What does the process of Data Cleanup entail?

Data Cleanup involves detecting and correcting corrupt or inaccurate records within a dataset. This process is crucial because it ensures the integrity and reliability of the data being used for analysis or decision-making. Clean data is essential for accurate reporting, effective customer relationship management, and maintaining the overall quality of information systems.

By identifying errors, such as duplicates, inconsistencies, or incomplete records, and rectifying them, organizations can achieve more trustworthy outcomes. This enhances data quality, which ultimately supports better business decisions and strategies. The process also includes standardizing formats, validating data against rules, and ensuring that the data aligns with actual real-world conditions.

The other options do not accurately define Data Cleanup. Creating new records does not address existing inaccuracies, transferring data to a new server does not ensure data quality, and removing all records would eliminate useful information rather than enhance it.