1.

INTRODUCTION

The use of data as a resource in society has promoted the rapid development of some economies¹. As the first batch of enterprises to contact and use data resources, Internet companies use algorithms to mine all kinds of data and then get the information resources needed by the company. The deep mining of data enables Internet companies to realize accurate marketing to users, fully understand user information, and then provide users with personalized services². On the one hand, it brings users fast and convenient services. On the other hand, it also enables Internet companies to have better business conditions.

But at the same time, data, as a resource, is rarely used in industry. According to the statistical results, data contribute very little to the development of China’s industry. Moreover, the data application of a large number of industrial enterprises is still a single point, local and low-level, and enterprises do not pay attention to it and do not want to use data; There is also a lack of data talent, and the cost of data application investment is large. In 2020, China’s Ministry of industry and information technology issued guiding opinions on the development of industrial big data. The guiding opinions clearly pointed out that whether it is enough or not, big data should be fully applied to the R & D and design, production and manufacturing, operation and management of industrial enterprises, promote the data haul, networking and intelligence of manufacturing industry through industrial Internet, and actively develop a new data-driven industrial development model.

Facing the problems of low level, high cost and localization of China’s industrial data use, our government is taking measures to create a better and better industrial data use environment, but the actual situation often fails to meet expectations. In the following investigation and analysis of the results, this paper will further explore the promotion of the economy to industrial enterprises and how to solve various problems existing in today’s industrial data.

This paper will explore the impact of data as a new resource on the economy at the macro and micro levels. In the analysis of the production and operation activities of enterprises, the public’s understanding of enterprise development is understood in the form of a questionnaire. By analyzing various parameters that have an impact on enterprise development, we can explore the contribution of data to productivity in the process of enterprise development³. We analyzed hundreds of small and medium-sized enterprises surveyed to understand the impact of data resources on different types of enterprises. At the same time, with the help of the analysis of various influencing factors, we can analyze the influence of data in the enterprise, so as to help the enterprise make better decisions in production and operation activities.

2.

SPECIFIC MEASUREMENT ANALYSIS

This analysis is mainly aimed at small and medium-sized enterprises, so it mainly analyzes small and medium-sized industrial enterprises with an operating income of fewer than 100 million yuan. By grading the collected company data, we divide the enterprise into six grades by using the two parameters of employees and enterprise operating income. The first prize will be awarded if the number of employees in the enterprise reaches 50 and the operating income is less than 3 million; The number of employees of the enterprise reaches 150 and the operating income of the enterprise reaches 3 million as the second level; The third level is that the number of employees reaches 250 and the business income reaches 8 million; The number of employees of the enterprise reaches 350 and the operating income of the enterprise reaches 30 million as the fourth level; The fifth grade is the enterprise with 450 employees and 50 million operating income; The number of employees of the enterprise reaches 550 and the operating income of the enterprise reaches 80 million as the sixth grade.

2.2

Relevant charts

Table 2 shows the analysis results of the model, including the model ² Normalization coefficient, t value, Vif value and R value, Adjust R ² Formulas for testing and analyzing models.

Table 2.

Analysis results.

	Standardization coefficient Beta	t	P	VIF	R
Constant	-	9.96	0.0***	-	0.99
Chairman	0.008	0.625	0.532	0.00
Financial personnel	0.252	12.64	0.0***	1.20
Number of people in purchasing department	0.114	5.710	0.0***	1.26
Number of data processing personnel	0.041	2.16	0.032*	1.18
Enterprise credit rating	0.028	1.43	0.146	0.00
Obtain beneficial development	-0.021	-1.18	0.239	0.00
Increased company expenses	-0.018	-0.86	0.390	1.37

Note:“***”: Reject the original hypothesis on the confidence interval of 99%; “*”: Reject the original hypothesis on the confidence interval of 90%.

Xgboost regression model requires that the overall regression coefficient is not 0, that is, there is a regression relationship between variables⁴. Test the model according to the F test results

R ² Represents the degree of fit of a curve regression. The closer to 1, the better the effect⁵; The value of Vif indicates the severity of multicollinearity, which is used to test whether the model has collinearity, that is, there is a high correlation between explanatory variables⁶ (Vif should be less than 10 or 5, strictly 5); B is a constant coefficient.

The standardization coefficient is the coefficient obtained from the standardization data; Vif is collinear; F (DF1, DF2) is DF1, equal to the number of independent variables; DF2=sample size - (number of independent variables+1).

The rejected original assumption that the regression coefficient is 0, and the goodness of fit of the model is r ², 0.173, the performance of the model is poor, so the model basically meets the requirements. For the collinearity of variables, Vif is less than 10, so the model does not have multiple collinearity problems, and the model is well constructed.

3.

CONCLUSIONS AND RECOMMENDATIONS

In the Internet era, data and economic development are interdependent, and the development of data resources plays a strong role in promoting the economy⁷. However, as a new resource, the use cost of data is high, and it is not fully used in various fields. At present, the data is mainly driven by Internet companies, and the data has a good promotion effect.

In the era of the Internet of things, the combination of data and industry not only provides users with better and better services but also adds new vitality to enterprises⁸. However, at present, the use of top data in China’s industrial system is good, but the use of bottom data is poor. There are problems such as low level, high cost and localization. Enterprises are unwilling to use data resources, and it is difficult for data resources to enter the enterprise. Some enterprises that use data resources suffer from low data welfare, resulting in a large number of enterprises unwilling to set up data processing departments.

However, at present, China’s data is only limited to top Internet companies, and there is not much participation in industrial manufacturing companies. Therefore, the government should increase investment in industry and use tax dividends to guide industrial companies to set up data departments.