1. Structured Data: This type of data is organized according to pre-defined models such as databases, flat files, and spreadsheets. Structured data makes it easier to process and analyze the data since they have an established format.

2. Unstructured Data: This type of data is not organized according to a specific model or schema and cannot be easily integrated into traditional databases or programming languages. Examples include web logs, social media posts, images, videos, emails and text documents among others.

3. Semi-Structured Data: Also known as ‘meshed’ or ‘hybrid’ data structure, semi-structured data contains elements of both structured and unstructured forms of big data; meaning that there may be some organization present but it is not as rigid as with structured formats like tables/databases or XML documents. Examples include JSON (JavaScript Object Notation), CSV (Comma Separated Values) files etc..

4. Multimedia Data: Multimedia Big Data refers to digital audio/video recordings containing vast amounts information which require powerful systems for real time analysis in order to extract valuable insights from them (e.g.: facial recognition).