Methodology and Tools of Smart Statistics within Data-Driven Management System in the Tourism and Hospitality Sector

Abstract

The article contains results of a comprehensive research of the methodological framework and tools of smart statistics in the context of transformations in the tourist and hospitality management system, based on the data-drive approach. The need for replacing the traditional model of official statistics, based on regular surveys and aggregated indicators, with an integrated digital statistical system combining administrative data, private-sector data, and alternative information sources is substantiated. The role of international statistical standards in assuring methodological consistency and comparability of data is outlined. It specifically concerns International Recommendations for Tourism Statistics and Tourism Satellite Account laying ground for measuring economic parameters of tourist activities. It is shown that the adaptation of these standards to the digital economic context requires the extension of tools by engaging big data, digital footprints of users, and streaming data. The essence of smart statistics as an integrated system for production of statistical data, operated by processing models GSBPM та GAMSO and ensuring automation, scaling and flexibility of statistical processes is highlighted. Special emphasis is placed on methods for integrating heterogeneous data, in particular the data from online booking platforms, social networks, search engines, and geospatial services. Advanced approaches to analysis of big data in the tourist sector are summed up: methods for processing feedback tests, analysis of consumer behavior, prediction of demand, and identification of spatial and temporal patterns in the tourist activity. The expediency of using composite indicators as a tool for the integrated assessment of trends in the tourist and hospitality sector in view of multidimensional and fragmented nature of data is substantiated. A conceptual model for smart statistics tools is proposed, incorporating the subsystems for data collection, integration, processing, analysis, and visualization, and designed to support management decision-making in the mode approximated to real time. It is demonstrated that smart statistics, once introduced, will enhance the accuracy of estimates, shorten time lags, extend analytical capacities, and improve the adaptability of the management system to volatilities in the internal environment. The practical significance of the research results is their applicability for public administration bodies, tourist administrations, and tourist-and-hospitality businesses for setting up effective data-driven development strategy, competitiveness enhancement, and ensuring the sustainability of this sector.