The process of tackling errors and locating anomalies in databases is known as what?

Data cleansing is the process focused on identifying and correcting errors or inconsistencies in data within databases. This procedure is crucial for maintaining data quality and ensuring that the information is accurate, reliable, and usable for analysis and decision-making. During data cleansing, activities such as removing duplicate entries, correcting typographical errors, standardizing formats, and validating data against defined rules are conducted.

This practice is important because high-quality data is essential for effective decision-making and operational efficiency. By addressing anomalies and errors, organizations can enhance their data integrity, which in turn supports better business outcomes.

The other options present different aspects of data management. Data maintenance involves ongoing updates and management of database systems but does not specifically focus on error correction. Master data management refers to the processes that ensure the consistency and accuracy of key data entities across an organization, which is broader than just dealing with errors. Data analytics pertains to the techniques and tools used to analyze data for insights but does not inherently involve the process of identifying and correcting data errors or anomalies.