Study on financial internal control strategies of SMEs in the context of big data

FangXuan Lin

doi:10.1117/12.2682573

26 May 2023 Study on financial internal control strategies of SMEs in the context of big data

FangXuan Lin

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127003R (2023) https://doi.org/10.1117/12.2682573
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

Today, there has been a great shift in the way data is utilized, and data has risen to become a new factor of production and a new driving force for business development in the digital era. The commercial application of new-generation information technology has reduced the cost of data processing, accelerated the accumulation of data resources, and given rise to new business development models. These enterprises use data as "raw materials" and digital technology as means to provide big data products and services to the outside world, which are called big data service-oriented enterprises. With the recognition of the value of data by the whole society and the emphasis on digital transformation, the big data service industry is in an accelerated expansion stage. On the contrary, in academia, there is still a gap in the research on the value of big data service-based enterprises. The special nature of the business model of big data service enterprises and the diversity of value influencing factors cause uncertainty in the future earnings of the enterprises, comparable companies are difficult to find, the reports cannot reflect the overall value of the enterprises, and the application of traditional valuation methods is limited. In this context, this paper examines the issue of valuing big data service companies.

Citation Download Citation

FangXuan Lin "Study on financial internal control strategies of SMEs in the context of big data", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127003R (26 May 2023); https://doi.org/10.1117/12.2682573

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KEYWORDS

Data modeling

Neural networks

Deep learning

Error analysis

Samarium

Machine learning

Education and training

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