What can be considered a purpose of performing data reconciliation?

Performing data reconciliation primarily aims to ensure consistency and accuracy between datasets. This process involves comparing data from different sources to identify discrepancies and correcting them. In an environment where data comes from various systems or departments, inconsistencies can arise due to multiple factors such as human error, differing entry methods, or system updates. By reconciling data, an organization can maintain the integrity of its information, which is crucial for making informed decisions based on accurate analytics.

While the other options touch on various aspects of data management, they do not directly relate to the core purpose of reconciliation. Automating data entry processes focuses more on efficiency and productivity rather than verification. Comparing data from multiple time periods is a relevant analytical function but is not a specific aim of reconciliation. Visualizing complex datasets is essential for understanding data but does not address the accuracy and consistency that reconciliation seeks to achieve. Thus, ensuring consistency and accuracy is the most pertinent purpose of data reconciliation.