There are two angles to this. One, there could be some possible use for the more detailed data - but I don't want to be inventing fictious and biased examples so let's just acknowledge a possibility.
But the second, more important angle is that at this scale, there is no such thing as "anonymized data". There have already been papers showing that what is now called "anomymized" can be trivially deanonymized by mashing together other data sets and some computing power. Trying to hold on to the concept of anonymized data is futile - the reality itself is highly correlated and interconnected, and all those causal relationships will be reflected in the colected datasets. The more data sources you have, the easier is to deanonymize them all together.
So the choice is - either stop collecting data or accept that there ain't such thing as anonymity. What we definitely shouldn't do is fooling ourselves that we can do both. Datasets are and will be trivially deanonymized if anyone cares to do so.
EDIT:
> Data doesn't need names, places, faces, phone numbers, geolocation and social media accounts correlated and updated in realtime
Names - social studies. Places - social studies, urban optimization. Faces - social and medical studies. Phone numbers - dunno, probably something. Geolocation - urban optimization, crime prevention, social studies once again. The more data you have, the more interesting experiments you can devise.
I'm pushing that social studies example for a simple reason - this level of data collection is a holy grail for psychology, sociology, et al. Right now those sciences mostly keep faking "lifelike" conditions to test some theories and we all know how much problem there is with small sample sizes, unrealistic experiment setups, etc. Inferences based on natural patterns of behaviour of millions, or even billions of people would be much, much more reliable. We could finally learn a lot about ourselves, just by observing.