What is Aggregated Measures in 'Analysis' in Tableau?

 In Tableau, aggregated measures refer to calculations or functions that summarize or aggregate data across multiple rows or records in a dataset. They allow you to perform calculations such as sum, average, minimum, maximum, count, and other aggregate operations on your data.

When working with measures in Tableau, you have two types of fields: aggregated measures and non-aggregated measures.

Aggregated measures:

• Aggregated measures perform calculations across multiple data points. For example, summing the sales of multiple products, finding the average temperature over a period, or counting the number of orders.
• Aggregated measures are represented with aggregation functions, such as SUM, AVG, MIN, MAX, COUNT, etc., applied to the underlying data.
• Aggregated measures appear in the Measures area of the Tableau interface, typically in the "Columns" or "Rows" shelf when building visualizations.

Non-aggregated measures:

• Non-aggregated measures, also known as raw or detail measures, are the original, individual data points in your dataset.
• Non-aggregated measures are often used for calculations that don't require summarizing the data, such as calculating the difference between two dates or finding the percentage of a single value.
• Non-aggregated measures appear in the Dimensions area of the Tableau interface, typically in the "Columns" or "Rows" shelf when building visualizations.

When you drag a measure field onto a visualization, Tableau automatically applies an aggregation function to it based on the visualization type and the default aggregation settings. You can customize the aggregation function for a measure by right-clicking on the measure in the view and selecting the desired aggregation option from the context menu.

Tableau provides a wide range of aggregation functions, including SUM, AVG, MIN, MAX, COUNT, COUNTD (count distinct), MEDIAN, STDEV (standard deviation), and many more. These functions allow you to summarize and analyze your data at different levels of granularity to derive insights and answer specific analytical questions.

Understanding the distinction between aggregated and non-aggregated measures is crucial for accurately representing and interpreting data in Tableau visualizations.