Power BI: Table vs Matrix
Power BI has 2 similar visuals for presenting tabular data: Table and Matrix. This leads to confusion on which one to use when. Below is a brief summary on the differences, advantages, and disadvantages between these two visuals. Let me know in comments if I have missed anything.
Table
As the name itself suggests, this is a basic 2 dimensional visual to represent tabular data. Its a primitive 2-D grid representation of the data. If you want to add more dimensions, you can only add them as ‘values’, which will appear as new columns. Basically, flat structure of the data.
For example, in the below sample data, we have 3 dimensions — Client, component, and region. Consider that we want to present average performance and reliability across these dimensions, which will look like below in table visual.
As you can imagine, it gets hard to interpret the data as the number of dimensions and measures grow. If there 2 dimensions and each have 10 possible values, we will need 100 rows to represent all possible combinations! This is the major drawback of this visual as there is no easy way to summarize by higher dimension and drill down further to other lower dimensions. That’s where Matrix visual comes in.
Matrix
To be blunt, Matrix is just a fancy name, given to excel’s pivot table in the world of Power BI. Just like pivot table in excel it becomes easier to represent the data across multiple dimensions. In Matrix visual, you have the option to add rows, columns, and values unlike Table.
Below is the brief what exactly these mean and enable us.
Rows: Each dimension in the data can be added as a row in a Matrix visual. If you add multiple rows, matrix automatically enables drill down mode. Using this mode, you can dig from a higher aggregated dimension to other lower dimensions. Below example demonstrates this concept.
Again considering the same sample data which has 3 dimensions: Client, component, and region. Below are the various drill down options available.
1. Using the drill down buttons shown above the visual user can deep dive between these dimensions. Basically, you can switch from one dimension to other using this capability.
2. Using tree-like looking button, a user can expand all the dimensions in the Matrix giving a concise way to represent and compare data across dimensions. If there more than 2 dimensions, each button click enables you drill down further into the tree.
3. Further, a user can click on a particular dimension value and drill down only on this value. To do this, you can right-click on matrix cell and select drill down.
Columns: This basically groups all the added values into a single column group for each of the column’s dimension value as shown below. Here we have added region as the column and its unique values, EMEA and NA are shown as column groups.
Also, matrix offers a lot of conditional formatting options. For more details on matrix conditional formatting, check out my other blog post here.
In summary,
If you want to display basic 2D information with less number of values per dimension, use Table visual.
If you want to represent data which has many dimension values and want to equip a user with rich drill down and formatting experience, use Matrix.
But as the Matrix is super-set visual of the Table, i.e., a Matrix can do everything what table can do and much more, its always a good idea to use Matrix by default. Let me know in the comments if I have missed anything.
