Application of business modeling tools in the context of the innovation development of the digital economy in China

Faculty of Innovation Management, Center of Management of Industries, Department of Applied Economics, People’s Friendship University of Russia (RUDN University), Moscow, Russia

^* Corresponding author: strelka5@bk.ru

Abstract

With the development of China’s digital economy, a large number of high-tech enterprises have emerged, and the application of business model tools for enterprises has also been innovatively developed. Big data technology has been widely used in business process management. This article analyzed from the perspective of big data to discuss the inovative application of business modeling tools. The article first analyzed the current status of data-driven research in the field of business process management, and expounds the use of big data analysis methods to establish a BPM knowledge base to guide, optimize, and predict the flow of business processes. From the perspective of big data, use the results of data analysis to drive the flow of business processes. The article analyzes and summarizes the core links of building a data-driven BPM. On this basis, the establishment of a data-driven BPM implementation process is described in detail. Finally, the development trend and research challenges of data-driven BPM are given. It has great reference significance for the business process management of Chinese high-tech enterprises in the development environment of the digital economy. At the same time, it also has a certain reference significance for the innovation of business model tools in Russia, helping enterprises to better manage. This article analyzes the application and development of business modeling tools from the perspective of big data, which is in line with the current development direction of the times. At present, most of the literature is studying the construction of BPM process. This article mainly studies the optimization of the process after construction and the processing of data in the process, and gives the development trend and research challenges of data-driven BPM.