Beyond Power BI: Why DAX Skills Are More Valuable Than Ever in 2025
Introduction
In 2019 I wrote an article titled “Power BI: What is DAX? And Why you Should (or Should Not) Learn It”. Almost 100k people read it and for a couple of years, this got me the number one spot on the Google search results for “what is dax”. While I am — unfortunately- no longer topping that chart, the content still applies. The main reasons I listed in the article were:
- It opens up a new world
- Fewer headaches
- Speed up your dashboard
- DAX is more than Power BI
- Makes you a better data professional
More than 5 years have passed since I wrote it, and — at least in my opinion — DAX is more relevant than ever. In this post you can find additional reasons to take the plunge in learning this language.
Why Learn DAX
It Makes You Stand Out
Working with data is an attractive field. With the abundance of courses, bootcamps, academic programs and whatnot, there is a huge influx of data analysts, data engineers and data scientists. For every job listing there are more applicants than ever, making it more important to stand out. I believe one of the best ways to stand out is to have a solid business knowledge of the field you are applying to. Another great way is to master valuable technical skills that few others do. Learning basic Python, Power BI or SQL to pass an interview is a skill that one can acquire fairly quickly, DAX is not. Just to be clear, I am not saying saying advanced DAX is harder than advanced Python or SQL, I just mean that the initial barrier to get started is higher.
Being proficient in DAX will require time and effort, but this will definitely give you an edge when applying for a data position at a company using the Microsoft stack.
AI Is Not Very Good At It (Yet)
AI is getting increasingly better at writing code and can produce relatively good code for easy to mediocre problems. However, it is surprisingly bad at writing DAX. I have yet to come across a model that can output decent DAX, at a level at which it could also write Python or SQL.
DAX scripts mostly live in .pbix files and are hardly shared on public repositories. Most of the popular AI models are trained on GitHub which might explain this lack of skill. Additionally, DAX is also less documented than other languages and requires more abstract thinking.
However, the models keep improving and it’s a matter of time before they keep up and even surpass.
It Is Hard
“If it was easy, everyone would do it” is a popular saying that applies here. By going through the effort and time of really learning DAX you separate yourself from a lot of others. DAX being a hard language to learn creates an opportunity for you to do something others won’t.
I noticed the final video of the alpha & the omega of DAX, Alberto Ferrari’s course on DAX Studio only has 6k views. This absurdly low number shows how few are actually taking the deep dive into this language.
There Is No Spoon
Once you can write DAX you will start seeing beyond the limitations of Power BI. I have amassed hundreds of thousands of reads on my different Power BI “hacks” where I use DAX to solve problem that were perceived as impossible like for example “Implement AND/OR Selection” or “Create a Stacked Funnel Chart” and many more. By combining some creativity with DAX you can meet business needs other say are unattainable.
Fabric Integration
As mentioned in my initial article, DAX is not limited to Power BI. When I wrote that article, Microsoft Fabric was not launched yet. Meanwhile Fabric has been here for almost 2 years and is growing steadily. As companies start moving their data to Fabric there will also be an increase for semantic-model related business questions. This is another place where DAX can come into place to solve problems and answer questions others deem impossible.
Increase Your Value On The Dating Market
On your next date, casually explain why you would use SUMX instead of SUM to make sure the total amount for the drinks checks out and watch them be in awe. Obvious recipe for success.
How To Get Started
Alright, so it seems I have convinced you to dive into the world of DAX. That’s good news since a larger community benefits any software environment. At this point you are probably wondering where to start; Well, granted learning DAX is challenging but not complicated per se. It will take time and effort to understand the concepts, but nor will you need a PhD in Computer Science to get started.
No worries, I am not going to pitch you some expensive online courses. As with most (if not all) programming languages there are an abundance of free sources, documentation, videos and communities online that can teach you everything there is to know. Some useful sources I frequently visit are:
- Power BI community: the source :)
- Guy in a Cube: amazing YouTube channel with a ton of tutorials
- r/PowerBI: Power BI’s subreddit
- The official DAX documentation
- https://dax.guide: Some more great documentation
- DAX Formatter: Free tool to make DAX code more readable
Finally, aside from these free sources, I do strongly recommend reading The Definitive Guide to DAX by Marco Russo and Alberto Ferrari, which can be considered the bible of the language.
Conclusion
Well there you have it, 5 years later and even more reasons to deep dive into DAX. Additionaly I shared some good resources to start learning (same ones as the previous article) and threw in some Matrix references for absolutely no reason. Happy developing!
About me: My name is Bruno and I work as a data consultant. Check out the other stuff I built like a Mumble Rap Detector or connect with me via my website: https://www.zhongtron.me
