標題: In order to open up multiple data sources Aishu has explored the integration [打印本頁] 作者: mamunur4685400 時間: 16:36 標題: In order to open up multiple data sources Aishu has explored the integration Not only big companies like Baidu have begun to develop GBI generative business intelligence products SaaS manufacturers have begun to delve into the PaaS layer and database companies have also provided BI services to enterprises based on the analysis capabilities of AP. For a time BI has become the data industry chain. At the intersection the entire industry structure is also facing a reshuffle. The further improvement of intelligence requirements for data capabilities has allowed the capabilities accumulated by lowerlevel data vendors to be released in the era of large models.
For example regarding the implementation of large models in the indu Job Seekers Phone Numbers List stry there are key problems in the data layer hallucination problems inexplicability security risks etc. Aisu believes that this is not a problem with large models but a data problem. The basic capabilities of data vendors are also the core capabilities required for the next stage of BI intelligence. For example the perennial data island problem in enterprises makes it impossible to achieve effective connection and unified query utilization of data. Moreover duplicate and redundant data exist in multiple systems resulting in a waste of storage and computing resources.
There are inconsistent versions of the same data in different systems and data quality cannot be effectively controlled. This will seriously affect the analysis results and accuracy. This problem cannot be solved by replacing the previous system changing tools or buying some SaaS products for local diagnosis. Enterprises need systematic and global BI products.of data pipelines and data lakes and warehouses and has also applied this exploration to RGA retrieval augmentation technology. When large language model inference generates answers additional retrieval calls are made. The external domain knowledge network uses comprehensive search results to generate answers.
