5 Data Aggregations You Can’t Live Without 👊

Essential Data Aggregations for Powerful Insights

There are (at least) five aggregation types that you absolutely must understand as a data analyst. I’ve included concrete examples for each type to help you understand them better.

1. Sum

Sum adds together all the values in a dataset or a subset of a dataset. It’s often used when dealing with numerical data.

Example: If you have a dataset of all transactions made in a store, you could use a sum aggregation to calculate the store’s total sales for each product type.

2. Average

Average (or mean) calculation involves summing all the values in a dataset and then dividing by the count. This gives a good general indicator when analyzing numerical data.

Example: You could use an average aggregation to determine the average sales for each product type and market.

3. Count

The count is one of the most straightforward aggregation types. It measures the number of items in a dataset or a subset of a dataset.