What is meant by data quality 'completeness'?

Completeness in the context of data quality refers to the degree to which all required data is present to fulfill a particular business objective. This means that the data set contains all necessary information to adequately address the needs for analysis or decision-making. Having complete data ensures that analyses and reports are based on a comprehensive view and that key elements relevant to business processes, such as customer information, transaction details, or operational metrics, are not missing. Incomplete data can lead to erroneous conclusions, missed opportunities, or flawed strategies, hence emphasizing the importance of completeness in data quality.

The other choices focus on different aspects of data quality or data management. The proportion of data that meets expected standards revolves around data accuracy and validity rather than completeness. The total amount of data processed pertains to data volume, which does not directly address whether all necessary data elements are included. The overall reliability of data across systems emphasizes data consistency and trustworthiness rather than completeness by itself.