Which scenario would likely raise concerns about data completeness?

The scenario that likely raises concerns about data completeness is one where a database of employee salaries is excluding certain groups, such as senior executives. Data completeness is fundamentally about having a full representation of the subject matter at hand. In this case, if senior executives' salaries are omitted, the database does not provide a complete picture of the salary structure within the organization. This can lead to misleading conclusions about overall employee compensation and may affect decision-making based on that data.

In contrast, while the other scenarios may have issues related to data integrity and accuracy, they do not directly reflect the completeness concern as defined by missing entire segments of data. For example, hourly temperature readings or a sales database missing transaction times may show issues with specific data points or records, but as long as those records exist for the intended scope, they may still represent a complete dataset for their purpose. An inventory system with typing errors suggests inaccuracies rather than completeness, as the records exist but are flawed. Thus, the correct scenario highlights a significant gap in necessary information, illustrating a profound issue with data completeness.