The core value of conversational BI lies in lowering the threshold for data analysis and promoting "data democratization." Traditional BI requires users to master SQL, OLAP, and possess data thinking ...

The design of your study, the research questions you’ve posed, and types of data you’ve collected (e.g., quantitative, qualitative) are important considerations in determining the data analysis and ...

Leveraging AI to help analyze and visualize data gathered from a variety of data sets enables data-driven insights and fast analysis without the high costs of talent and technology. In today's ...

Improving your data analysis and result presentation skills is essential for making data-driven decisions and effectively communicating insights. Mastering these skills involves a systematic approach ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

Fanruan FineChatBI: Summary of the Conversational BI Innovation Data Analysis Process Report ...