How To Detect And Visualize Outliers Using DAX In Power BI [2023 Update]

Knowing how to detect outliers in datasets and how to visualize them is important for data analysts because it helps them identify potential errors or anomalies in data that can skew statistical analyses and modeling results.
Outliers can also provide valuable insights into unusual or unexpected patterns in the data that may be indicative of underlying trends or issues, facilitating more effective data-driven decision-making. In this video, Sam is going to demonstrate how you can detect and visualize outliers using DAX in Power BI.
****Video Details****
00:00 Introduction
01:13 Determining the logic
03:44 Doing logic in a dynamic way
04:00 Criteria for outliers
05:22 Outlier detection logic
08:10 Grouping
09:15 Conclusion
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[@EnterpriseDNA]

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[@RelaxingGamingMusic85]

Great video as always Sam. :) Love the work you and the guys at EDNA are doing

[@EnterpriseDNA]

Hi Damien, glad that you appreciated our content! If you haven't yet, you can subscribe to our channel to see all our upcoming Power BI and Power Platform video tutorials and announcements. Cheers!

[@ezequielaguirre3]

Great video!!
If I may... I think it would be more efficient if you indicate in the Outlier Sales measure, in an explicit way a "MAX": [Total Sales] >= MAX('Outliers Detection Logic'[Total Sales min]).
And a MIN in the Non Outlier...
Then the measure would only calculate the Total Sales for the Customers needed.

[@EnterpriseDNA]

Hi Alvaro, glad that you appreciated our content! Thanks for sharing your top as well! If you haven't yet, you can subscribe to our channel to see all our upcoming Power BI and Power Platform video tutorials and announcements. Cheers!

[@rajarshisingh2547]

Pretty cool, will implement it right away. 😋

[@EnterpriseDNA]

Hi Rajarshi, glad that you appreciated our content! If you haven't yet, you can subscribe to our channel to see all our upcoming Power BI and Power Platform video tutorials and announcements. Cheers!

[@yemunnsoe8450]

In statistics, data points that fall beyond 3 standard deviations (3rd stdev) from the mean are often considered outliers.

[@ashrafadel54]

Great content! However, I have a few questions:
1. at 6:16, will it make a difference if I choose a different column name for the `Customers` table instead of customer name? (e.g., VALUES(Customers[Customer ID])).
what i mean by "make a difference" is this:
when I hover over each scatter point now, will the tooltip display the customer ID instead of the customer name? also, isn't it more accurate to use ID, since there's a rare chance that names are duplicated?
2. `Total Sales` column is found in the `Sales` table which is implicitly related to the `Customers` table via the `VALUES(Customers[Customer Name]) expression, correct?
3. at 8:55, from what I understand, `Sales Grouping` measure is evaluated for each data point in the scatter chart. If that's the case, then for each of these points, how does SELECTEDVALUE() function correctly evaluate to `Outlier` or `Non-Outlier`, given that the `Outlier Detection Logic` table is a detached table (i.e., not connected to `Sales` or `Customers` tables)?

[@pepper_lab]

🎉🎉🎉🎉🎉😮

[@EnterpriseDNA]

Thanks for the continued support, autobase16!