本文以H公司为例,探讨金融数据仓库的数据质量评估。首先介绍证券行业数据仓库数据内容及特点,构建包含完整性、准确性等7个一级指标及相关二级指标的评价体系并量化。接着阐述模糊层次分析法和熵权法,前者通过构建层次模型和模糊判断...本文以H公司为例,探讨金融数据仓库的数据质量评估。首先介绍证券行业数据仓库数据内容及特点,构建包含完整性、准确性等7个一级指标及相关二级指标的评价体系并量化。接着阐述模糊层次分析法和熵权法,前者通过构建层次模型和模糊判断矩阵确定主观权重,后者经数据标准化等步骤计算客观权重,两者结合得出综合权重。通过对H公司主体、交易等四个主题域数据集的算例分析,包括指标量化、权重计算及质量评估,结果表明主体和渠道数据在准确性及一致性方面有不足,研究为金融数据仓库数据质量管理提供了科学方法和改进方向。This research focuses on the data quality evaluation of financial data warehouses, taking H Company as an example. Firstly, it introduced the data content and characteristics of the security industry data warehouse, constructed an evaluation system including 7 first-level indicators such as integrity and accuracy and related second-level indicators, and quantified them. Then, it elaborated on the fuzzy analytic hierarchy process and the entropy weight method. The former determines subjective weights by constructing a hierarchical model and a fuzzy judgment matrix, while the latter calculates objective weights through steps such as data standardization. The two methods are combined to obtain comprehensive weights. Through a case analysis of the data sets of four theme domains such as the main body and transactions of Company H, including index quantification, weight calculation, and quality assessment, the results show that the main body and channel data have deficiencies in terms of accuracy and consistency. This study provides a scientific method and an improvement direction for the data quality management of financial data warehouses.展开更多
文摘本文以H公司为例,探讨金融数据仓库的数据质量评估。首先介绍证券行业数据仓库数据内容及特点,构建包含完整性、准确性等7个一级指标及相关二级指标的评价体系并量化。接着阐述模糊层次分析法和熵权法,前者通过构建层次模型和模糊判断矩阵确定主观权重,后者经数据标准化等步骤计算客观权重,两者结合得出综合权重。通过对H公司主体、交易等四个主题域数据集的算例分析,包括指标量化、权重计算及质量评估,结果表明主体和渠道数据在准确性及一致性方面有不足,研究为金融数据仓库数据质量管理提供了科学方法和改进方向。This research focuses on the data quality evaluation of financial data warehouses, taking H Company as an example. Firstly, it introduced the data content and characteristics of the security industry data warehouse, constructed an evaluation system including 7 first-level indicators such as integrity and accuracy and related second-level indicators, and quantified them. Then, it elaborated on the fuzzy analytic hierarchy process and the entropy weight method. The former determines subjective weights by constructing a hierarchical model and a fuzzy judgment matrix, while the latter calculates objective weights through steps such as data standardization. The two methods are combined to obtain comprehensive weights. Through a case analysis of the data sets of four theme domains such as the main body and transactions of Company H, including index quantification, weight calculation, and quality assessment, the results show that the main body and channel data have deficiencies in terms of accuracy and consistency. This study provides a scientific method and an improvement direction for the data quality management of financial data warehouses.
