People generally want to use data to help them investigate an issue or to inform a decision. In most cases this involves using data from many different sources and the challenge of combining data from different 'data silos' is well known.
Several aspects of linked data help with this problem:
- it makes data accessible through the web
- Its approach to naming conventions make the data easier to join up. Each statistic has its own, globally unique identifier (name). This means that separate datasets can re-use the same identifier when they are talking about the same thing
- data is machine readable and queryable, which helps with automated filtering and processing of large amounts of data
- in linked data, the data model or 'schema' is described in the data itself, making it possible to enrich data by adding in lookups and connections
An example of available metadata for a linked data dataset taken from environment.data.gov.uk
The linked data representation of information is a very precise one - it enables you, and sometimes requires you, to be explicit about aspects of the data that other approaches might leave implicit or open to interpretation. This makes it excellent for automated processing but requires some extra effort. The process of cleaning and transforming the data often helps expose mistakes or inconsistencies that might otherwise go unnoticed, and so helps improve data quality as a side effect of the data publishing process.