Structured and unstructured data are two types of data that differ in their organization and format. Structured data is highly specific and is stored in a predefined format, such as relational databases, and consists of clearly defined data types with patterns that make them easily searchable. On the other hand, unstructured data is a compilation of many varied types of data that are stored in its native format and not processed until used, which is known as schema-on-read. Unstructured data has no predefined structure or data model, and it is usually not as easily searchable as structured data. Examples of unstructured data include text, social media activity, video files, audio files, surveillance imagery, and various other file formats.
Here are some key differences between structured and unstructured data:
Structured Data
- Consists of clearly defined data types with patterns that make them easily searchable
- Stored in a predefined format, such as relational databases
- Lives in rows and columns and can be mapped into predefined fields
- Often quantitative data, meaning it usually consists of hard numbers or things that can be counted
Unstructured Data
- A compilation of many varied types of data that are stored in its native format and not processed until used
- Has no predefined structure or data model
- May have an internal structure but is not structured via predefined data models or schema
- May be textual or non-textual and human- or machine-generated
Structured data is usually easier to search and use, while unstructured data involves more complex search and analysis. While structured data analytics is a mature process and technology, unstructured data analytics is a nascent industry with a lot of new investment in research and development. The structured data versus unstructured data issue within corporations is deciding if they should invest in analytics for unstructured data and determining if it is possible to aggregate the two into better business intelligence.
