How to Calculate Moving Average?

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Moving Average in Tableau is one of the most useful metrics used to analyze various metrics of the business data. A Moving Average provides at a glimpse of how the specific metric is performing with time. Based on our need, we can customize how many specific months or weeks we can consider to calculate the Moving Average.

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Tableau Calculate Moving Average
Tableau Calculate Moving Average

Steps to Calculate Tableau Moving Average

Let us assume a scenario where we have to calculate the Moving Average of Sales of the prior 5 months and including the current month. Hence at any point of time, our chart will show a figure which will represent the Average value of the previous 5 months, and that specific month inclusive. This can be achieved as shown below.

Tableau Calculate Moving Average
Tableau Calculate Moving Average Complete Guide

We choose a metric that we want to show, in this case Sales. We also add Month and Year for a range of our choosing. Then on further customizing and including the Moving Average logic, we have created a chart that is showing us the Moving Average of each point of time in the Chart based on our defined criterion.

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Why Create Moving Average

It is important to note that Moving Average is a very useful metric that can be used in a variety of ways. Businesses can use it to perform cohort analysis on a variety of dimensions. Different dimensions can be combined with different sets of metrics to get further insights from the data. Businesses can thus utilize the Tableau Moving Average values to make key decisions based on the insights gained. We can also use Tableau Labels in our charts in different ways so that even if the chart if designed differently, we can still show the Moving Average as an annotation or a metadata when the user interacts with the Tableau dashboard. This will allow us to create enriching Tableau dashboards with enhanced insight capabilities.

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Published by Rahul Bhattacharya

Rahul is a data analyst and expert in visualizing business scenarios using data science. He has performed extensive research across varied business scenarios and datasets to come up with insightful results. Rahul is skilled in a number of programming languages and data analysis tools. When he is not busy refining business data, Rahul can be found somewhere in the Appalachian trails or in an ethnic restaurant in Chicago.

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