Data aggregation is collecting and presenting data in a summarized manner for statistical analysis and to effectively fulfill business objectives. Data aggregation is important to store data as it helps make decisions based on a huge amount of raw data. In addition, data aggregation enables the user to forecast trends and help with predictive modeling. Effective data aggregation also helps in minimizing performance issues.

The aggregation offers more information related to data clusters like an individual’s profession or income. For example, a store that wants to analyze the sales performance of different regions can easily aggregate the sales data based on specified regions.

Queries with aggregation give faster results. For instance, the query for the total product sales in a month brings faster results related to the query for sales of the product as a whole. This is due to the aggregation applied on the first query; only the total is displayed, while the other query brings the individual records. Therefore, faster queries result in better performance of the system.

What are the Steps in Data Aggregation?

Different data integration platforms gather data from numerous sources to achieve data aggregation. The second step is data processing, which helps improve the data, and the data aggregation stage comes. Here is when the information is statistically studied for the newest insights.

Here’s an explanation of the steps included in the data aggregation process below:

Data Collection

A data integration platform pulls data from numerous sources and stores it either in a data warehouse or on the cloud. It can be extracted from sources like social media platforms or data stored in files regarding sensor data. The benefit of a data integration platform is that it makes it seamless and easier to bring all the data from different data silos together.

Data Processing

In the data processing stage, the data is processed for interpretation. This processing is performed using a combination of machine learning algorithms. The processing method varies based on the source of data being processed. This data is processed in different methods; it could be in the form of an image, a vector file, audio, charts, a graph, a table, or any other format of choice.

Data Presentation

Developing and delivering a data-driven presentation is done by various data aggregation tools. First, collect and analyze the raw data, after which the aggregated data is displayed in a summarized form.  

Conclusion

Data aggregation tools take care of collecting, handling, and presenting the data. It’s an important part of data integration. The data aggregation summarizes data from various, disparate, and numerous sources. It raises the value of information.