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In today’s data-driven environment, effective data analysis is crucial for deriving actionable insights and making informed decisions. As organizations gather increasingly complex datasets, the ability to explore and interpret data at varying levels of granularity becomes essential. Tableau, a leading tool for data visualization and analysis, offers advanced features to manipulate and refine data. Among these, Level of Detail (LOD) expressions stand out for their unparalleled control over data aggregation and analysis.
What Are LOD Expressions?
In Tableau, LOD expressions enable analysts to override the default level of detail in a visualization, allowing for calculations at a more granular or aggregated level than what is currently displayed. This flexibility is vital when dealing with complex datasets where default aggregations may fall short.
There are three primary types of LOD expressions:
FIXED LOD: Computes a value at a specific level of detail, ignoring other dimensions or filters in the view.
INCLUDE LOD: Aggregates data by including additional dimensions, even if they are not part of the current visualization.
EXCLUDE LOD: Performs calculations by excluding certain dimensions, regardless of their presence in the view.
In this guide, we will focus on INCLUDE LOD, exploring its purpose, use cases, and how it can enhance your analytical precision.
Exploring INCLUDE LOD: Dynamic Aggregation with Additional Dimensions
While FIXED and EXCLUDE LODs control granularity by locking or removing certain details, INCLUDE LOD offers flexibility by incorporating dimensions into an aggregation without fixing the level of detail. However, INCLUDE LOD is not primarily intended for simply adding dimensions to the current visualization. For example, if you want to visually break down your data by a dimension such as "Category," you can achieve this by dragging the dimension to the Marks section, like adding it to Color. This approach visually distinguishes the sales figures by category within each region with minimal effort.
However, INCLUDE LOD excels when more granular or precise aggregations are required. It allows for calculations that consider additional dimensions that may not be visible in the view but are essential for analytical accuracy.
When to Use INCLUDE LOD: Precision in Aggregation
INCLUDE LOD is valuable when you need to calculate metrics that include dimensions not visible in the current view. This approach is particularly useful for computing metrics such as averages, sums, or counts that factor in additional dimensions, providing deeper insights.
Example Scenario: Average of Total Sales by Sub-Category within Regions
Suppose you have sales data (rows) segmented by Region and Sub-Category (Columns) , and you want to calculate the average of total sales per sub-category within each region. While dragging Sub-Category to the Color shelf will visually break down the data by sub-category, it will only display total sales per sub-category without providing the average of these totals.
To compute the average of total sales per sub-category, a specific calculation using INCLUDE LOD is required. This allows you to first calculate the total sales for each sub-category and then find the average of these totals within each region.
Steps to Implement INCLUDE LOD:
Create a Calculated Field:
Open Tableau and connect to your data source.
Go to the Data pane on the left.
Right-click and select Create Calculated Field from the dropdown menu.
Input the LOD Expression:
Name your calculated field (e.g., "Average Sales by Sub-Category").
Enter the INCLUDE LOD expression: { INCLUDE [Sub-Category]:SUM([Sales])}
Click OK to save the calculated field.
Incorporate the INCLUDE LOD Field into Your Visualization:
Drag the newly created calculated field to the Rows or Columns shelf.
Ensure this measure is set to display the average value.
Understanding the Results
By using INCLUDE LOD, Sub-Category is factored into the aggregation, even if it’s not directly visible in the visualization. This will allow the chart to reflect the average sales per sub-category within each region, providing a more detailed analysis.
To display this metric alongside total sales without cluttering your dashboard, you can combine both measures in a single view:
Dual Axis Chart: Right-click on the second Y-axis (created by your calculated field) and select Dual Axis.
Synchronize Axes: Right-click one of the axes and choose Synchronize Axis to align the scales, creating a cleaner, more readable chart. Adjust the chart type as needed.
Conclusion: Unlocking Analytical Depth with INCLUDE LOD
While it’s straightforward to break down data by adding dimensions directly to the Marks shelf, INCLUDE LOD offers more granular control for aggregations that include dimensions not visible in the current view. This makes INCLUDE LOD ideal for calculating metrics like averages or sums requiring multiple levels of detail.
Mastering INCLUDE LOD equips data analysts with the precision needed for insightful analyses, enabling the discovery of deeper patterns and trends within complex datasets. As data complexity grows, the ability to perform sophisticated calculations using LOD expressions will become increasingly valuable for deriving actionable insights.