What is data wrangling?

Data wrangling is specifically focused on the transformation and preparation of raw data for analysis, which makes the chosen option the best answer. This process involves cleaning the data, which means correcting or removing any inaccuracies or inconsistencies, structuring it into a format that is easier to work with, and enriching it by adding relevant external information or deriving new variables. These steps are fundamental for analysts and data scientists to ensure that the data they are working with is reliable and suitable for the intended analysis, such as statistical modeling, machine learning, or reporting.

The other options do not capture the essence of data wrangling. Visualization pertains to the graphical representation of datasets, which is a distinct step that follows data wrangling. Securing data against breaches refers to data privacy and cybersecurity measures, which are crucial but unrelated to the data preparation process. Lastly, sharing data across platforms focuses on data interoperability and dissemination rather than preparation, which is at the core of what data wrangling entails.