Absolutely love it.
Let's distinguish a few things though. "Data science" seems like a pretty weird name. I mean, it's just "Science" right. Of course there's statistics, mathematics, signal processing, systems analysis, machine learning... all the good things that you and I are into.
But how does this get huddled uncomfortably beneath the umbrella "Data science"?
I think the answer is found by asking about the ends of data science, the old Cui Bono?
There's the raw entertainment value you mention. It's cool to have knowledge and visualise it. Sensors, transducers, processing is fun.
Then there's legibility. That is political and is about control.
What most scientists are doing with data is either hypothesis testing or combing for causal relations to then abductively feed back into hypothesis generation.
What most business people are trying to do is optimise, and adjust constraints and parameters. It's modelling for the most-part. It's ancient and goes back to linear analysis and regression from before the last century.
Security people are looking for stress signifiers, suspicious patterns with various triggers, selectors and tripwires.
Financial people want fortune telling. They want the models to extrapolate into beautiful hockey sticks.
Within any organisation we may need to do one, a few, many or none at all of the above. The problem then is that "Valuable data" is such a broad, open prospect it seduces gushing, credulous administrators into valuing the process, and the tools, but not the ends.
Instead we have social science, computer science and now data science.
Like you said, whether or not the visuals provide any value is a completely different line of discussion.
Have you looked at the Financial Modeling World Cup? https://www.youtube.com/channel/UCOlnCUAKLENyFC8wftR-oNw
Its tagline is: "Excel Esports. Yes, It's a thing"