How to Use DAX to Split Values from Various Rows and Columns in Power BI

Mastering KPI Calculations in Power BI: A DAX Approach

When working with Power BI, handling Key Performance Indicators (KPIs) efficiently can be challenging. Often, we need to extract and manipulate values from different rows and columns, but the default aggregation methods don’t always suffice. 🚀

One such scenario occurs when trying to calculate GP% (Gross Profit Percentage) by dividing a specific KPI's GP value by the sum of two other KPIs. This requires using DAX expressions to filter and extract the right values dynamically.

Imagine you are analyzing financial reports, and you need to compute a percentage based on figures spread across different KPI rows. Simply summing or dividing within a single column won't work—you must reference multiple rows explicitly.

In this article, we'll explore how to solve this issue using DAX filtering techniques to ensure accurate KPI calculations. Whether you're new to Power BI or an experienced user struggling with row-based calculations, this guide will provide a structured approach to solving this problem. ✅

Optimizing DAX Calculations for KPI Analysis in Power BI

In Power BI, dealing with KPI calculations that require referencing multiple rows and columns can be tricky. To solve this, we used DAX functions such as CALCULATE, FILTER, and DIVIDE to extract the necessary values dynamically. The first script focuses on obtaining the GP value from KPI 7 and dividing it by the sum of Sales from KPI 3 and KPI 4. This method ensures that only the relevant rows are considered, rather than aggregating an entire column. 🚀

Another approach we used is SUMX, which iterates over filtered rows to compute Sales Sum before performing the division. Unlike standard SUM, this function provides better control over row-level calculations, especially when dealing with complex KPI structures. For example, if a dataset contains dynamically changing values, SUMX ensures that only the right rows contribute to the final computation. This is particularly useful in financial dashboards where KPI definitions may vary per report. 📊

To validate our calculations, we implemented SUMMARIZECOLUMNS, a command that groups and presents data based on conditions. This step is crucial when checking whether the DAX expressions work correctly before deploying them in a live Power BI report. Without proper testing, errors like dividing by zero or missing values could lead to misleading insights, which can impact business decisions.

Finally, for users preferring Power Query, we provided a script that precomputes the GP% column before importing data into Power BI. This approach is beneficial when working with large datasets, as pre-processing reduces real-time calculation load. By using Table.AddColumn and List.Sum, we can dynamically generate the correct GP% values at the data source level, ensuring a more optimized and responsive dashboard.

// DAX solution using CALCULATE and FILTER to divide values from different rows
GP_Percentage =
VAR GPValue = CALCULATE(SUM(KPI_Table[GP]), KPI_Table[KPIId] = 7)
VAR SalesSum = CALCULATE(SUM(KPI_Table[Sales]), KPI_Table[KPIId] IN {3, 4})
RETURN DIVIDE(GPValue, SalesSum, 0)

// Alternative method using SUMX for better row-wise calculations
GP_Percentage =
VAR GPValue = CALCULATE(SUM(KPI_Table[GP]), KPI_Table[KPIId] = 7)
VAR SalesSum = SUMX(FILTER(KPI_Table, KPI_Table[KPIId] IN {3, 4}), KPI_Table[Sales])
RETURN DIVIDE(GPValue, SalesSum, 0)

// Test the GP% calculation with a sample dataset
EVALUATE
SUMMARIZECOLUMNS(
  KPI_Table[KPIId],
  "GP_Percentage", [GP_Percentage]
)

// Power Query script to create a calculated column for GP%
let
    Source = Excel.CurrentWorkbook(){[Name="KPI_Data"]}[Content],
    AddedGPPercentage = Table.AddColumn(Source, "GP_Percentage", each
        if [KPIId] = 7 then [GP] / List.Sum(Source[Sales]) else null)
in
    AddedGPPercentage

Advanced DAX Techniques for KPI Comparisons in Power BI

Beyond basic calculations, DAX allows for dynamic row-based aggregations, which is essential when dealing with KPIs that rely on cross-row computations. One powerful method is using VAR (variables) in DAX to store intermediate values, reducing repetitive calculations and improving performance. When handling financial data like revenue and profit margins, storing values as variables before applying division ensures accuracy and efficiency.

Another key concept is context transition. In Power BI, row context and filter context play a crucial role in determining how calculations behave. Using CALCULATE with FILTER allows us to override the default row context and apply a specific filter dynamically. For example, if we want to compute profit margins based on specific KPI categories, we need to manipulate context effectively to ensure that only the correct data is considered.

Additionally, working with dynamic measures can enhance report interactivity. By leveraging USERELATIONSHIP in DAX, we can switch between different data relationships on demand. This is useful when comparing KPIs across multiple timeframes or business units. For instance, in a sales dashboard, allowing users to toggle between monthly and yearly profit calculations provides deeper insights into performance trends. 📊