What is the primary action taken to improve the quality of data during the cleaning process?

The primary action taken to improve the quality of data during the cleaning process is to fix it. Data cleaning involves identifying and correcting errors or inconsistencies in the data set. This can include a variety of actions, such as correcting typos, filling in missing values, standardizing formats, and removing duplicate entries. The goal of fixing data is to enhance its accuracy and reliability for analysis. High-quality data is crucial for making informed decisions based on analytics, as errors in the data can lead to misleading insights and conclusions.

Ignoring data would leave any issues unaddressed, leading to potential inaccuracies in analysis. Deleting data may resolve some problems but could result in loss of valuable information that could have been salvageable through correction. Archiving data may be useful for storage, but it does not actively improve data quality. Therefore, fixing the data is the most effective approach for ensuring it is suitable for analysis and decision-making.