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

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.

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).

6. ISO/TC 154 , «ISO/TS 17369:2005 Statistical data and metadata exchange (SDMX)» 10 10 2005. URL: https://www.iso.org/standard/40555.html. (Accessed: 16 05 2018).

7. M. Hausenblas, W. Halb, Y. Raimond, L. Feigenbaum, D. Ayers « SCOVO: Using Statistics on the Web of Data». Proceedings of ESWC 2009 — 6th European Semantic Web Conference, Heraklion, 2009.

8. M. Hausenblas, W. Halb и Y. Raimond, «Scripting User Contributed Interlinking». 4th Workshop on Scripting for the Semantic Web (SFSW08). Tenerife, 2008.

9. K. Alexander, R. Cyganiak, M. Hausenblas и J. Zhao, «Describing Linked Datasets» в Proceedings of the Linked Data on the Web Workshop (LDOW2009), Madrid, 2009.

10. W. Arms, Digital Libraries. Retrieved 04.04.2017. Boston: M.I.T. Press., 2000 .

11. N. Paskin Naming and meaning of Digital Objects // Proceedings of the 2nd International Conference on Automated Production of Cross Media, 2006.

12. Y. Lipuntsov, R. Beatch, I. Collier. Financial Markets Data Collection Using the Information Model of Interagency Cooperation and the International System of Codification of Financial Instruments // Communications in Computer and Information Science. 2017. Vol. 745.

13. Y. Lipuntsov. An Information model of Interagency Communication Based on Distributed Data Storage» в International Conference on Electronic Governance and Open Society: Challenges in Eurasia (EGOSE ‘16), New York, 2016.

14. RBK. Nazvan edinyy operator po elektronnoy markirovke tovarov 12.07.2018. URL: https://www.rbc.ru/rbcfreenews/5b45ee149a79474230d-4c946?from=newsfeed. (Accessed: 08.08.2018). (In Russ.)

15. GS1. «GS1 General Specifications» URL: https://www.gs1.org/docs/barcodes/GS1_General_Specifications.pdf.

16. R. Cyganiak, M. Hausenblas, E. McCuirc. Official Statistics and the Practice of Data Fidelity. New York: Linking Government Data, 2011.

17. Rosstat. «O nekotorykh dopolnitel’nykh merakh po realizatsii gosudarstvennoy politiki v sfere gosudarstvennoy statisticheskoy deyatel’nosti v svyazi s vstupleniyem Rossiyskoy Federatsii v OESR» 2017. URL: www.gks.ru/free_doc/new_site/rosstat/os/docl2_5.doc. (Accessed: 03.05.2018). (In Russ.)

18. UK Cabinet Office «e-Government Metadata Standard» 28.08.2006. URL: www.nationalarchives.gov.uk/documents/information-management/egms-metadata-standard.pdf.

19. EuroStat. Kodeks norm evropeyskoy statistiki URL: http://ec.europa.eu/eurostat/quality.

20. R. Cyganiak, S. Field, A. Gregory и J. Tennison. Semantic Statistics: Bringing Together SDMX and SCOVO» в Richard Cyganiak Simon Field Simon Field Arofan Gregory Jeni Tennison, Semantic SProceedings of the WWW2010 Workshop on Linked Data on the Web, LDOW 2010, Raleigh, 2010. (In Russ.)