Yes, but they’re common talk around my workplace/profession (at least in much of the academic side of it. I’m not sure if it’s being talked about on the public library side).
ETA: Reading your link:
So much of this is what we already do or talk about in libraries, academic or public, anyway.
While it is true that all scientists use data, they generate and then analyze the data through observation or experimentation, and the data needs to be very specific. What you do in analytics is to collect huge amounts of data and look for correlations - something which is a big no no for a typical experiment.
For instance, WalMart may find that sales of snow shovels and hot chocolate mix are correlated, and put some hot chocolate near the snow shovel display. They don’t care about causation, they just care that they can sell some more hot chocolate.
One real example - we were having these weird failures of our chips. I analyzed where the bad parts came from, and found that there was a strong correlation between parts which tended to fail this way and location on the wafer. We were able to quarantine those locations and reduce the failure rate. We found out the root cause, but we never found out the reason for this correlation to location. But it didn’t matter.
This field is one that is going to grow, as your devices collect more and more data on you. Imagine when your phone, in order to give you information about what you are looking at, starts to track your eye movements. if you owned a store, wouldn’t it be great to get information about what window shoppers were looking at? Creepy, but that is where things are going.
If you believe that there are patterns to things, and that when you look at masses of data things don’t happen randomly, it is very cool. I’m building an infrastructure now for this. I’ve already saved some people lots of time by giving them the history of what happened to some chips, but I’m looking forward to playing with the data.