A Data Standard is a published specification or set of rules agreed upon by a community of users on how data should be gathered, stored, and shared. This can include rules on:
Data standards can improve data interoperability and data quality.
Following a standard prevents apples to oranges comparison or the mistake of comparing two different datasets. For example, one dataset uses centimeters to record lengths while another uses inches, we can’t just compare the values recorded in the two datasets. On the other hand, if we have two datasets that follow the same standard, then we can be sure that these datasets are compatible, meaning that a process or tool that was used for one dataset can safely be used for the other. Data standards are also important because almost all work we do with data requires us to use data from different sources and knowing how different datasets relate to each other can save us a lot of time.
Standardization can help ensure that data is compatible and comparable among different data users and providers thus allowing everyone to realize mutual gains.
Another thing that data standards improve is data quality. By having a standard, we are able to separate the bad apples from the good apples. By conforming to a standard, we make sure that our data is usable for its intended use.
Standardization can help maintain the quality of our data and decisions by ensuring that only data that meet certain criteria are considered and accepted.