Final answer:
Dividing a column into multiple columns generally involves parsing and possibly normalization, while checking data correctness involves validation.
Step-by-step explanation:
Dividing a single column into multiple columns typically involves the process of parsing, where a single string of data is divided into parts based on certain delimiters or patterns, potentially as a step in normalization to organize the data into a more accessible format. Regarding your second question, checking the data for correctness involves validation to ensure that it meets the required criteria and format. Aggregation is more frequently associated with combining multiple pieces of data into a single summary value, while filtering is applying criteria to select certain data and indexing is more about optimizing access to data rather than verifying its correctness.
In the context provided, labeling a column as 'Quantity/units' is an example of presenting data in a clear and understandable manner, indicating how units relate to the quantity to produce a straightforward, interpretable number.