When I first started using Power BI back in 2015, I obviously encountered DAX, or Data Analysis Expressions—the secret sauce that makes Power BI so powerful. At first, I figured, “Hey, it’s just another formula language like Excel, right?” Well, yes and no. As anyone who’s spent time with DAX knows, it can make or break your experience in Power BI. It’s not just a tool; it’s almost like learning a new way of thinking. And for someone a bit stuck in their ways like me, that can mean both incredible insights and endless frustration.

So here’s a snapshot of my journey with DAX—the good, the bad, and the downright confusing. Hopefully, if you’re starting out or just feeling stuck, my experience will shed some light on what to expect and how to (eventually) make friends with the language.

Getting Started: When DAX Felt (Almost) Like Excel

When I first started using Power BI, I noticed that DAX formulas had a familiar Excel feel. You’ve got functions like SUM, AVERAGE, and IF. Nothing too complex... right? I could plug in some basic calculations, throw in a few visualisations, and boom—I felt like a data pro. But this “honeymoon” phase didn’t last long.

Then, I hit my first wall: the context. DAX, unlike Excel, doesn’t just operate in a two-dimensional grid. It’s a world of tables, relationships, and filters that interact in ways I hadn’t anticipated. Suddenly, what looked like a simple formula was giving me completely unexpected results, and I had no idea why.

The Context Wall: Learning to Think Like DAX

One of the most challenging aspects of DAX is understanding “context.” In Excel, a formula generally affects a single cell. But in DAX, the context is everything. When you write a measure (a calculation that dynamically changes based on filters), DAX considers the context of the data, which includes things like row context and filter context. Sound confusing? Yeah, it was for me too.

For example, I wanted to calculate the total sales for each category in my business. In Excel, I would have just created a sum for each row and dragged it down the column. But in DAX, I had to consider what context the data was in—was I summing at the row level, or did I need to account for filters across multiple tables? I ended up with totals that didn’t make sense until I grasped how DAX handles row and filter contexts. Once I understood this, it was like a light bulb turning on, but getting there required a lot of trial and error.

CALCULATE() – My Lifeline and My Headache

After a bit of research, I realised that CALCULATE() is one of the most important functions in DAX. It’s basically the gateway to controlling context and changing filters in ways that make your data analysis way more powerful. I felt like I’d discovered the holy grail—until I actually started using it.

The problem with CALCULATE() is that, while it’s incredibly versatile, it’s also incredibly picky. For example, I tried to create a measure that calculated my revenue for specific products, applying only certain filters. But a small mistake, like mixing up filter expressions, would send me into a spiral of errors. Debugging DAX can feel like trying to untangle headphones after they’ve been in your pocket for too long—frustratingly complex.

Over time, I learned to appreciate CALCULATE(), but only after plenty of trial and error. I still keep a few reference guides open when I work with it, as it’s one of those functions that’s simple on the surface but has deep complexities once you dig in.

Iterators: SUMX, AVERAGEX, and the Mystery of Row Context

If I thought basic aggregations were tricky, iterators took things to a whole new level. Iterators in DAX, like SUMX and AVERAGEX, allow you to go row by row through a table and apply calculations. It sounds simple, but they opened up a world of nuance I hadn’t considered.

For instance, I wanted to calculate the average order value, but only for specific types of products. Using AVERAGEX, I was able to create a custom calculation that filtered through my data on a row-by-row basis. But the syntax wasn’t forgiving. One misplaced argument and my measure returned nothing but errors. I’d find myself revisiting the same formulas, adjusting arguments over and over until I got the right result.

Iterators taught me that DAX is less about straightforward math and more about carefully orchestrated logic. I had to think through each function’s role in the formula. It felt like programming rather than just calculating, and that shift in mindset was both challenging and rewarding.

The “Aha” Moments – When DAX Clicks

After struggling with DAX for a while, I started to notice certain patterns. One of the biggest “aha” moments was realising that DAX isn’t just a language for calculations; it’s almost like a set of instructions that tell Power BI how to treat your data under different conditions. Once I began thinking of DAX as instructions, not just formulas, things started to make more sense.

For example, I stopped trying to use DAX as I would in Excel, with a series of nested functions. Instead, I began breaking down each formula into steps: first filtering my data, then applying my calculations, and finally using CALCULATE() to bring everything together. It was like finally learning the rules of a game after playing it wrong for ages.

The Downsides of DAX: Not All Sunshine and Rainbows

As much as I’ve come to respect DAX, it’s not without its challenges. Here are a few of the cons I’ve encountered along the way:

• Steep Learning Curve: If you’re used to straightforward formulas, DAX can feel like learning calculus. There’s a lot of trial and error, and it’s easy to get stuck on small mistakes.

• Troublesome Debugging: DAX errors aren’t always clear, and debugging can be tedious. There’s no simple “trace” feature, so you’re often left guessing where your formula went wrong.

• Complex Syntax for Advanced Functions: Functions like CALCULATE() and iterators require careful syntax and understanding. Misplace an argument, and your result is useless.

Despite these issues, DAX is an incredibly powerful tool that lets you manipulate data in ways I never thought possible. Sure, it’s not perfect, but the results speak for themselves.

Advice for DAX Newcomers

If you’re new to DAX or just frustrated with it, here are a few tips that helped me:

1. Start Small: Begin with simple calculations before diving into complex measures. Don’t try to do everything with one formula.

2. Understand Context Early On: Row and filter context are foundational to DAX. Spend time understanding them, and a lot of other concepts will fall into place.

3. Use Resources and Examples: There are tons of resources online, from forums to Microsoft’s own documentation. Learning from others’ examples helped me make sense of tricky formulas.

4. Be Patient: DAX takes time to get comfortable with. Don’t be discouraged by errors; they’re just part of the learning process.

Wrapping Up: My Love-Hate Relationship with DAX

DAX has been one of the more challenging parts of my BI journey, but it’s also the most rewarding. It pushed me out of my comfort zone and made me rethink how I approach data, and it has made me a far better Power BI Consultant for it. But it’s not a skill you pick up overnight. DAX requires patience, a willingness to experiment, and a good dose of persistence.

If you’re considering giving DAX a shot, or you’re in the middle of the struggle, know that it will get easier. And once it does, you’ll unlock a whole new level of data analysis that’s worth the effort.