From Messy CSV to Clean Dashboard: What I Learned Building My First Power BI + Google Analytics Dashboard (Part 1)
I get distracted easily. If something isn't explained to me properly, step by step, it just doesn't stick, even if I've technically seen the right answer once already. So when I sat down to build a Power BI dashboard for a nonprofit client using their Google Analytics 4 data, I wasn't just trying to get a working file by the end of the day. I wanted to actually understand what I was doing,…
Building a Power BI dashboard from Google Analytics 4 data proved to be a steep learning curve for the author. They emphasized the importance of understanding the process, rather than just finding a quick solution. The client wanted more than just a visual representation of their data, but a deeper understanding of the story behind their digital campaigns.
The initial attempt to download a snapshot export from Google Analytics 4 (GA4) and load it into Power BI was met with numerous errors and blank cells due to the export not being a single, consistent table. This led the author to discover that GA4 had a better export tool called Explore, which allowed building their own clean table with one dimension and one metric.
This was a turning point in the project, as it eliminated the need to fix a broken file later on. Power BI's built-in cleaning tool, Power Query, proved to be crucial in preparing the data for the dashboard. The author had to strip out headers, filter out hidden total rows, and convert date formats. This process was challenging, but it taught them valuable lessons about data cleaning and the importance of understanding the data before visualizing it.
They learned that a smaller, fully understood dataset is more valuable than a larger, incomprehensible one.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
