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 ...

AI-enabled analytics are transforming healthcare from static data to real-time insights. A driving force behind this shift, Kiran Veernapu is modernizing systems to improve patient outcomes and ...

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 ...

In today’s data-driven world, the ability to quickly and accurately analyze information effectively is a pivotal skill across a wide variety of different industries. If you have large amounts of data ...

As the industry adopts in-line monitoring and analytics, integrated AI-linked sensors may soon enable model-based process controls.

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