Data Analysis

We discussed how the DAX engine is optimised for working with columns, not rows. However, there are many situations where that isn't going to be enough. For those situations, we need something called an iterator.

Filtering, whether implicit or explicit, is integral to analytical reporting. This is because so much analytical reporting is about slicing and dicing. It's about making comparisons.

When dealing with financial reports, it's often critical to be able to look at specific time periods such as years, quarters, and months. It's also important to be able to compare periods of time such as year-on-year, year-to-date, month-to-date, etc.

Let's say you have a sales data model. You can use a parameter to experiment with different discount rates to see the effect on the gross profit margin.

The ability to use DAX is an important skill to have for building great Power BI models. It stands for Data Analysis Expressions, and it's the key to the analytical capabilities available in Power BI.

Relationships between tables are the key to successful data models in Power BI. There may be a lot of tables you are using, and if you have not yet defined relationships, then you need to check to see if relationships exist.

Whenever you import data into Power BI, you often need to transform the data in some way. There are many options available in the Power Query Editor, but there's just a handful that you'll use regularly. One of the first things you should consider when working on your Power BI model is