Intelligent Make Policy Support Systemic for Business Management Based on Big Data Analysis

Abstract

With the development of message technology, big data is connected with business management. The research content and present situation of intelligent make policy support systemic in big data are introduced. Based on the knowledge-based systemic and its relationship with make policy-making process, the knowledge and knowledge processing systemic in IDSS are discussed. With the development of computer and its network, many enterprises have established their own message management systemics. How to observe the enterprise itself and the outside world in a flexible, real-time, vertical and horizontal, dynamic and slicing way, so as to capture relevant message for the development needs of the enterprise. The rapid development of big data has aroused widespread concern and attention at home and abroad. Scientific and effective analysis and processing of big data are the core issues in the field of big data. This paper summarizes the research status of big data analysis at home and abroad from three aspects: analysis as a service, big data analysis methods, and big data-driven science germination through literature review. By analyzing the statistical and semantic features of the existing data, we can find out the rules and then summarize them into an abstract data analysis model, thus providing the basis for data analysis, and the establishment of data graph and model can better reflect the specific content of the study. In the research of big data, it is found that both traditional data and big data have packet loss rates. According to the research, the packet loss rate of traditional data is as high as 70.9%, but that of big data is only 34.5%. Making a formula in the calculation of big data can help us to observe more. Through this research, it is found that big data has a more direct relationship with business management, and it controls various ways of enterprises. The era of big data has come, and all walks of life are faced with unprecedented data volume and data analysis requirements.