The advent of the big data era not only makes more and more people realize the importance of "data", but also, thanks to the advancement of data science and intelligent algorithms, "Business Intelligence" (BI) has also officially entered Public vision. If traditional BI is the path for managers to fully understand the enterprise, and it presents the past and current status of the enterprise through various types of data, then the future demand for enterprise BI lies in the discovery, prediction and verification of new business.
With the continuous improvement of corporate data awareness and data literacy, and following the development route of "data-knowledge-ability-wisdom", BI is ushering in a new round of explosion. According to the Gartner Business Intelligence Report, the global business intelligence market capacity is expected to reach US$22.8 billion by 2020. The rapid development of big data, artificial intelligence, machine learning, deep learning and other technologies is promoting a new round of evolution of BI and data analysis products. These trends in the future development of BI deserve the attention of business managers——
Although self-service data discovery has become the standard, BI products will further improve the function and operability of self-service analysis, and meet the needs of business personnel to customize the front-end layout. The presentation of results needs to increase the richness of charts.
The future development trend of BI mainly includes five categories, which are process sharing and real-time intercommunication; real-time response needs are more important; embedded BI is popular; cloud storage and cloud computing; and platform security is improved.
1. Process sharing, real-time intercommunication
As part of the analysis process, different business personnel need to connect task data. In order to adapt to more diverse users, the big data BI platform needs to support powerful OLAP multidimensional analysis functions to satisfy users' sharing and communication of data pages.
2. The need for real-time response is even more important
The powerful data calculation processing engine of the BI tool can reduce the waiting time cost of enterprise data query and improve the efficiency of business data analysis. At the same time, by directly connecting to the enterprise big data platform, it can also meet the needs of enterprises for real-time data analysis.
3. Embedded BI is popular
In order to meet the needs of enterprise business personnel for data mining, the big data BI platform needs to support embedded advanced analysis functions. Users can easily use the built-in advanced analysis functions of the platform, or import and integrate externally developed advanced analysis models.
4. Cloud storage and cloud computing
As companies move data to the cloud, cloud data warehouses will become a popular data target. Although many will choose a hybrid architecture of cloud and local solutions, cloud computing still represents a faster and more scalable solution.
5. Improve platform security
In order to meet the functions of multi-level departments in the enterprise with different permissions at the same time, in addition to providing flexible and rich user management functions and permission control functions, China's big data BI products also need to have built-in powerful user behavior monitoring and analysis functions to ensure enterprise data security And information confidentiality.
And Sematic software Smartbi is also moving towards this trend, improving the software step by step. As a leading domestic business intelligence BI manufacturer, it connects to various business databases, data warehouses and big data platforms for processing, analysis, mining and visualization; it meets the needs of various data analysis applications, such as enterprise reporting platforms, self-exploration and analysis, and maps Visualization, mobile management cockpit, command large screen, data mining, etc. The Smartbi product has a comprehensive functional design, covering the four major links of data extraction, data management, data analysis, and data sharing. It helps customers describe business status, analyze business reasons, predict business trends, and drive business changes from a data perspective. It is widely used in finance, government, and Universities, manufacturing, real estate, communications and other fields.