Custom vs. Conditional Columns in Power BI: Optimizing for Better Viewing in Power Query

As part of my ongoing project to enhance my Steps Count project, I've been meticulously refining the Power Query. After successfully removing extraneous columns, my next challenge was to enhance the dashboard's clarity and utility. The essential metrics—steps, miles, and calories—needed distinct columns for straightforward display and analysis.

Initially, I experimented with a single conditional column to manage all three metrics. This approach required filtering out the metrics to focus on one at a time. This not only made direct comparisons difficult but also reduced the dashboard's overall efficiency and user-friendliness.

I noticed my dashboard needed some improvements, so I decided to try something new. Instead of having all the information—like steps, miles, and calories—jumbled together in one place, I made three separate columns for each one. This change made my dashboard much better because now you can see everything clearly at the same time without having to switch back and forth.

Some details I picked up on custom versus conditional columns in Power BI:

Custom Columns: These allow you to tailor your data exactly how you need it. For basic tasks, they are straightforward to use. However, as your needs become more complex, understanding the M language—a specific programming language used in Power BI—is essential.

Conditional Columns: These are simpler to use, functioning much like filling out a form to organize your data. They work well for basic categorization. However, I found they are not ideal when you need to manage and view multiple metrics simultaneously without confusion.