Through normalisation, the tendency is to start with a data set, and by a methodical process extract candidate keys and their dependent attributes. In many cases there isn't a genuine or usable candidate key and artificial / surrogate keys need to be generated. While your bank can generally work out who you are based on your name and address, either of those could change and so they assign you a more permanent customer or account number.
The difficulty comes when those identifiers take on a life of their own.
In the dark ages, the phone number would indicate a particular exchange and a copper wire leading out of that exchange hard wired to a receiver (or a set of receivers in the case of Party Lines). Now all the routing is electronic, telephones can be mobile and the routing for calls to a particular number can be changed in an instant. A phone number no longer identifies a device, but a service, and a new collection of other identifiers have risen up to support the implementation of that service. An IMEI can identify a mobile handset and the IMSI indicates a SIM card from a network provider, and we can change the SIM card / IMSI that corresponds to a phone number, or swap SIM cards between handsets. Outside the cellular world, VOIP can shunt 'phone number' calls around innumerable devices using IP addresses.
Time is another factor. While I may 'own' a given phone number at a particular time, I may give that up and someone else might take it over. That may get represented by adding dates, or date ranges to the key, or it can be looked at as a sequence. For example, Elizabeth Taylor's husband may indicate one of seven men depending on context. The "fourth husband" or "her husband on 1st Jan 1960" would be Eddie Fisher.
Those without a data modelling background that includes normalisation may flinch at the proliferation of entities and tables in a relational environment. As developers and architects look at newer technologies some of the discipline of the relational model will be passed over. Ephemeral transactions can cluster the attributes together in XML or JSON formats with no need for consistency of data definitions beyond the period of processing. Data warehousing quickly discarded relational formats in favour of 'facts' and 'dimensions'.
The burden of managing a continuous and connected set of data extending over a long period of time, during which the identifiers and attributes morph, is an ongoing challenge in database design.