# How to Extract Values from Columns to Use in Calculated Measure Formulas in Power BI
In the world of data visualization and business intelligence, Power BI stands out as a robust tool capable of transforming raw data into insightful and interactive dashboards and reports. One of Power BI's strengths is its DAX (Data Analysis Expressions) language, which enables users to create complex calculations and data models. A common requirement in Power BI is to extract values from columns and use them in a calculated measure formula. This could sound daunting, but by understanding and utilizing the right functions, particularly SUMX, you can dramatically enhance your data models' flexibility and power.
## Core Concept: Calculated Columns vs. Calculated Measures
Before diving into the specifics, it's pivotal to grasp the distinction between calculated columns and calculated measures in Power BI. A **calculated column** is a column that you add to an existing table in your model, where each row's value is computed based on other columns' values in that row. In contrast, a **calculated measure** is a calculation that is performed on the fly, aggregating data across multiple rows but only when the measure is called in your report.
### This Is Why You Need SUMX
In scenarios where you need a row-by-row calculation across a table that results in a single aggregated number, rather than creating a new column for each row's result, you turn to calculated measures. The `SUMX` function shines here by iterating over a table and evaluating an expression for each row, then summing those results. It's a sophisticated approach that maintains your model's performance and storage efficiency.
## The Formula: Leveraging SUMX
The usage of `SUMX` in creating an insightful calculated measure is remarkably straightforward yet powerful. Consider the formula:
New Measure = SUMX(TableA, [targets] – [totals])
This measure processes each row in `TableA`, subtracting the value in the `[totals]` column from the `[targets]` column, and then summing up all those differences. The beauty of `SUMX` is its ability to do this iteration internally, making your calculated measure dynamic and efficient.
### Real-life Example: Sales Target Analysis
Imagine you're analyzing sales data, and you have a dataset with actual totals and target sales figures. You need an overall measure of how much your sales figures diverged from targets across all entries. Instead of manually calculating this for each row or cluttering your model with additional columns, you can use the `SUMX` function as shown above.
## Here Are the Steps:
1. **Load your data** into Power BI and ensure your table (e.g., 'TableA') includes both the 'targets' and 'totals' columns.
2. **Create a new measure** by navigating to the modelling tab and selecting 'New measure'.
3. **Enter the SUMX formula**, adjusting the table and column names according to your dataset.
4. **Use this new measure in your reports**. For instance, you can create a card visual to display the total difference across all entries.
### These Are the Advantages
- **Performance**: Calculated measures are generally faster than calculated columns because they are computed at query time, making use of Power BI's in-memory technology.
- **Flexibility**: Adjusting your calculation or adding more complexity is straightforward as you're dealing with a single measure instead of multiple columns.
- **Insights**: This method provides clear, actionable insights into specific performance metrics, such as variances between targets and actuals.
## This Is How Flowpoint.ai Can Help
While mastering DAX and functions like `SUMX` can significantly enhance your Power BI reports, identifying the precise calculations needed to improve your conversion rates or other KPIs can be challenging. Here, [Flowpoint.ai](https://flowpoint.ai) offers a compelling solution. Flowpoint's AI-driven analysis can pinpoint technical errors and inefficiencies on your website, identifying exactly where your data model may need refinement. By integrating these insights into Power BI, you can directly translate this analysis into actionable, data-driven strategies to boost your conversion rates.
## Conclusion
In summary, while calculated columns have their place, the power and efficiency of calculated measures—especially when leveraging functions like `SUMX`—cannot be overstated. By extracting values from columns and using them in calculated measures, you'll unlock a deeper level of data analysis and reporting in Power BI.
Understanding and applying these techniques will enhance your capabilities as a data analyst or business intelligence professional. Remember, the journey to becoming proficient with Power BI and DAX is ongoing, filled with learning opportunities that, in turn, pave the way for more insightful, impactful data storytelling.