Structured data is highly-organized and follows the same format, making it easy to search, store, and analyze. It is often numeric and quantitative, meaning it can be measured and counted. Examples of structured data include dates, phone numbers, customer numbers, product databases, and invoicing systems.

Unstructured data has no predefined format or organization, making it challenging to process and extract meaning from. It is often text-heavy and non-numeric, and requires more sophisticated techniques such as data cleaning, natural language processing, and computer vision. It is often subjective and qualitative, meaning it has an interpretive nature. Examples of unstructured data include email messages, customer chats, video files, images, social media posts, and IoT sensor data.
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