Power BI is an essential tool for data analysis and reporting in the higher education sector, enabling universities and colleges to make data-driven decisions about student performance, enrolment trends, and funding allocation. However, users often find themselves asking: Should I use M Language or DAX? While both are powerful tools, they serve different purposes. In this blog post, we’ll explore the key differences between M Language and DAX and provide a real-world example from UK higher education to illustrate when to use each. To learn more, contact me about my Power BI training course.
Understanding M Language in Power BI
M Language is used in Power Query, the data transformation engine in Power BI. It is designed for Extract, Transform, Load (ETL) processes, allowing users to clean, reshape, and merge data before it is loaded into Power BI.
Key Features of M Language:
- Data Preparation: Used for cleaning and structuring raw data.
- Pre-Load Transformation: Works before data is loaded in, improving performance.
- Automated Query Generation: Power Query records your actions and converts them into M Language.
- Functional Programming: Uses step-based transformations similar to Excel formulas but more powerful.
Understanding M Language in Power BI
M Language is used in Power Query, the data transformation engine in Power BI. It is designed for Extract, Transform, Load (ETL) processes, allowing users to clean, reshape, and merge data before it is loaded into Power BI.
Key Features of M Language:
- Data Preparation: Used for cleaning and structuring raw data.
- Pre-Load Transformation: Works before data is loaded in, improving performance.
- Automated Query Generation: Power Query records your actions and converts them into M Language.
- Functional Programming: Uses step-based transformations similar to Excel formulas but more powerful.
Understanding DAX in Power BI
DAX (Data Analysis Expressions) is used after data is loaded into Power BI. It enables users to create calculated columns, measures, and aggregations to drive interactive reports and dashboards.
Key Features of DAX:
- Post-Load Calculations: Used for dynamic reporting and aggregations.
- Works on Data Models: Enables the creation of calculated fields and relationships.
- Optimised for Analysis: Supports functions for totals, averages, and time intelligence.
- Measures & Calculated Columns: Helps in creating new insights without modifying the original dataset.
Real-World Example: Student Enrolment Analysis in UK Higher Education
Let’s consider a UK university analysing student enrolment trends across multiple academic years. The university collects data from different sources, including:
- Internal databases (e.g., student records system).
- Excel reports from different faculties.
- HESA (Higher Education Statistics Agency) returns.
Step 1: Using M Language in Power Query
Before analysing enrolment trends, the university needs to clean and prepare the data. The enrolment data contains academic years in a six-digit format (e.g., 202021 instead of 2020/2021). Using M Language, they apply a transformation to reformat this data:
Text.Start([AcademicYear],4) & "/" & Text.End([AcademicYear],2)This step ensures that all academic year formats are standardised before loading into Power BI.
Step 2: Using DAX for Aggregation and Analysis
Once the cleaned data is loaded into Power BI, the university wants to analyse the total number of new enrolments per academic year and compare growth trends. Using DAX, they create a measure to calculate total enrolments:
Total Enrolments = SUM('Student Data'[EnrolmentCount])To compare enrolment trends year-over-year, they use a time intelligence function:
Yearly Growth =
VAR PreviousYear = CALCULATE([Total Enrolments], SAMEPERIODLASTYEAR('Student Data'[AcademicYear]))
RETURN
IF(NOT(ISBLANK(PreviousYear)), [Total Enrolments] - PreviousYear, BLANK())This allows decision-makers to quickly compare enrolment changes and make informed decisions about student recruitment strategies.
When to Use M vs. DAX in Higher Education Reports
| Scenario | Use M Language (Power Query) | Use DAX (Power BI Data Model) |
|---|---|---|
| Cleaning and restructuring raw data | ✅ Yes | ❌ No |
| Merging student records from multiple sources | ✅ Yes | ❌ No |
| Standardising academic year formats | ✅ Yes | ❌ No |
| Creating interactive dashboards | ❌ No | ✅ Yes |
| Calculating student retention rates | ❌ No | ✅ Yes |
| Performing time-based analysis (e.g., Year-over-Year trends) | ❌ No | ✅ Yes |
Final Thoughts
Both M Language and DAX are essential for working with data in Power BI, but knowing when to use each will improve efficiency and report performance.
- Use M Language in Power Query for data transformation and preparation before loading into Power BI.
- Use DAX for calculations, aggregations, and interactive analysis after the data is loaded.
By combining both effectively, universities and colleges can streamline data reporting, enhance decision-making, and improve student success tracking.