What action may be included in the data-cleaning process?

Standardizing data sets is a critical step in the data-cleaning process, as it ensures that data is consistent and in a uniform format. This involves transforming data into a standard format or structure, which can help improve the accuracy and reliability of analyses conducted later on. For example, standardization may include ensuring consistent date formats, units of measurement, or naming conventions across various datasets. This reduces confusion, minimizes errors, and facilitates comparison and integration of data from different sources.

In contrast, while analyzing data relationships and calculating data statistics are important for understanding data, they do not modify the data itself in the way standardization does. Creating data visuals is necessary for interpreting and presenting data but is typically done after data cleaning and standardization have occurred, rather than being part of the cleaning process itself.