Editing Granularity (section)

==Data and information==
{{See also|Significant figures}}
{{Unreferenced section|date=November 2019}}

The ''granularity'' of data refers to the size in which data fields are sub-divided. For example, a postal address can be recorded, with ''coarse granularity'', as a single field:

# address = 200 2nd Ave S #358, St. Petersburg, FL 33701-4313 USA

or, with ''fine granularity'', as multiple fields:
# street address = 200 2nd Ave S #358
# city = St. Petersburg
# state = FL
# postal code = 33701-4313
# country = USA

or even finer granularity:
# street = 2nd Ave S
# address number = 200
# suite/apartment = #358
# city = St. Petersburg
# state = FL
# postal-code = 33701
# postal-code-add-on = 4313
# country = USA

Finer granularity has [[computational overhead|overhead]]s for data input and storage. This manifests itself in a higher number of [[object (computer science)|object]]s and [[method (computer science)|methods]] in the [[object-oriented programming]] paradigm or more [[subroutine]] calls for [[procedural programming]] and [[parallel computing]] environments. It does however offer benefits in flexibility of data processing in treating each data field in isolation if required. A performance problem caused by excessive granularity may not reveal itself until [[scalability]] becomes an issue.

Within [[database design]] and [[data warehouse]] design, [[data grain]] can also refer to the smallest combination of columns in a table which makes the rows (also called records) unique.<ref>[https://docs.getdbt.com/terms/grain Data grain: What granularity means in terms of data modeling]</ref>