Structured Data refers to quantitative data, such as names, credit card numbers, and addresses, that is stored in anchored fields within a relational database (RDBMS). Structured data conform to pre-defined data models, and therefore can easily be sourced by humans or algorithms within that RDBMS structure.
Structured data exhibits in a longitudinal format, adhering to a tabular configuration with links between the adjacent rows and columns (e.g. SQL databases or Excel files). Structured data relies on data models of how to store, process, and access data. These models allow for data fields to be retrieved separately or collectively with data from other fields. Thus, structured data can be amassed from several different locations within the database.
This differs from unstructured data and semistructured data. Unstructured data does not fit into predetermined models and remains in its native format until it is analyzed. Semistructured data is data organized with tagging structure but that is still unable to fit into a formal relational database.
Structured data also makes web pages easier for search engines to find and understand by utilizing vocabulary (i.e. translating page content into code) that is easily comprehended by a search engine. Search engines "read" that code and use it to feature richer search results. There are many varieties of structured data, but they're all composed of code. This code determines the level of rich results or rich snippets. Websites use structured data to “talk to" search engines, "telling" them information such as cooking time, calorie counts, movie times, book reviews, blog posts, and more.
Structured data alters how a snippet (i.e. search results) displays on the search results page, showing more and specific information to current and potential customers, thus increasing the probability that viewers will click on those results. Structured data is increasingly powering rich results, as increased clicks lead to higher rankings.