The concept of GUI is general. It just means providing an event-driven user interface managed with the mouse (the most classical graphical interfacing device). Usually, a GUI is based on menus and dialog boxes. Regarding the data analysis, there are different ways to organize the commands and to display both the data and the results. Here is a typology of the different GUIs that have been used so far in data analysis:
One GUI cannot be said to be better than another, nor a GUI is better than the CLI. The answer depends upon the context, that is, the environment, the analysis and the user. Giving a general data analysis software should adapt to the context, the best user interface is perhaps the most versatile one. It should offer various ways to drive the analyses: CLI, MDB, spreadsheet or notebook, depending on the user's needs and preferences.
The environment, mainly the platform imposes some habits to the user. For instance, most Unix/Linux users are accustomed to a CLI, while Windows/MacOS users are prone to prefer a GUI with the same look and feel as their preferred operating system. The way the data analysis package is used is also a major criterion. Whether for teaching, for research or for demonstration... Finally, the user skill is key aspect. A beginner prefers a point and click user interface, while the expert likes to get full control of the process through a CLI, and consequently, dislikes rigid and limited GUIs. This latter aspect is controversial. Many teachers prefer to use a CLI, even with their beginner students, because it forces good habits in data analysis (the user must really know what he is doing). On the contrary, a beginner in front of a simple GUI can click everywhere and get results anyway, even if these results are meaningless.