Measures and calculated columns are important components that allow you to create custom calculations in Power BI. While these two features seem similar at first glance, there is actually a great deal of difference between them in terms of the types of calculations they are used for, when to use them, and the advantages and disadvantages of each.
In this article, we will explore the differences between Power BI Measures and Calculated Columns, when to use each, and the best practices for making use of both.
Table of contents
- What Sets Measures and Calculated Columns Apart?
- Calculated Columns
- Best Practices: Measures vs Calculated Columns
What Sets Measures and Calculated Columns Apart?
A calculated column is a static value added to a table, calculated row by row when you load the data. In contrast, a measure is a dynamic value that changes based on what you’re looking at in a report, calculated on the fly within the filter context of a visual.
Now to really understand this difference, let’s delve a bit more into both measures and calculated columns.
What is a Power BI Measure?
A Power BI measure is a custom calculation you create using DAX. They are used for analyzing or visualizing data. These measures are dynamic, adjusting instantly to user actions like filtering selection specific data points.
To create one, click “New Measure” at the top of the screen, enter your formula in the new window, and press enter. Measures update automatically with data changes and can handle a variety of calculations, from simple sums to complex ratios and forecasts.
Advantages of Using Power BI Measures
Some advantages of using Power BI measures are:
Dynamic Calculations: Measures are made for on-the-fly calculations that change based on user actions, like filtering or slicing. This lets you use the same measures for different visuals and data sections.
Reusability: Once you make a measure, you can use it in many visualizations or reports, keeping your analysis consistent and efficient. You can also break down your logic into multiple measures for easier reuse.
Computation: Measures only calculate values when added to a visual in a report. You can have many measures, but if you don’t use them, they won’t impact the report. Calculated columns, however, use RAM memory even if not used in your report.
Disadvantages of Using Power BI Measures
Next to advantages, measures also have disadvantages:
Context-dependent: the result of a measure heavily depends on the filter context. This can be desired, but can also lead to unexpected or confusing results. For example, totals may not add up correctly when the definition of a measure requires a calculation to be performed on a different level and aggregate the underlying results.
Performance: Measures can sometimes cause performance issues with large data sets or complex calculations. For instance, calculating a cumulative sum per product group per day for a big dataset can result in slow calculations and affect report performance.
What is a Calculated Column?
A Calculated Column is a custom column in your table containing the output of a DAX formula. They are designed to extend your dataset by adding new information or transforming existing data to make it more useful for analysis.
For example, you can make a new column that shows the difference between two other columns or a column that calculates a percentage. Calculated Columns can also make custom data columns that the original data set doesn’t have.
To create a Calculated Column in Power BI, click the “Calculated Column” button at the top of the screen. A new window will open for you to type in your formula. After entering your formula, press enter, and your Calculated Column will be made.
Advantages of Using Calculated Columns
Some advantages of using Power BI Calculated Columns include:
Static Values: Unlike Measures, Calculated Columns are not dynamic – they are only calculated once when the table is first loaded or refreshed; this happens during query time. As a result, calculated columns provide static values that do not change based on filter context, making them more predictable and easier to understand in certain scenarios. That makes them ideal for more complex calculations, such as determining the running total of a series of values. You pre-compute the values on dataset refresh so that measures can retrieve them relatively quickly, instead of calculating these values on the fly.
Row-level calculations: Because Calculated columns perform calculations at the row level, they let you create values on individual records in your dataset. This is useful when you need to calculate values for each row in your table, like applying a discount to individual product prices.
Data Model: Unlike measures, calculated columns become part of your data model, meaning they can be used in relationships between tables. This can help you create more complex data models and establish connections between related data points. This also allows you to drag a calculated column into the rows or columns section of a visual, which is not possible with measures.
Disadvantages of Using Calculated Columns
Calculated Columns also have disadvantages:
Increased Storage: Calculated columns create additional data in your data model because their values are stored for every row in the table. This can lead to increased storage requirements and potentially impact the performance of your Power BI dataset refresh, especially when working with large datasets.
Less Dynamic: Because calculated columns perform row-level calculations, they’re not ideal for aggregating data or performing complex calculations that involve multiple rows or tables. Measures are more suitable for these types of calculations since they can aggregate data on the fly based on the current context.
DAX Expressions: When using calculated columns in DAX expressions, you may need to write more complex formulas to achieve the desired result, as they don’t automatically adjust to changes in the filter context like measures do. You can use CALCULATE to perform context transitions in a calculated column.
Best Practices: Measures vs Calculated Columns
So when should you use Measures, and when do Calculated Columns come to the rescue?
As a rule of thumb, use a measure when you need flexible calculations that change with user actions or when you need to add up data. For example, if you want to see gross profit percentage over time or by location or employee, measures are a better choice since static values won’t be as helpful.
If your data model is slow, using calculated columns can help improve performance. While your data model refresh might take longer compared to using only measures, it can greatly speed up your DAX calculations in your reports. If possible, do these calculations even further upstream (in Power Query or SQL) to get better data model compression and use less memory.
If you need the result of a calculation in the rows or columns of your visual, choose a calculated column. You can easily drag the column into the right fields, but you can’t do this with a measure.
In conclusion, both Measures and Calculated Columns have their own advantages and disadvantages in Power BI. The key is understanding when to use each one for the best results in your data analysis and visualization tasks.
Use Measures when you require dynamic, context-dependent calculations that can be applied across various visuals and data intersections. On the other hand, use Calculated Columns for static values, row-level calculations, as an optimization technique or when the results need to be displayed in the rows or columns of a visual.