Potential of Big Data in Poverty Assessment

Nowadays a person lives in a digital world. Every day people leave a huge number of digital footprints that offer unprecedented opportunities for social scientists to deeper explore the problems of social inequality and poverty. The statistics used in the analysis of poverty are lagging, and one of the ways to verify them is through Big Data enabling us not only to determine the time-relevant socio-demographic composition of the region and the employment rate of the population, to draw up its financial profile, to identify income groups, but also to define the extent of poverty more objectively. The innovative services of digital solutions based on real-time Big Data make it possible to ensure the accuracy and wide-coverage analysis of the poverty profile as well as its territorial concentration.

At the same time, it is important to consider not only new opportunities, but also the extent of how the new research field differs from the traditional sociological field. Empirical studies on poverty conducted using Big Data and new analytical tools have transformed the research process itself. Service developers are often natural science researchers who are not always able to achieve a sociological vision of the problem. Therefore, the topical question is: to what extent do researchers in other sciences "inherit" the sociological vision of the problem under investigation? Do they contribute to new models of conceptualization? In this situation, the development of a methodology for the sociological vision of the problem of poverty based on innovative digital solutions acquires particular importance.