What is the challenge of 'data wrangling' primarily about?

The challenge of 'data wrangling' primarily focuses on the process of cleaning and transforming raw data into a format that is more suitable for analysis. This often involves preparing data manually, which can include tasks such as handling missing values, correcting inconsistencies, merging multiple data sources, and ensuring the data is structured appropriately for analytic tasks. This iterative process is crucial because the quality of the data directly impacts the validity and reliability of the insights derived from it.

While automating data retrieval and visualizing data insights are also important aspects of data handling, they fall outside the primary scope of data wrangling. Data wrangling specifically targets the preparation phase of the data lifecycle, aiming to create a clean and usable dataset for further analysis. Therefore, the correct option highlights the emphasis on the manual aspects of preparing data rather than the processes involved in data retrieval or visualization.