Which process involves identifying and correcting errors in data prior to analysis?

The process of identifying and correcting errors in data prior to analysis is known as data cleaning. This step is crucial because raw data often contains inconsistencies, missing values, duplicates, or inaccuracies that can significantly impact the results of any analysis. Data cleaning involves various techniques such as removing or imputing missing values, correcting typos or formatting issues, and standardizing information. By ensuring the data is clean and reliable, analysts can perform more accurate and meaningful interpretations of the data during the analysis phase.

Understanding the significance of data cleaning highlights its role in the broader context of data preparation before any analytical tasks, such as data validation or data mining, take place. Data validation focuses more on checking data integrity rather than directly correcting it, while data integration involves combining different data sources. Data mining refers to the exploration of large datasets to discover patterns and insights, but that can only be done effectively once the data has been cleaned and deemed ready for analysis.