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Data aggregation is the process of gathering large amounts of data from multiple sources with the intent of combining and then processing that data. While data aggregation itself just refers to just the method of gathering data, it is most often used in conjunction with data processing, as the data itself doesn’t do much if it is not analyzed and understood. For that reason, data aggregation usually deals with data gathered from multiple locations that is the outputted in a condensed or summarized format so that it can be processed and utilized in some manner.
Data aggregation tools allow for large amounts of data to be quickly collected. Attempting to gather from multiple locations can be time consuming when done manually, but automated tools can speed up the process substantially. These tools are also beneficial as they free up manpower to work on more complex tasks, rather than the tedious nature of data aggregation work.
After aggregation, the next step is generally to extract data that is deemed relevant and summarize it for analysis. This process can also be automated. Machine learning can be used to identify only the data that is relevant to the assigned task. This data can quickly be exported and used for data processing and analysis. Analyses such as predictive analytics and statistical analysis often rely on data aggregation, as both types need a large pool of data to return actionable results. Predictive analyses attempt to make predictions about future actions and results based on information from the past. The more information these analyses have, the more accurate their results are likely to be. The same holds true for statistical analyses, which uses large amounts of data to arrive at numerical facts. These types of analyses are often used by businesses and organizations in order to plan marketing strategies or for price monitoring, and can be run in real time as the data is aggregated.
Data aggregation tools can offer a wide range of business benefits. They: