> For the complete documentation index, see [llms.txt](https://machine-learning.paperspace.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://machine-learning.paperspace.com/wiki/structured-vs-unstructured-data.md).

# Structured vs Unstructured Data

![Source: igneous.io](/files/-LwQbPMXwVHshv40D9bA)

### Structured

Structured data is organized and searchable and is the easiest to work with. Examples include names, dates, addresses, credit card numbers, stock information, etc.

### Unstructured 

Unstructured data is much more prevalent. Examples include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery, etc.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://machine-learning.paperspace.com/wiki/structured-vs-unstructured-data.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.