Aggregation is a process in database management systems that involves combining multiple rows of data into a single summary record. This can be done by applying a mathematical or statistical function to a set of values, such as the sum or average of a group of values. Aggregation is often used in data analysis to create summary statistics that provide insights into the underlying data.

The main problem associated with aggregation is that it can result in a loss of information. When data is aggregated, some of the details and nuances of the original data set are lost, as the data is simplified into summary statistics. This can make it difficult to analyze and interpret the data, especially if the underlying data set is complex or contains a large number of variables.

To mitigate this problem, there are several remedies that can be employed:

Use grouping: By grouping the data into smaller subsets, the loss of information can be minimized. For example, instead of aggregating sales data for an entire year, the data can be grouped by quarter, month, or even day, to provide more detailed insights into the underlying trends.

Use sampling: Rather than aggregating all the data, a sample can be taken and aggregated, which can help to minimize the loss of information. However, care must be taken to ensure that the sample is representative of the underlying data set.

Use more sophisticated statistical methods: There are more sophisticated statistical methods that can be used to analyze aggregated data, such as regression analysis or time series analysis. These methods can help to uncover patterns and trends in the data that might not be immediately apparent from the summary statistics.

Store raw data: One way to ensure that data is not lost during aggregation is to store the raw data in a database. This allows for more detailed analysis of the data, and provides a reference point for the summary statistics.

In summary, aggregation is a powerful tool for data analysis, but it can result in a loss of information. By using grouping, sampling, more sophisticated statistical methods, or storing raw data, this problem can be mitigated, and the insights gained from the summary statistics can be more informative and accurate.