How to Use Level of Detail (LOD) Expressions in Tableau

What is LOD: LOD (Level of Detail) Expressions in Tableau are a powerful feature that allow you to compute aggregations at different levels of granularity than what is displayed in your visualization, providing greater flexibility and control over data analysis. They are particularly useful for answering questions that involve multiple levels of detail within a single view

Why use LOD expressions?

• Granular control over data: LODs allow you to calculate metrics at a more detailed or broader level than what your chart currently displays, according to The Data School.

• Handle multi-level data aggregations: Useful when comparing metrics across different levels of detail within the same view, like comparing city-level sales to country-level sales.

• Consistency: Ensure complex calculations remain consistent across different visualizations, maintaining accuracy in analysis and reporting.

• Complex calculations: Enable advanced analytics like calculating rankings, percentages, and custom aggregations that might be difficult to achieve otherwise.

Types of LOD expressions and Use Cases:

1. FIXED: These expressions calculate values based on specified dimensions, disregarding the dimensions present in the view.

   Syntax example: {FIXED [Region] : SUM([Sales])}

   Use case: Calculating total sales per customer regardless of the other dimensions in the visualization, says Tableau.

2. INCLUDE: These expressions incorporate the specified dimensions into the calculation in addition to the dimensions already present in the view.

   Syntax example: {INCLUDE [Product] : SUM([Sales])}

   Use case: Computing values at a finer level of detail than the current view or including a dimension not currently in the view, like including Order ID to calculate average sales per order.

3. EXCLUDE: These expressions remove specific dimensions from the calculation, resulting in a less granular aggregation.

   Syntax example: {EXCLUDE [Category] : SUM([Sales])}

   Use case: Ignoring a particular dimension to analyze other dimensions or for calculating values without the influence of certain dimensions, such as calculating total sales while ignoring the Category field.

   
   

Table Calculations vs. LOD Expressions in Tableau

Understanding the difference between Table Calculations and Level of Detail (LOD) Expressions is key to choosing the right tool for your analysis.

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Feature Table Calculation LOD Expression

When Calculated After the view is built (post-aggregation) Before the view is built (during data processing)

Scope Works on what's visible in the view Works on all data in the data source

Filters Affected by all filters (including quick filters) FIXED can ignore filters unless set as context filters

Use Cases Running totals, rankings, moving averages Customer lifetime value, fixed benchmarks

Flexibility Highly responsive to user interactions More stable and consistent

Performance Can be slower on large datasets Often more performant for fixed calculations

Syntax Example RUNNING_SUM(SUM([Sales])) { FIXED [Customer ID] : SUM([Sales]) }

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✅ When to Use Each

• Use Table Calcs when your calculation depends on the structure of the view (e.g., running total, rank).

• Use LODs when you need to control aggregation level, regardless of what's shown or filtered.

Common Mistakes When Using LOD Expressions:

While Level of Detail (LOD) expressions are incredibly powerful, misusing them can lead to incorrect analysis, inefficient dashboards, or unnecessary complexity. Here are some common mistakes to watch out for:

1. Confusing LODs with Table Calculations

Mistake:

Thinking that LOD expressions behave like table calculations, which operate based on the visible structure of your view.

Why It Happens:

Both can be used to calculate values at different levels of granularity, but they work differently. LOD expressions are calculated before the view is built, while table calculations happen after.

Tip:

Use table calcs for things like rankings, moving averages, and running totals.

Use LODs when you need to control the level of aggregation regardless of the view.

2. 🧩 Not Understanding How Filters Affect LODs

Mistake:

Applying filters and expecting LODs to react the same way, without considering their type (Context vs Regular).

Why It Happens:

LOD expressions behave differently depending on where your filters sit in the Tableau order of operations.

Tip:

Use context filters if you want FIXED expressions to respect certain filters. Always check how your filters interact with the LOD expression.

3. ⚠️ Using LODs When a Simple Calculation Will Do

Mistake:

Jumping straight into FIXED/INCLUDE/EXCLUDE when a basic aggregation like SUM([Sales]) or AVG([Profit]) would be sufficient.

Why It Happens:

LOD expressions seem powerful and flexible, so they’re tempting to use—especially for new users looking to "do it right."

Tip:

Start with basic aggregations and only use LODs when you need to change the granularity of your calculation independently from the view.

Best Practices for Using FIXED, INCLUDE, and EXCLUDE in Your Analysis:

Using LOD expressions the right way can bring clarity and flexibility to your dashboards. But with great power comes great responsibility! Follow these best practices to get accurate, performant, and user-friendly results.

1. Use FIXED for Consistent Groupings or Benchmarks

Use When: You need values that stay the same, even with filters.

Example: Customer lifetime value, category averages.

Best Tip: Use when consistency matters more than interactivity.

{ FIXED [Customer ID] : SUM([Sales]) }

2. Use INCLUDE for Deeper Analysis in the Current View

Use When: You want finer granularity without changing the visible dimensions.

Example: Avg sales per order in a region-level view.

Best Tip: Great for adding hidden depth to analysis.

{ INCLUDE [Order ID] : SUM([Sales]) }

3. Use EXCLUDE to Simplify Over-Detailed Views

Use When: You want to ignore specific fields for cleaner aggregation.

Example: Show regional totals while hiding sub-category detail.

Best Tip: Use to reduce clutter in over-detailed views.

{ EXCLUDE [Sub-Category] : SUM([Sales]) }

4. 🧪 Always Test and Validate Results

Why It Matters:

LOD expressions can override filters, create counterintuitive behavior, or give misleading results if not checked.

Best Practice:

• Use tooltips to check intermediate values

• Validate against raw data or simple calculations

• Add temporary tables or crosstabs to troubleshoot

• Use context filters carefully with FIXED

  
  

Limitations of LOD Expressions in Tableau

1. Cannot Use with Certain Filters Easily

• FIXED LOD ignores regular filters unless they are context filters, which can complicate filtering logic.

2. No Support for Table Calculation Functions

• LODs can’t perform running totals, rankings, or moving averages—these require table calculations.

3. Cannot Reference Table Calculation Results

• LOD expressions cannot use or be combined directly with table calculations.

4. Performance Impact on Large Datasets

• Complex LODs over huge data may slow down performance, especially FIXED LODs.

5. Limited Support with Data Blending

• LOD expressions don’t work well across blended data sources.

6. No Row-Level Access

• LODs aggregate data and can’t return individual row-level results.

Conclusion: Unlocking the Power of LOD Expressions

LOD expressions—FIXED, INCLUDE, EXCLUDE—give you precise control over data aggregation, no matter the filters or view.

By mastering them, you can:

• Build consistent KPIs

• Dive deeper into analysis

• Simplify complex views

Use LODs for smarter segmentation, performance tracking, and clean, reliable reporting—key to better, data-driven decisions.