The Possibilities of Using Big Data Mining for Human Potential Studies

On March 30, 2022, the Institute for Statistical Studies and Economics of Knowledge of the Higher School of Economics held a webinar «iFORA Big Data Mining System: Research Possibilities for Human Potential Studies».

The speakers were Maria Svarchevskaya and Daria Sidorova, experts of the Center for Strategic Analysis and Big Data of the HSE ISSEK.


The main theses on the results of the event:

In recent years, there has been an increasing need for various analysis tools that help navigate the information flow and identify current trends. Thus, experts are given the opportunity to analyze events in close to real time mode, minimizing possible distortions and subjectivity of value judgments. In view of this agenda, the HSE ISSEK developed the iFORA Big Data Mining System.

In the conditions of technological progress, changes are also taking place in the labor market: the trend towards the emergence of completely new professions, including Internet experts, is becoming undeniable. Among the most popular platforms are social networks, where the range of specialists' activities is gradually expanding. Thus, professions such as visualizer, content manager, chatbot developer, etc. are appearing on the market. Accordingly, the importance of «digital» competencies increases: machine learning, graphic design, mobile application development, data visualization, web design.

The emergence of new trends in the field of human potential is also influenced by external factors that encourage the market to adapt to current circumstances. Thus, the COVID-19 pandemic has seriously accelerated the automation and robotization of all industries, as it allowed maintaining a social distance at the enterprise and transferring the management of many processes online.

You can get acquainted with the webinar via the link.

The webinar is organized within the framework of the world-class Human Capital Multidisciplinary Research Center at the expense of a research grant funded by the Ministry of Science and Higher Education of the Russian Federation (grant ID: 075-15-2020-928)