What is the consequence of failing to aggregate data when necessary?

Failing to aggregate data when necessary can lead to a situation where the query generates irrelevant results. Aggregation is important for processing large datasets into a more manageable form, allowing insights to be drawn from a summarized view rather than from raw and potentially overwhelming amounts of data.

When aggregation is overlooked, a query might pull in excessive detail without filtering out unrelated or unnecessary information. This can obscure key trends or insights that would otherwise emerge from a properly aggregated dataset. As a result, the output of the query could become too granular or scattered, making it difficult to derive meaningful conclusions.

The other options do not accurately reflect the impact of failing to aggregate data. Comprehensive data is not a benefit of poor aggregation; rather, it would lead to complexity. Performance might actually degrade due to increased load time and processing requirements of handling unaggregated data. Similarly, without aggregation, managing the data becomes more challenging, not easier. Hence, the focus on producing relevant results highlights why failing to aggregate has significant consequences.