Usage of the digital economy information infrastructure to improve the quality of statistical data

Author:

Lipuntsov Yuriy P.

Abstract

Statistics agencies are the main data provider on the economic position of the macroeconomic level. Most economic decisions on a national scale are based on statistical data. Data processing is a key business process for statistical agencies. At the same time, the quality of statistical data supplied by Rosstat is not always high enough. There are adjustments, a discrepancy between data sets describing the same economic phenomenon is revealed. The purpose of the work is to describe the methods of collecting and processing statistical information that will contribute to improving the quality of the presented data. From the information point of view, the statistical agency is engaged in the organization of information exchange between data providers and consumers, acts as a data aggregator. To organize the information exchange within community you need to create a semantic space to ensure the meaningful filling of the data. The main role in the semantic space is played by the identifiers of objects. The article considers the unified identifiers of statistical accounting objects as a method of collecting and processing statistical information and improving its quality. The international statistical practice use methods of standardizing the turnover of statistical data. Information standards are designed to unify identifiers and namespace for participants of the statistical information turnover and to provide a single semantic space. If you use of unified identifiers, the procedures for processing statistical data become transparent, it allow you grouping by different sections, as well as decomposition of aggregated data into components.The results of the work are recommendations on the use of Core component of the information infrastructure for the collection and analysis of statistical data. In the existing information infrastructure of the Russian digital economy, there are a number of data sources, the use of which will improve the quality of collection and processing of statistical data. To create a semantic space of statistical data in the Russian Federation, the most important section is the registers of Core Components. The use of registers will allow you to organize the binding of statistical data from different domains, as well as to implement the link of aggregated data with microdata. Significant progress is observed in the marking of goods, which allows you to track object’s movement through all stages of the life cycle, as well as the location. The government of the Russian Federation initiated a project on labeling of goods, and this information gives an opportunity to get a clear picture of a significant part of the economy. An additional information source of statistical data can be the corporate sector, where actively used tracking systems that monitor the goods, vehicles, containers, warehousing.Conclusion: There are several options for creation of the semantic space for statistical data. World experience is guided by the use of the Web architecture, which involves the technological identifiers. Semantics of statistical data can be ensured by using the potential of the information infrastructure, which will solve a number of problems of statistical accounting.

Publisher

Plekhanov Russian University of Economics (PRUE)

Reference20 articles.

1. Salikhov M. V zalozhnikakh u statisticheskoy pogreshnosti: pochemu slozhno verit’ Rosstatu 27.06.2018. URL: https://www.rbc.ru/opinions/economics/27/06/2018/5b3201229a794725025a6958. (Accessed:07.07.2018). (In Russ.)

2. TASS. «MER: Rosstat dolzhen povysit’ kachestvo statistiki i uroven’ doveriya k ney» 04.04.2017. URL: http://tass.ru/ekonomika/4153390. (Accessed:18.05.2018). (In Russ.)

3. UNSD. «Official Statistics: Principles and Practices» 18.02.2017. URL: https://unstats.un.org/unsd/methods/statorg/FP-Russian.pdf. (Accessed: 19.04.2018).

4. M. Pellegrino. Maintaining the quality of EU statistics while enabling reuse. SEMIC, Dublin, 2013.

5. Statistical Working Group. «Statistical Data and Metadata Exchange» URL: http://sdmx.org/. (Accessed: 23.03.2015).

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3