In other realms, we don't call this data but intelligence. Humans will still make the decisions, but in more and more places, the decisions are more likely to be informed by data (intelligence).
Consider that Netflix's data is tiny compared to the amount of data that any government must sift through. Algorithms don't make government decisions, people do. But they (hopefully) base those decisions based on intelligence (some of) which was gathered and filtered by algorithms, then synthesized by humans.
In Health Informatics at least this is taken even further
Data
X of Y people have some disease
Information
Based on data, I can predict disease likelihood
given some environmental and personal factors of
a patient
Knowledge
Using informed predictions, I make good inferences
about how to proceed with diagnosis
Wisdom
Using knowledge and experience I choose the right
approach for treating and diagnosing a patient
which is efficacious, healthy, and works with the
patient's actual needs
It's easy to draw these lines in other places or to call the tower a lot of woo woo able to be reduced into inferences atop raw data all combined correctly... but it serves to remind just how difficult it is to combine the right data in the right way to make the right decisions at the right times.
It also serves as a sharp counterpoint to the idea of, say, machine learning patient diagnoses. It turns out that diagnostic accuracy is terrible, but not because people are directly bad at it (even if they are) but instead because knowledge/wisdom dictates that perfect accuracy isn't that valuable---perfect care is and that can involve chasing down treatment and care avenues that would never be predicted or acting on information that is not currently in your model.
I am not entirely sure what you are saying here, but the way I have always understood it is: data is knowledge; the ability to apply knowledge is intelligence.
Netflix has a considerable amount of data (knowledge) and its algorithms exemplify some efforts to apply that knowledge (intelligence). As it stands presently, though, humans still tend to be more intelligent than any algorithms we have created. (Generally speaking, of course.)
I think we are trying to say the same things here, right?
I'm trying to reframe what Netflix does as something that is already done - we just tend to use different names for it in those other domains. What's new is that we're doing this old thing - processing massive amounts of data, extracting the relevant facts, synthesizing those facts into a coherent story, and presenting that story to decision makers - in new contexts.
In the Netflix context, what is mostly called "data" is called "intelligence" in, say, government decision making.
This is another facet of the "amplified-teams" trend that's been happening over the past few years. This book review of 'Average is over' has some good information about it:
"In his vision intelligent machines will revolutionize everything from medicine to education to business management and negotiation to love. The human beings who will best thrive in this new environment will be those whose work best complements that of intelligent machines, and this will be the case all the way from the factory floor to the classroom."
Perhaps another way of stating that is: The people who will best thrive are those whose work cannot be automated. Which, in some ways, has always been true.
Consider that Netflix's data is tiny compared to the amount of data that any government must sift through. Algorithms don't make government decisions, people do. But they (hopefully) base those decisions based on intelligence (some of) which was gathered and filtered by algorithms, then synthesized by humans.