XIV Международная студенческая научная конференция Студенческий научный форум - 2022

In the 21st century, information has become the most valuable resource, the use of which allows to reach new heights in all areas of human activity. There is so much information that it has become very difficult to store and process it in traditional ways. Besides, data processed traditionally appears, as a rule, with a delay, that in many cases does not allow to make management decisions in a timely manner, both at macro-, and microlevels. In recent decades, the role of technology in all spheres of human life has increased significantly. The technologies used in the work of any company must be newer, of higher quality and more efficient than those of competitors, as the competitive ability of enterprises is determined by the level of their technological development.

Methods and systems for collecting, processing and systematizing data are designed to solve urgent problems both at the state level and in the field of business. It is the need for fast and high-quality data processing, especially of their large volumes, that pushes forward the development and subsequent improvement of data processing technologies.

Various information systems provide the ability to store data with a high degree of reliability, duration, to conduct accounting and inventory, packaging and labeling data in their own way. To create conditions for the safe storage of the collected data, it is necessary to control access and protect this data. The ability to search for the necessary data in the accumulated arrays should be organized as well.

Big data analytics is the process of collecting and analyzing the large volume of data sets (called Big Data) to discover useful hidden patterns and other information like customer choices, market trends that can help organizations make more informed and customer-oriented business decisions. Big data is a term that characterizes the data by 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Since about 2010, “Big Data” has become the ubiquitous term to describe all the data that is generated by people from their smartphones, web browsing history, social media and purchasing behavior, together with any other information that organizations hold about them. Why is big data different to any other type of data? In one sense, there isn’t a difference; it’s all just zeros and ones at the end of the day. However, the term “Big Data” tends to be applied to large collections of different types of data which are often volatile and changeable, and where one would struggle to analyze it using traditional computer hardware and software.

Big Data represents a new source to be harnessed by official statistical agencies with the aim to produce additional information, or increase the quality of already available data, while reducing related costs. As in the case of administrative data, Big Data also can be exploited in the context of the official statistics production process.

The potential benefits to official statistical agencies and owners of public and private administrative data are very significant. For official statistical agencies, if the challenges of concepts, definitions, and representativeness can be resolved, the use of big data has the promise of more timely and detailed data at a significantly lower cost than new or expanded survey collections.

Nowadays, Big Data is regarded as an effective tool for government decision-making. One of the ways to operate with big data to regulate socio-economic and political processes is the compilation and analysis of official statistics solely on their basis and in combination with traditional sources: registers, polls, surveys, etc. The arena is seeing a growing interest in the use of big data in government statistics. The main advantage of big data in statistical research is the timely receipt of voluminous arrays of information with the lowest financial and time costs. Big data can provide a wide range of information on various aspects not covered by traditional sources. In addition, the high frequency of obtaining information in comparison with a conventional survey provides a detailed examination of processes and the solution of problems at the stage of their inception.

However, there are significant challenges to the use of big data for statistical purposes. Currently, issues of methodology, quality, data access, legislation, privacy, governance and funding are relevant. Collecting and storing big data on servers seems to be difficult due to technical possibilities at the moment, therefore cloud technologies are becoming more and more popular in statistical organizations. To ensure the availability of big data technologies to a wide range of users, it is necessary to ensure public confidence in the use of personal data. To date, few countries have developed long-term strategies for leveraging big data. In order to minimize the risks in the development of new technologies, the powers interact within the framework of the world's leading research centers, such as the UN Statistical Commission and the Global Working Group on Big Data in Official Statistics.

A number of countries have established laboratories and working groups for pilot projects to determine whether big data is suitable for use as a source of official statistics. Most of the projects are related to the production of economic and financial statistics, demographic and social statistics and price statistics. Mobile communications and the global coordinate system (GPS), geospatial information and social networks are considered as the main sources that form big data for their subsequent application in statistics. Some of this data is not in the public domain, but is the property of the private sector, so there is a need to establish interaction between statistical research bodies and companies.

In some countries, mobile communications and GPS are used to collect data on population mobility during the day and during epidemiological outbreaks, tourism statistics, help with population censuses, and transport statistics. In Colombia, recording devices are widely used, which store information about the vehicle and track its location. Satellite imagery applications in Australia provide agricultural statistics on land use and yield; in addition, in China, Colombia and Mexico, satellite information optimizes ecosystem accounting, and in Brazil, big data from meteorological stations is the foundation for water statistics. In addition, the monitoring of illicit crops is based on the use of satellite imagery. Social media, web forums and blogs are used for social statistics, with examples being the Netherlands, which uses datasets from public posts of Twitter and Facebook users to measure consumer sentiment, and Italy and China, which use the Internet for labor statistics.

In conclusion, there is already a number of positive examples of the use of big data in official statistics. Their application leads to an effective and timely solution of urgent problems of the economy, politics and the social sphere. However, the process of introducing new technologies and methods for collecting and processing data should be accelerated based on the modernization of the entire statistical system, scientific and methodological, technical and regulatory framework.

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