Structured data is data in a standardized format, has a well-defined structure, complies with a data model, follows a continual order, and is easily accessed by humans and computer programs. Structured data is often stored in a relational database (RDBMS).
Unstructured data does not conform to a model, has no identifiable structure or organization, and cannot be stored in any logical way. Unstructured data has no format or rules and are stored in non-relational databases, or NoSQL, databases.
Structured data tends to have a range of common characteristics, such as:
An identifiable structure that conforms to a data model
Presented in rows and columns, such as in a relational database
Organized so that the definition, format, and meaning of the data are understood
Fixed fields in a file or record
Similar groups of data clustered together in classes
Data in the same group have shared attributes or types
Information is easy to access and query for humans and other programs
Structured data is more easily used by machine learning algorithms. Organized “structured” data used in machine learning algorithms is easier for the algorithm to understand when compared to unstructured data. It also allows for easier manipulation and querying of the data.
An additional benefit of structured data is that it can more easily be used by average business users who have high-level knowledge of the data topic. This removes the need for an in-depth understanding of different data relationships.
Structured data has a long history of use in comparison to unstructured data. This results in more tools built for structured data analysis giving data managers more product choices when compared to tools built for unstructured data.
The metadata (post id, hashtags, user, date, comments, likes, share counts, etc.) from social media is structured; the content itself is unstructured.
Social media content
There are numerous ways that unstructured data can be stored, such as:
Excel and Google Sheets