统计研究 ›› 2020, Vol. 37 ›› Issue (2): 119-128.doi: 10.19343/j.cnki.11-1302/c.2020.02.010

• • 上一篇    

大数据应用的质量控制

李金昌   

  • 出版日期:2020-02-25 发布日期:2020-03-10

The Quality Control of the Application of Big Data

Li Jinchang   

  • Online:2020-02-25 Published:2020-03-10

摘要: 数据作为重要的数据资源存在,不论是其内在蕴含的信息价值还是其已经成为人类社会所需数据有机组成的客观事实,都迫使我们去不断加强对大数据的应用。然而,由于大数据作为信息技术应用的副产品,其复杂性、不确定性和涌现性决定了我们应用大数据并非易事,存在着很多质量上的问题,除了具有传统数据所有的质量问题外,还包括一些独特的新问题。为了更好地应用大数据,本文对如何进行大数据应用的质量控制进行了初步的研究。主要内容包括以下三个方面:一是对什么是大数据质量、受哪些因素影响、可能存在哪些质量问题进行了探讨;二是从做好理论准备、建立质量控制方案、重视对小数据研究、加强大数据管理、加强大数据人才培养和加强大数据法制建设六个方面,提出了大数据应用的质量控制的基本想法;三是对大数据应用中需要引起注意的几个方面进行了讨论,并结合例子进行了阐释。

关键词: 数据, 数据质量, 质量控制

Abstract: Big data has proven to contain invaluable information, and has become an indispensable part of the information structure for the advancement of our society. It is therefore critical for us to explore how big data can be further applied to maximize its value as an important source of information. However, as a side product of the information technology application, big data has demonstrated its complexity, uncertainty and emergence, which contribute to the difficulty of its application. In addition to the quality issues in common with traditional data, there are also a few problems that are specific to big data. To better utilize big data, this paper aims to serve as a preliminary study on the quality control of the application of big data, with three major sections: 1) a discussion on the definition of big data quality, what factors it is subject to and what potential quality issues it may have; 2) an introduction to the basic methodology of the quality control in big data application, explained from six perspectives, namely making theoretical preparations, building a quality control plan, attaching importance to studying small data, reinforcing big data management, advancing the training of big data personnnel and improving the legal framework for big data; and 3) a case studybased analysis of a number of things that entail particular attention in the application of big data.

Key words: Big Data, Data Quality, Quality Control