What is one of the four checkpoints for assessing data quality during exploration?

Data integrity is a critical checkpoint for assessing data quality during exploration because it refers to the accuracy, consistency, and reliability of the data. Ensuring data integrity means verifying that the information contained within a dataset is accurate and trustworthy, which is essential for making informed decisions based on that data.

During the exploratory phase of data analysis, analysts must confirm that the data is not corrupted, contains no duplicates, and adheres to defined standards. Assessing data integrity involves checking for issues such as missing values, inconsistencies in data formats, and the validity of relationships between data points. By focusing on data integrity, analysts can ensure a solid foundation for subsequent analysis and reduce the risk of drawing incorrect conclusions based on faulty data.

Other elements like data storage stability, data retrieval speed, and data extraction methodology, while important in their respective contexts, do not directly reflect the accuracy and reliability of the data itself, which is the primary concern during the exploration phase.