On Enterprises’ Total Budget Management Based on Big Data Analysis

• Main Authors: Hangjun Zhou, Jie Li, Jing Wang, Jiayi Ren, Peng Guo, Jianjun Zhang
• Format: Article
• Language: English
• Published: Springer, Cham 2020
• Subjects: H Social Sciences (General)
• Online Access: https://eprints.ums.edu.my/id/eprint/26256/1/On%20Enterprises%E2%80%99%20Total%20Budget%20Management%20Based%20on%20Big%20Data%20Analysis.pdf; https://eprints.ums.edu.my/id/eprint/26256/; https://doi.org/10.1007/978-3-030-57884-8_19

At present, there is a delay in the transmission and acceptance of information in the total budget management of enterprises, which can only provide a basis for short-term decision-making, but has limitations on long-term decision-making. However, big data analysis can increase the efficiency of capital operations, carry out budget supervision and help companies make long-term decisions. This dissertation attempts to solve the problems above by using the Lasso method and the GM-Model. Based on big data, a series of experiments are carried out on the comprehensive budget management of enterprises to study the methods and application effects of big data analysis and prediction of enterprise income. Finally, through experiments, it is found that the predicted value of the first three years is larger than the actual value, and the deviation is gradually reduced in the following years. However, the actual income in 2001 is almost the same as the predicted value. These results indicate that using this method more accurately requires a large data from different years to support and operate.