Using Variables While Creating Custom Columns in Power Query
Power Query is one of the most powerful tools in Power BI, but writing repetitive or complex M code often makes it difficult to manage. This is where variables come in. By using the let and in keywords, you can simplify your logic, avoid duplication, and build custom columns that are easier to read, maintain, and optimize.
Whether you’re calculating sales values, applying conditional logic, or handling large datasets, variables help BI developers and business analysts alike streamline transformations and accelerate report performance. More importantly, structured M code allows organizations to scale their Power BI consulting, Microsoft Fabric implementations, and enterprise reporting with confidence.
In this guide, we’ll explore how variables in Power Query can be applied in real-world business scenarios, backed by examples, M code, and industry use cases that showcase the real impact on reporting efficiency and decision-making.
Problem Statement: How to create or use variables while creating custom columns in Power Query
Many analysts and BI developers working with Power BI often face a simple but common challenge:
How can I declare and use variables when building custom columns in Power Query, especially for calculations that require multiple steps?
Unlike Excel, where variables aren’t directly declared, Power Query’s M language uses the let … in construct to define and use variables. This makes the query process more powerful and reduces repeated calculations.
Solution
To declare variables in Power Query, let and in keywords are used.
This allows developers to:
- Assign intermediate calculations to variables.
- Improve query readability.
- Avoid repeating the same logic multiple times.
- Create structured, reusable code that scales across large datasets.
Scenario 1
In the example below, in a calculated column, a simple calculation is performed where the addition of three numbers is shown using variables.
M code is as follows:
Here, the variables x, y, and z are declared, and then combined in a single expression result.
Scenario 2
In the example below, in the Sales table, there are two columns — Unit Price and Quantity. In a single calculated column, the sales value is calculated, and whether the sale is above 2000 or below is mentioned using variables.
M code is as follows:
This scenario demonstrates how variables can reduce redundancy, improve business logic readability, and simplify conditional statements in Power Query.
Why Use Variables in Power Query?
- Performance Optimization:
- Avoid repeating calculations.
- Reduce query execution time for large datasets.
- Maintainability:
- Business analysts and consultants can easily interpret transformations.
- Future updates are easier to apply with a well-structured M code.
- Scalability:
- Works seamlessly across millions of rows.
- Supports advanced transformations (custom KPIs, calculated classifications).
- Integration with ERP & CRM:
- When Power Query pulls data from ERP systems like D365 Business Central, NAV, or GP, variables can streamline reporting logic.
- Example: Calculating OEE in manufacturing or gross margin in retail using pre-defined variables.
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Real-World Business Example
Imagine a manufacturing company tracking production efficiency. Their ERP (Business Central) stores machine hours and production units. Using Power Query variables, they can:
- Define intermediate variables for machine hours, ideal run time, and actual run time.
- Calculate Overall Equipment Effectiveness (OEE) in a clean, structured way.
- Feed results directly into custom Power BI dashboards that executives use to monitor bottlenecks.
This turns raw ERP data into actionable insights = a major driver of ROI.
Addend Analytics’ Consulting Perspective
At Addend Analytics, we’ve helped over 100+ clients in manufacturing, retail, and finance optimize their reporting workflows using Power Query, DAX, and Power BI best practices.
Our Power BI consulting services go beyond writing M code:
- Implementation Partner: Fast-track BI adoption in 6 weeks.
- Custom Dashboards: From OEE dashboards in manufacturing to real-time P&L in finance.
- Integration: Combine ERP (Business Central, NAV, GP), CRM (Dynamics, Salesforce), and IoT data.
- AI + Microsoft Fabric: Embed predictive models directly into Power BI.
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| Feature | Power Query Variables | DAX Measures |
| Execution Time | During data load | On-the-fly |
| Use Case | ETL / Pre-processing | Visualization / Aggregation |
| Best For | Cleaning & reshaping | Business metrics & KPIs |
| Reusability | Query-dependent | Report-wide |
Both are critical. Businesses that combine Power Query (M) and DAX achieve the best performance and flexibility.
FAQs
Q1. Can Power Query variables improve report performance in Power BI?
Yes. By avoiding repeated calculations in transformations, Power Query variables can make query refreshes faster, especially for large datasets.
Q2. Do Power Query variables replace DAX measures?
No. Variables in Power Query are for data preparation, while DAX measures are for data modeling and reporting. Both complement each other.
Q3. How do consulting partners like Addend Analytics use Power Query in real projects?
We embed Power Query logic into enterprise workflows, integrating with ERP, CRM, and IoT data. This ensures KPIs are consistent, reliable, and actionable.
Q4. Is there a risk in using too many variables in Power Query?
Not necessarily. Best practice is to keep code modular and readable, but excessive, unnecessary variables can reduce clarity.
From Variables to Value
Using variables in Power Query custom columns isn’t just a technical trick – it’s a gateway to scalable, maintainable, and business-focused analytics. Whether you’re calculating simple sales values or building complex manufacturing KPIs, variables make your Power Query workflows cleaner and more efficient.
For organizations investing in Power BI consulting services, this small but powerful technique becomes part of a larger strategy: moving from manual spreadsheets to automated, AI-driven dashboards with measurable ROI.
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