What process involves cleaning and organizing raw data for analysis?

The process that involves cleaning and organizing raw data for analysis is known as data wrangling. This critical step in the data analysis workflow encompasses several activities aimed at transforming raw data into a more usable format. It includes tasks such as removing duplicates, handling missing values, and converting data into a consistent format, which are essential for ensuring the accuracy and reliability of any analysis that follows.

By effectively wrangling data, analysts can ensure that the datasets they work with are structured, tidy, and ready for further exploration or modeling. This preparation phase can drastically impact the quality of insights gained from the data, making it a vital part of the data science process.

The other options do not specifically represent the cleaning and organizing process. Data sources refer to the origins of data, data transformation involves the conversion processes applied to data, and unstructured data describes data that does not have a predefined format, rather than the processes involved in organizing and preparing data for analysis.