What are the seven dimensions of data quality?

The seven dimensions of data quality are accuracy, completeness, consistency, timeliness, uniqueness, validity, and relevancy. Each of these dimensions plays a critical role in ensuring that data is reliable and can be effectively utilized in decision-making processes.

Accuracy refers to the correctness of the data and whether it represents the real-world scenarios it is intended to describe. Completeness assesses whether all necessary data is present. Consistency involves ensuring that data across various sources does not conflict with one another. Timeliness highlights the importance of having data available when needed, which is essential for making informed decisions. Uniqueness emphasizes the need for data to be free from duplicates. Validity checks if the data meets the required constraints and formats expected for its use. Lastly, relevancy denotes whether the data is applicable and helpful for the intended purpose.

The other options do not accurately represent the established dimensions of data quality. For instance, while some aspects mentioned in those options touch on qualities related to data, they do not encompass the full spectrum that the correct choice does. This comprehensive understanding helps organizations maintain high data standards necessary for effective operations and decision-making.