合运用了文献分析法、调查问卷法、专家访谈法和比较分析法，研究有关数据治理体系框架的文献，分析A银行数据治理体系建设现状，调研并对标同业领先实践，形成数据治理体系的数据认责机制，为后续开展数据认责工作、应用数据认责结果、顺利建设企业数据治理体系提供理论依据和实践指导。本文的具体研究内容主要包括： 1.数据责任现状评估方法研究。为了确保全面评估A银行的数据责任分布现状，厘清A银行在数据治理各相关领域存在的数据责任问题，分析并制定切实可行的改进形象，本研究借助数据管理能力成熟度评估模型，研究形成评估企业数据治理体系现状的框架和评分标准。借助现状评估的框架和评分标准，通过开展问卷调研、专家访谈等方法，对A银行的数据责任现状进行充分评估，对同业领先实践的机制进行调研，制定可改进的方向，并验证评估方法的可行性。 2. 企业开展数据认责的数据治理体系框架研究。为了全面制定数据认责的机制，本研究以DAMA、DCMM等数据治理体系框架为基础，综合IBM、普华永道等咨询公司方法论，以及同业先进银行的数据治理体系实践，结合A银行在数据治理体系建设方面未来的改进方向，形成企业数据治理体系的框架。该框架涵盖数据战略、数据治理保障机制、数据治理领域、数据应用系统与技术平台等方面内容，覆盖数据生命周期各阶段的关键活动。数据认责机制研究将以此框架为基础，分析数据治理各领域、数据生命周期各阶段的关键活动的数据责任，并落实至数据治理各层次的组织中，并设计相应数据管理流程机制。 3. 数据认责机制研究。数据认责机制研究是本论文的研究核心。本论文以数据治理体系框架为依据，分组织架构层次、分生命周期阶段研究利益相关方应当承担的数据管理责任和数据责任，抽象提炼形成数据认责机制的“三要素”，即数据治理组织、数据认责对象和数据责任角色这三部分组成内容。同时，为了确保相关数据对象都能明确各相关部门应当承担的数据责任角色，本论文还设计了数据的认责原则和方法，制定实施数据认责的具体工作步骤和方案，为后续开展数据认责工作提供指导。 本文创新性地基于数据认责视角，研究如何运用企业的数据治理体系框架，建立起覆盖数据治理保障机制、数据治理领域和数据生命周期各环节活动的数据认责机制，并通过数据认责并明确利益相关方在数据治理和数据管理过程应当承担的责任，建立起相互协同的数据治理工作机制。通过本研究，可以有效促进企业内各部门相互协同开展数据治理工作，具有一定的理论价值和现实意义。
英文摘要：	Data has became important asset and production factor, it attracted widespread attention from our country, industries, and enterprises. Our country had placed great importance on build a basic data governance system and mechanism, activate the potential of data elements, in multiple policy documents. In the financial industry, data governance related work initiated earlier, due to informatization and data-intensive nature. The financial regulatory department provided clear guidance on data governance organizational structure, data management, data quality control, and data value realization. Various banks initiated the digital transformation work in the past years. They also improved their data governance system, enhanced data management capabilities, strengthened data quality control, and improved data application capabilities, while promoting the digital transformation work. However, enterprises often face problems such as low data quality, unclear data rights and responsibilities, and difficulty in sharing data, making it difficult for enterprises to use data well. Therefore, enterprises have to establish a clear and collaborative data governance system, establish an organizational structure for data governance, clarify the responsibilities of stakeholders, collaborate on data governance work, improve data quality and management efficiency, and promote the value of data. Building a data governance system is a systematic and massive project that involves various departments, various businesses or management activities of the enterprise. It often requires a long construction cycle and a large investment of resources to build the data governance system. Although banking enterprises have great advantages, the wide variety of business types and complex systems also bring many obstacles, such as lack of data strategy guidance, the framework of the data governance system incomplete, the responsibilities of data governance unclear, the collaboration of data governance work not well, the responsibility for data quality unclear, the rectification of data quality issues cannot be promoted, data cannot be fully shared, and the value of data difficult to found, and so on. This paper takes a bank as an example to study and build a data governance system framework based on the data responsibilities management, clarify the responsibilities about data and data management in the data lifecycle, establish data management mechanism, solve the problem that the enterprise faced. The research utilized literature analysis, survey questionnaire, expert interview, and comparative analysis methods, study the literature on data governance, analyze the current situation of Bank A's data governance, research and benchmark industry leading practices, generate the method and plan for data governance system and data responsibility management, providing theoretical basis and practical guidance for the future work. The research contents of this paper mainly include: 1.Research on current situation assessment method of data responsibility. In order to ensure completely evaluate the current situation of data responsibility in Bank A, this paper uses the data management capability maturity evaluation model to form the framework and scoring standards for evaluating the current status of enterprise data governance system. By utilizing the framework and scoring standards of current situation assessment, by questionnaire surveys, expert interviews methods, completely evaluate the data responsibility status of Bank A, investigate the mechanism of leading practices in the industry, formulate the improvement directions, and verifiy the feasibility of the evaluation method. 2.Research on data governance system framework for data responsibility management. Generate the data governance framework, based on the DAMA and DCMM, and the methodologies of consulting companies such as IBM and PwC, as well as the data governance system practices of advanced banks. The framework covering the contents such as data strategy, data governance mechanism, data governance area, data application systems and technology, covering all stages of the data lifecycle. 3.Research on the method of data responsibility management. This is the is the core of this paper. This paper studies the data responsibilities that stakeholders should take at the various levels' organization and various data lifecycle stage, based on the framework of the data governance system. This paper abstracts and extracts the "three elements" that form the data accountability mechanism,namely the data governance organization, data objects and data responsibility roles. In order to ensure that the relevant data objects can clearly define the data responsibility roles that each relevant department should undertake, this paper also designs the principles and methods of data responsibility management, develops specific work steps and plans for implementing data accountability, and provides guidance for subsequent data responsibility management work. This paper innovatively constructs the data governance framework based on data accountability management. It clarifies the responsibilities that stakeholders should undertake in the process of data governance and data management. It can promote the collaboration of various departments, and carry out data governance work effectively. This paper has certain theoretical value and practical significance.
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