There is the official answer and the realistic answer (from a business perspective):
Official
Officially, the greatest benefit your Python skills will bring you is flexibility. If you need to run an economic model where you want to show gradient uncertainty or something else complex, doing that manually in any Data Visualization/Business Intelligence software is going to be a pain. Even simpler tasks, like semi-complex aggregations, will often be easier to accomplish in a few lines of Python compared to the mess they can quickly become in BI software.
Practical
Business Intelligence software—which I will include Tableau in for this answer—can handle a significant portion of real-life data analysis and data visualization steps. While they are not particularly flexible compared to code, they are good enough for day-to-day use. In general, given a typical business setting, I would readily recommend them for most users. The greatest limiting factor with all of them is that the biggest job of a business data scientist is collecting and, most importantly, cleaning data, and that boils down to either manual labor... or coding. All BI software attempts to help with automatically pulling in data and, to a lesser extent, assisting with cleaning it up. However, the real job often boils down to: "connect to these databases, clean the data, combine the data, and put them somewhere so you or someone else can visualize the data in BI software."
And that's the thing: Google Data Studio is easily the least capable of all the popular BI solutions, yet it has become my go-to solution. This is because once I prepare the data correctly, I can give it to anyone to explore, and it has the easiest/best UX. And yes, any complex statistics will happen long before it gets into any BI software (in both Tableau and Microsoft Power BI, you can also run Python directly inside the product... personally, I wouldn't recommend it, as it 1) just becomes messy and 2) pulls it out of source control), but those occur less often than one might expect.
Conclusion
If you are in the business of business intelligence, then I would wholeheartedly recommend leaning on business intelligence software as much as possible. My experience is that you have:
- What your job really is: the Data Warehouse side of things (extract your data, transform (clean it), and load (store it somewhere you can access from both your BI software and Jupyter))
- What your end users will see: the BI software for standard visualizations
- What you want it to be: the occasional Jupyter notebooks for specialized analyses
Of course, your experience might be completely different, but this has been my personal experience after working for a couple of years for a company that helped companies with their data-driven business management (and thus, I got to see how it worked in a whole bunch of companies). And, yes, often enough, all a company will be using is Excel + Power Query.
PS. Tableau tries to be this all-in-one solution. Personally, my experiences have not been positive with them, but for what it's worth, they are the most established player on the market.
