Tableau is a powerful data visualization tool that allows users to create interactive and engaging visualizations from a wide range of data sources. One of the key features of Tableau is its ability to perform normalization, which is a method of scaling data to make it more comparable across different dimensions. However, sometimes it’s necessary to do the opposite, and reverse the normalization to bring the data back to its original scale. In this article, we will explore how to do reverse normalization in Tableau.

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1. Understanding Normalization – Normalization in Tableau is a process of scaling data to make it more comparable across different dimensions. This can be done by dividing the data by a specific value, such as the sum of all data points or the maximum value. Normalization is often used to make data more interpretable, especially when working with large data sets.
2. Reversing Normalization – To reverse normalization in Tableau, you will need to multiply the normalized data by the value that was used to divide it. For example, if you normalized data by dividing it by the maximum value, you will need to multiply it by the same value to reverse the normalization.
3. Create a Calculated Field – The easiest way to reverse normalization in Tableau is to create a calculated field. To do this, go to the “Analysis” menu and select “Create Calculated Field.” In the formula editor, enter a calculation that will reverse the normalization, such as “SUM([Sales])*[Normalized Sales].”
4. Use the Reversed Normalized Data – Once you have created the calculated field, you can use the reversed normalized data in your visualizations. You can drag the calculated field onto the appropriate shelf, such as Columns or Rows, and use it to create new visualizations or update existing ones.
5. Use the Original Scale – If you want to use the original scale instead of the normalized one, you can simply drag the original field to the visualization instead of the calculated field that you created.

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In conclusion, normalization is a useful method of scaling data to make it more comparable across different dimensions, but sometimes it’s necessary to reverse the normalization to bring the data back to its original scale.

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