Tableau, a powerful data visualization tool, empowers users to explore and analyse data in an intuitive and interactive manner. One of the key features that enhances this capability is Levels of Detail (LODs). LODs allow users to control the granularity of their analysis, providing a more refined understanding of data relationships. In this blog post, we will delve into the concept of LODs in Tableau, exploring how they work and how they can be effectively used to unlock deeper insights.


Levels of Detail Defined:

At its core, Levels of Detail refer to the different ways in which Tableau can aggregate and display data. There are three primary types of LOD expressions in Tableau:

1. Fixed LOD:

  • Fixed LOD expressions allow you to specify a particular level of granularity for the calculation, regardless of the visualization’s current level of detail.
  • For example, if you are visualizing monthly sales data but want to calculate the yearly average, a Fixed LOD expression can be employed to keep the calculation at the yearly level.
  • Example {FIXED [Year] : AVG([Sales])}

2. Include LOD:

  • Include LOD expressions enable you to fix the level of detail for certain dimensions while leaving others unaffected.
  • This can be beneficial when you want to analyse specific aspects of your data in isolation, maintaining a dynamic relationship with other dimensions.
  • Example  { INCLUDE [Customer Name] : SUM([Sales]) }

3. Exclude LOD:

  • Exclude LOD expressions work conversely to Include LODs, allowing you to exclude certain dimensions from the calculation while keeping others in play.
  • This is particularly useful when you want to analyse the impact of specific dimensions without their influence on the overall calculation.
  • Example  { EXCLUDE [City] : SUM(Profit)}


Practical Applications:

Understanding how LODs work is crucial for implementing them effectively in real-world scenarios. Consider the following practical applications:

1. Comparative Analysis:

  • Use Fixed LODs to compare aggregated values at different levels of granularity. For instance, compare regional sales performance while visualizing data at the country level.

2. Segmentation:

  • Leverage Include LODs to segment your data and focus on specific dimensions of interest, such as analysing the performance of a particular product category across different markets.

3. Anomaly Detection:

  • Utilize Exclude LODs to identify anomalies or outliers by excluding certain dimensions from the analysis, allowing you to pinpoint irregularities without compromising the overall data set.

4.Customized Calculations:

  • LODs allow users to create customized calculations based on specific levels of granularity. Whether calculating averages, percentages, or other metrics, LOD expressions offer flexibility in tailoring calculations to meet specific analytical needs.

5.Filtering and Contextual Analysis:

  • LODs can be used to create filters that operate independently of the visualization’s current level of detail. This is useful when users want to filter data based on specific dimensions without affecting the overall analysis.

6.Nested Aggregation:

  • Users can nest LOD expressions within each other to create more complex calculations. This allows for a high degree of customization in analysing data at multiple levels simultaneously.

LODs Order of Operation

In Tableau, Levels of Detail (LODs) are applied in a specific order known as the LOD order of operations. Understanding this order is crucial for creating accurate and meaningful visualizations. The LOD order of operations in Tableau is as follows:

Tableau's Order of Operations - Tableau



Levels of Detail in Tableau offer a flexible and powerful way to analyse data at varying granularities, providing users with the tools needed to extract valuable insights. Whether you’re conducting comparative analysis, segmentation, or anomaly detection, understanding and implementing LOD expressions can significantly enhance your data visualization capabilities in Tableau.

Felix Ralphs
Author: Felix Ralphs