I spent some time working in the data industry and was fascinated with this topic as well

dont forget the data itself is already a compression of reality. text data has lost tone, situational context, emphasis, etc.

then youve also got different data that yields the roughly same information that isnt exact but serves the same purpose. for example, birthday vs age; one can be used to infer the other although it is more accurate moving in one direction than the other.

youve got resolution of data. for example, A specific hexcode value vs "deep navy blue" vs "blue". sometimes the signal is low resolution but the data type is high resolution and you end up with some tells like when a lot of date data is the 1st of the month or 1st day of the year; implying the signal was at the month level or year level, no day signal available. which to call back an earlier example, can be caused by getting a signal for age but storing it in a birthday data type field

to your point, often the "data" signal is not a single field but set, and then youve got your source updating every month or year, and its not always the same format or exact set, maybe it was collected differently or two or more different sources contributed. its hard to apply any rules without assessing the type of data being considered. newer data that is missing older data, sometimes you carry the old pieces forward and sometimes you dont. sometimes you change the shape of the data by inferring events. job title changed? well the old job title isnt wrong data, its just not their current title and now can be moved to a past job titles" field. maybe past job titles arent something that add value to your use case and it can be dropped, etc.