What is DAX for Power BI and why you should learn it in 2022

Power BI What is Dax

What is DAX?

DAX is an abbreviation for Data Analysis Expressions, a language created by Microsoft to interact with data in various platforms such as Power BI, PowerPivot, and SSAS tabular models. It is intended to be straightforward while demonstrating the power and flexibility of tabular models. In some ways, it’s like Excel formulas on steroids. Using Data Analysis Expressions will truly unlock Power BI’s capabilities.

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Why You Should Learn DAX

DAX Opens Up A Whole New World

If you are a Power BI user, learning Data Analysis Expressions will seem similar to learning how to use formulas in Excel. You used to structure your tables, add charts, and click the sum/average/button (), but then you were introduced to VLOOKUP, IF functions, and so on. However, this comparison is not entirely valid because, in my opinion, Power BI is already an extremely powerful tool, even without Data Analysis Expressions. 

On the other hand, anything beyond light use in Excel requires formulas. That being said, learning Data Analysis Expressions will introduce you to a new world of Power BI. The most significant feature you will discover is the ability to select, join dynamically, filter, and so on. The dashboard can accept user input and dynamically generate calculated columns, measures, and tables.

Fewer Headaches

It doesn’t take much experience to find yourself cursing at your screen because your dashboard isn’t delivering the expected results. You will be surprised at how many headaches you can avoid or altogether avoid once you learn how to use Data Analysis Expressions (in some hacky way). The ‘blank’ value in the card widget is a simple example. When displaying numerical data in a card, such as revenue, it will return ‘blank’ if your filters are set, so there is no revenue to show. 

However, instead of ‘blank,’ a more natural way to display ‘no revenue’ would be ‘0.’ You can create a measure by adding a ‘0’ to the formula with a very simple Data Analysis Expression, which means you will never have to see ‘blank’ again.

Speed Up Your Dashboard

The more proficient you become in Data Analysis Expressions, the smarter dashboard you can make. Using Data Analysis Expressions, you can make smarter calculated columns and/or measures limiting the data the dashboard can fetch to create visuals. Even though some Data Analysis Expressions can push the data engines to their limits, a well-written expression can speed things up while limiting resource usage.

Why You Shouldn’t Learn DAX

Although some people are always excited to study Data Analysis Expressions, the above-listed compelling reasons make a good case for why you should use it. But, it is essential to emphasize that it is not for everyone. Here are some reasons why learning Data Analysis Expressions should not be one of your top priorities:

Steep Learning Curve

It is not possible to fully understand Data Analysis Expressions in a single day. Although you can quickly begin writing some basic code, it will take time to understand how the various filter contexts interact. It would help to determine how much effort you are willing to put in and then make an informed decision.

You Can Do a Lot With Alternatives

Even before your data goes to one of the widgets, Data Analysis Expressions allow for extensive data manipulation. Numerous alternatives exist for anything that does not need to be dynamically generated. Adding new extra columns to your dashboard, for example, is just as simple with Python.

Dashboards Can Become Cryptic for Outsiders

Including Data Analysis Expressions in a dashboard adds another level of complexity. This shouldn’t be a problem if you’re the only one building or manipulating the dashboard, but it may complicate things for those working in groups. Due to Data Analysis Expressions’ confusion, colleagues unfamiliar with the language become stuck in dashboard manipulation.

The need and desire to simplify things should not be used as an excuse to stop moving forward, but it is something to consider when taking this next step in Power BI.

The 80/20 Rule

The Pareto Principle, also known as the 80/20 rule, states that 80% of the result can be obtained with only 20% of the effort, and vice versa. Hardcore Data Analysis Expressions enthusiasts will be disappointed, but I believe 80% of the work can be done without Data Analysis Expressions. Power BI is a strong tool that allows even novices to create useful dashboards and insights. Of course, more advanced versions will rely on a large Data Analysis Expressions’ partition, but many dashboards are relatively simple and can answer the user’s needs without extensive code. 

The investment is not worthwhile for many Power BI users. In today’s world of 24/7 connectivity, freelancing platforms, digital nomads, and other similar things, it may be easier to outsource the Data Analysis Expressions portion of your dashboard to a professional.

DAX Is More Than Power BI

If you invest the time to learn Data Analysis Expressions, your newly acquired skill must not be limited to a Power BI environment. It can be found in Microsoft tabular products such as:

  • Microsoft Power BI
  • Microsoft Analysis Services 
  • Microsoft Power Pivot for Excel

Not to mention that the Data Analysis Expressions syntax is very similar to Excel formulas so knowledge can be transferred to this widely used and popular software.

It Makes You a Better Data Professional

DAX can only be used in environments that support it. So, knowing how to use it extends far beyond its scope. Because it is based on a system of different nested filter contexts where performance is most important, it alters your perspective on tables and data filtering. You might be capable of improving the performance of some Python code you wrote earlier in the afternoon by writing an intelligent piece of Data Analysis Expressions code in the morning. 

In other words, learning Data Analysis Expressions will help you think more efficiently about how to merge, filter, select, and manipulate data.

Where To Start Learning DAX

So it appears that you are persuaded to explore the world of Data Analysis Expressions. That’s great news because a larger community benefits any software environment. You’re probably wondering where to begin. While being an expert at Data Analysis Expressions is difficult, it’s not difficult to learn the fundamentals. Understanding the concepts will take time and effort, but you do not need a Ph.D. in Computer Science to start.

Like most programming languages, a wealth of free online resources, documentation, videos, and communities can teach you everything you need to know. Some useful websites to visit are:

Finally, besides these free resources, I highly recommend reading The Definitive Guide to Data Analysis Expressions by Marco Russo and Alberto Ferrari, which can be considered the language’s bible.


So there you have it. You can now decide to dive into the world of Data Analysis Expressions and, if so, where to begin. It is a straightforward language to write, but it can be challenging to grasp at first. Once you understand the underlying theory of Data Analysis Expressions, it is simple to write any formula and experiment with multiple nested contexts. Analysts can use the Data Analysis Expressions programming language to discover new ways to calculate data values and gain new insights.


How much time is needed to learn DAX?

It can take about 4-6 weeks to learn Data Analysis Expressions. The time it takes to learn can vary depending on the user’s prior experience with Microsoft Excel and business intelligence tools. Depending on the learner, mastery of Data Analysis Expressions at the advanced level may take several months to years.

Is it necessary to learn DAX for Power BI?

It is only necessary if you want to unlock Power BI’s full potential. Learning Data Analysis Expressions is required to create dynamic dashboards.

Is DAX important to learn?

Knowing how to create effective Data Analysis Expressions formulas will assist you in making the most of your data. When you have the required information, you can begin to solve real-world business problems that affect your bottom line.

What is the difference between DAX and SQL?

SQL is a structured query language, whereas Data Analysis Expressions is a data analysis formula language. When our data is stored in structured database systems such as SQL server management studio, MySQL, or others, we must use SQL to retrieve it.

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