Which feature allows users to assess the quality of data before it is used in reports and dashboards?

Data validation is a key feature that helps to ensure the integrity and accuracy of data before it comes into play for reporting and analysis. It involves setting criteria for data entry, which helps to prevent errors and inconsistencies from being introduced into the database. For instance, data validation can enforce rules regarding what constitutes acceptable values for certain fields, whether through specific formats, required fields, or ranges of acceptable values. This proactive measure ensures that only accurate and reliable data is used in subsequent reporting and dashboard creation.

While data monitoring, data analysis, and the data quality dashboard are indeed useful in their own ways, they do not directly focus on assessing data quality prior to its usage. Monitoring typically involves tracking data trends and performance over time rather than validating the data at the entry point. Data analysis refers to the examination of data sets to draw conclusions, which comes after the data has already been collected and entered. The data quality dashboard provides visual representations and insights about the quality of data but does not assess data as it is being inputted. Therefore, data validation stands out as the feature specifically designed to assess data quality before it is utilized in reports and dashboards.