Which option best describes how data is processed in aggregation?

Data processing in aggregation is fundamentally about collecting and compiling data from various sources into a single cohesive dataset. This approach allows for a comprehensive analysis by providing a broader view of the information collected across different points.

When multiple data streams are combined, it enables analysts to discern patterns, make comparisons, and derive insights that might not be apparent when examining individual datasets in isolation. This process is crucial in various applications, such as business intelligence, where the goal is to make informed decisions based on consolidated data insights.

While other options touch upon aspects of data processing, they don't accurately represent the core function of aggregation. Data remaining unchanged during processing does not align with the principles of aggregation, as the very act of aggregating typically involves altering the perspective on the data. Compression and encryption are important factors in managing and securing data but do not define the aggregation process itself. Therefore, the focus on gathering data from multiple sources for analysis accurately captures the essence of data aggregation.