For sensor data analytics, you are frequently using many orthogonal sensor data sources to measure the same thing, precisely so that you can remove source bias. And most non-trivial sensor analytics are not statistical aggregates but graph reconstructions, the latter being greatly helped by having as much data as you can get your hands on.
The "let's store everything" isn't being done for fun; it is rather expensive. For sophisticated sensor analytics though, it is essentially table stakes. There are data models where it is difficult to get reliable insights with less than a 100 trillion records. (Tangent: you can start to see the limit of 64-bit integers on the far horizon, same way it was with 32-bit integers decades ago.)
Some remote sensing data models. Many population behavior data models; you discover that these are mostly garbage if you actually ground truth them unless you have completely unreasonable quantities of data.
The "let's store everything" isn't being done for fun; it is rather expensive. For sophisticated sensor analytics though, it is essentially table stakes. There are data models where it is difficult to get reliable insights with less than a 100 trillion records. (Tangent: you can start to see the limit of 64-bit integers on the far horizon, same way it was with 32-bit integers decades ago.)