Which process is involved in data cleaning?

Data cleaning, also known as data cleansing, is a critical process in data management that involves detecting and correcting errors or inaccuracies in the data set. This can include addressing issues such as duplicates, missing values, inconsistencies, and inaccuracies. Ensuring the quality and integrity of the data is essential for accurate analysis and decision-making.

Detecting and correcting inaccurate records ensures that the insights derived from the data are reliable. For instance, if a customer database contains incorrect addresses or erroneous transaction amounts, these inaccuracies need to be resolved to draw meaningful conclusions or generate effective business strategies.

Creating new data entries, analyzing customer behavior, and collecting data from user feedback, while important parts of data management and analysis, do not specifically pertain to the process of data cleaning. They involve different stages of data manipulation and analysis, rather than focusing solely on correcting existing data issues. Thus, the correct process involved in data cleaning is the detection and correction of inaccurate records.