They don't really mean anything, you can't draw actionable conclusions, and no one will even read the report after the first week, but you gather the metrics anyway to satiate the Metrics God's hunger.
Curious too see exactly what they are.
In terms of some real world examples:
Labour force statistics are almost always reported on an instance level by commentators/media, but its trend that's recommended/meaningful.
Net promoter scores and breakdowns are consistently done and reported too frequently on too small a base to establish a trend, and they're usually cross tabulated and spoke on far too many dimensions.
Customer opinions and employee ratings/surveys.
Generally it's just fundamental statistics: don't reason too much about the general from the specific. And as you get greater resolution and move towards real time events, your specific becomes smaller and smaller, which means that extrapolations onto the general result in greater and greater errors...
It is also a common tread in books by Tufte : https://en.wikipedia.org/wiki/Edward_Tufte
I like "Visual Explanations: Images and Quantities, Evidence and Narrative" : https://www.amazon.com/Visual-Explanations-Quantities-Eviden...
Speaking as an analyst, you'd be amazed (or not) at how much analytics is done either for resume padding, because one guy thinks it's "cool", or to enable marketing or executives to chase their own tails.
Indeed, with the general phenomenon of bullshit jobs, infinite instantaneous and always changing information is a welcome smokescreen, because it always allows people to "justify doing something". With such numbers, there is always something for them to do :p