What is meant by 'Dataset Health Monitoring'?

Dataset Health Monitoring refers to the process of checking the integrity and performance of datasets. This involves assessing various aspects such as data accuracy, completeness, consistency, and any potential errors that could affect the usability of the data. Monitoring the health of datasets is critical in ensuring that the data remains reliable and useful for analysis and decision-making.

Maintaining the integrity of a dataset includes verifying that the data is stored correctly and that there are no corruptions. Performance evaluation may involve looking at factors such as how quickly data can be accessed, how well it supports user queries, and identifying any bottlenecks in data processing. By focusing on these areas, organizations can ensure that their datasets remain robust and effective for their intended purposes.

The other options relate to different aspects of data management. For instance, tracking the number of datasets created mainly provides insight into data growth but does not address the quality of the datasets. Evaluating user interactions offers valuable feedback on how datasets are used but does not specifically monitor their health. Recording the history of modifications is important for audits and understanding data evolution but does not directly correlate with the assessment of dataset integrity and performance.