What process involves standardizing data formats and removing errors?

The process of standardizing data formats and removing errors is referred to as data cleaning. This critical step involves identifying and correcting inaccuracies or inconsistencies within a dataset. Data cleaning aims to improve the quality of the data, ensuring that it is accurate, reliable, and usable for analysis.

Standardization may include unifying naming conventions, formatting dates, or ensuring consistency in measurement units. Removing errors can involve fixing typos, addressing missing values, and eliminating duplicates. The outcome of data cleaning is a more robust dataset that supports better decision-making and analysis.

In contrast to the other processes mentioned, data maintenance focuses on the ongoing process of updating and managing data over time; data optimization pertains to enhancing performance and efficiency, often in the context of spatial queries or algorithms; and data validation involves verifying that the data meets specific criteria or constraints but does not inherently focus on standardizing formats or correcting errors.