Research on the evaluation system of data quality in new retail based on FISM-ANP
基于FISM-ANP新零售数据质量评价体系研究

摘要

目的 新零售利用数据的优势，充分挖掘其丰富的商业价值，以利用数据资产实现商业利益最大化。而数据质量的优劣是挖掘有实用价值的信息的关键所在。为了推动数据质量系统评估与规范化管理，需要建立相应的新零售数据质量评价指标体系。方法 本文以新零售和数据质量等相关概念为基础，通过国内外文献梳理，借鉴已有的数据质量评价体系，最终以数据固有质量、用户适用度、数据监管水平和数据技术水平4个数据质量评价维度和18个二级数据质量评价指标。通过模糊解释结构模型确定评价指标因素之间的存在的直接与间接关系，将得到的关系作为网络层次分析的网络结构的输入，最终构建评价指标体系。结论 在新零售数据质量评价体系中，数据准确性是决定新零售数据质量的关键因素。此外，在整体数据质量评估中，用户适用度是新零售数据质量的重要维度，因此在数据业务发展成熟阶段，应该专注于提升用户适用度。

Abstract

Objective New retail leverages the advantages of data to fully explore its rich commercial value and maximize business benefits through the utilization of data assets. The quality of data is crucial in extracting useful and valuable information. To promote the evaluation and standardized management of data quality systems, it is necessary to establish a corresponding evaluation index system for data quality in new retail. Method Based on the concepts of new retail and data quality, this paper conducts a review of domestic and foreign literature and references existing data quality evaluation systems. Ultimately, it identifies four dimensions of data quality evaluation, including intrinsic data quality, user applicability, data governance level, and data technology level, along with 18 secondary data quality evaluation indicators. The fuzzy interpretive structural model is used to determine the direct and indirect relationships between the evaluation indicator factors. These relationships are then used as inputs for the network structure of the analytic hierarchy process to construct the evaluation index system.
Conclusion   In the evaluation system for data quality in new retail, data accuracy is identified as the key factor determining data quality. Additionally, user applicability is an important dimension in overall data quality assessment. Therefore, in the mature stage of data business development, efforts should be focused on enhancing user applicability.