What best describes data wrangling?

Data wrangling, often referred to as data munging, is a crucial step in the data preparation process, which involves transforming and cleaning datasets to make them suitable for analysis. The essence of data wrangling lies in its ability to convert raw data into a more understandable and usable format by performing various tasks. This includes removing inconsistencies, handling missing values, reshaping the data structure, and enriching data for better context.

The choice that describes this process accurately emphasizes the methodical steps taken to convert disparate and often messy datasets into a cohesive, meaningful dataset, which is essential for drawing insights through analysis and reporting. Therefore, this option clearly illustrates the comprehensive nature of data wrangling.

Other choices do not align with the definition of data wrangling. Deleting data does not encompass the transformative aspect essential to wrangling, nor does storing large datasets focus on the manipulation and organization of data for analysis. Similarly, generating raw data from structured formats does not capture the primary objectives of data wrangling, which aims to refine and prepare data rather than create it.