Are you new to Tableau and struggling to understand why your visualization isn’t turning out how you want it to? Or are you a seasoned Tableau user who is looking to optimize your performance? In either case, understanding the Tableau order of operations is critical to building effective visualizations.

Tableau’s order of operations determines the sequence in which you can apply calculations, filters, and other functions to your data. Without a solid grasp of this concept, it’s easy to make mistakes that can impact the accuracy and speed of your analysis.

In this tutorial, we’ll dive into the details of Tableau’s order of operations and explain each step. We’ll cover the topics such as aggregation, filters, table calculations, and more. By the end of this tutorial, you’ll clearly understand how to structure your Tableau workbook to achieve the results that you’re after.

What Is the Tableau Order of Operations?

Tableau’s order of operations is a set of rules that determines how you apply the calculations and other operations to your data. Understanding these rules is essential to ensure that your visualizations are accurate and are optimized for performance. The order of operations in Tableau consists of ten steps which are applied in a specific sequence:

Data source filters: These are filters that are applied to the entire data source and reduce the amount of data that Tableau has to process.

Context filters: Context filters allow you to define a specific subset of data that should be used in subsequent calculations and analysis. Context filters differ from other filters where they create a temporary table that contains only the selected data which is then used for further calculations.  This can improve the performance for complex analyses with large datasets. Context filters should be used sparingly and strategically as they can affect the results of other calculations and filters in the visualization.

Dimension filters: These filters limit the data that are displayed based on specific dimensions such as time or location. You can use the dimension filters to include or exclude the particular dimension members or to filter based on a range of values.  Dimension filters are applicable to individual worksheets, dashboards, or the entire workbook. Besides, they can help you focus on the most relevant data which allows you to analyze the trends and patterns quickly. However, using the dimension filters carefully ensures that you’re not removing an important data or biasing your analysis.

Measure filters: These filters limit the data that’s displayed based on specific measures such as sales or profit. You can use the measure filters to show the data that meets certain conditions, such as only displaying the sales data for a specific product category or only showing the customers who purchased more than a certain amount. Measure filters can help you focus on the most critical data and facilitate more in-depth analysis.

Table calculations: These are calculations that are performed on the data in a visualization such as running the totals or moving the averages.

Aggregate measures: These are calculations that are performed on aggregated data such as the sum or average of sales by region.

Calculated fields: They are user-defined calculations that can be used to create new fields in a visualization.

Reference lines, bands, distributions, and box plots: These are visual aids that are added to a visualization to help analyze the data.

Totals, subtotals, and grand totals: These are calculations that summarize the data at different levels of granularity such as by month or year. Subtotals are the sum, average, or other aggregate calculations that are applied to a subset of the data, typically within a group of related dimension members. For example, the subtotals might show the sum of sales by region within each sales quarter.

Grand totals, on the other hand, are the sum, average, or other aggregate calculations that are applied to all the data in the visualization. For example, the grand totals might show the total sales for all regions and all quarters.

Tableau also provides options to customize the level of detail for totals and subtotals. For instance, you can choose to display the grand total or subtotal for all rows or columns or only for selected rows or columns. You can also customize the calculation that is used for totals and subtotals such as changing the aggregation function or adding additional calculations.

Level of Detail (LOD) expressions: These are expressions that allow you to define a specific level of detail for a calculation such as computing the average profit by product category.

Chart Illustration of the Order of Operations

The following chart illustrates Tableau’s order of operations:

Understanding the order of operations is critical to build compelling visualizations in Tableau. Here’s an example of how the order of operations can impact a visualization:

Let’s say you want to create a chart that shows the average sales per day by region. If you apply a measure filter to only show the sales that are greater than $100, the order of operations applies the filter before the aggregation step. As a result, the chart only shows the average sales per day for those days where the sales are greater than $100 which may not accurately reflect the overall average sales per day by region. To address this, you could move the measure filter to the dimension filters step to ensure that the aggregation is performed before the filter is applied.

Conclusion

Understanding the Tableau order of operations is crucial for building accurate and optimized visualizations. By following these rules and understanding how they impact your data, you can create visualizations that provide valuable insights into your data.