- Structured data conforms to a data model and is usually presented in rows and columns, as in a relational database.
- Unstructured data has no identifiable structure or format and is typically stored in non-relational (NoSQL) databases.
- Machine learning algorithms use structured data more easily than unstructured data.
- Social media metadata such as post ID, hashtags and like counts is structured; the post content itself is unstructured.
- Email, text files, text messages, voicemails and instant messages are common examples of unstructured data.
Unstructured Data
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.
Common Characteristics of Structured Data
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
Why Structured Data is Important in Machine Learning
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.