Hi, I'm Tom Blessing. I'm an expert at helping small to mid-sized companies adopt Power BI. I'm also a recovering investment banker, with deep experience applying technology to otherwise manual data problems. I have dual degrees in Finance and Technology Management from Indiana University.

Resources to help build your fundamentals: Learning Power BI? Data fundamentals are critical for this process to be easier. I've assembled a few DataCamp courses to help. I earn a small commission if you click and purchase.

Introduction to Power BI course: Master Power BI basics and learn to build impactful reports.

Exploratory Data Analysis in Power BI course: Learn about Power BI's Exploratory Data Analysis (EDA) and enhance your reports.

Introduction to SQL course: Learn how to create and query relational databases using SQL in just two hours.

Introduction to DAX course: Learn the fundamentals of Data Analysis Expressions (DAX) such as calculated columns, tables, and measures.

Database Design course: Learn to design databases in SQL to process, store, and organize data in a more efficient way.

Like any tool, Power BI is not immune to the occasional misinterpretation or misuse of its functions.

In this blog post, I'll explore scenarios where the Average function may not provide the expected results and discuss how to overcome these challenges.

The Power of Averaging

Averaging is a fundamental statistical concept used to summarize data and identify trends or central tendencies.

In Power BI, you can easily calculate averages using DAX (Data Analysis Expressions) functions like AVERAGE, AVERAGEA, or AVERAGEX.

These functions work well in many scenarios, but it's essential to understand when they might fall short.

Scenarios Where the Average May Not Be Correct

1. Outliers Skewing the Results: Averages are sensitive to outliers, which are data points significantly different from the rest of the dataset. If your data contains extreme values, the average can be heavily influenced by these outliers, leading to a skewed result.

2. Non-Numeric Data Types: Averages are typically used with numeric data types. If you attempt to calculate the average of non-numeric data, like text or dates, you'll encounter errors or meaningless results.

3. Aggregating Aggregates: Sometimes, you may need to calculate the average of aggregated data. For example, finding the average of already averaged values. This can lead to incorrect results, as the original data distribution is lost in the process.

4. Changing Contexts: In Power BI, calculations are highly dependent on context. When you slice and filter your data, the context changes, affecting the average's calculation. Failure to account for context changes can lead to unexpected results.

Overcoming Average Pitfalls

1. Use Measures Carefully: Instead of calculating averages directly in visuals, consider creating measures that explicitly define how the average should be calculated. Measures allow you to define the logic, handle filtering, and provide more control over your calculations.

2. Visualize Data: Visualization tools in Power BI, like scatter plots (example below) and box-and-whisker plots, can help you identify outliers and understand the distribution of your data. This visual context can guide your decision on whether to use the average or other statistical measures. 

3. Consider Weighted Averages: In situations where not all data points are equally important, consider using weighted averages. This method assigns different weights to data points, giving more significance to certain values over others.

4. Data Cleaning and Transformation: Address outliers and non-numeric data types by cleaning and transforming your data before performing calculations. You can use Power Query in Power BI to handle data cleansing tasks effectively.

Conclusion

Power BI is a versatile tool for data analysis, and its "Average" function is a valuable tool when used appropriately.

However, it's essential to recognize its limitations and be mindful of situations where the average may not yield the correct results.

By understanding these challenges and applying best practices such as creating measures, visualizing data, and considering weighted averages, you can ensure that your data analysis in Power BI is both accurate and insightful.