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Dashboard Week Day 2: Power BI Day

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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On Day 2 of dashboard week, our resident Power BI expert Madeli set us a challenge of using Power BI to visualise global plastic production and pollution data.

In Power BI we had to harness multiple data sources and complete several tasks including:

  • Merge Joins and Queries
  • DAX Formula
  • Custom Visual
  • Interaction
  • Merge with an External Data Source

Merge Join and M Queries

I found the user interface of Power BI relatively intuitive to navigate, as it inherits many features from the Microsoft Office family. Merging different tables is easy using the ‘Merging Queries’ actions. Firstly, lock ‘Edit Queries’ to open the visual power query builder and select ‘Merge Queries’.  As with Tableau, select the fields that you wish to join on, ensure that data types are consistent between both fields and specify the join type (left, inner etc.).

DAX Formula

DAX can be used to create calculated fields in Power BI. The syntax is significantly different from Tableau and I felt frustrated at not being able to compute simple calculations (LOD’s, Table Calcs). I did, however, manage to classify countries based on their pollution rate and population using conditional statements.

Interaction

Power BI default settings include interactions between charts with common fields. This is great, as it allows for interactions between two worksheets without setting up any actions, as you would have to in Tableau.

Custom Visualisation

The custom visualisation marketplace is an excellent feature and I enjoyed selecting additional visualisation templates. Also, could this be a development opportunity for Tableau, with importable visualisation templates available in the ‘Show Me’ window?

Merge with an External Data Source

I gathered data relating to global disposable coffee pod production between 2011 and 2019.  and Using Power BI’s standard line chart I was able to easily configure a forecast in coffee pod production for the next 10 years.

Below is my visualisation in Power BI:

Any feedback is welcome in the comments below!

In summary, my main take-home points from today’s dashboard are:

  • Power BI is a reasonable alternative to Tableau, with some handy ETL capabilities.
  • I prefer Tableau to Power BI, for now.
  • Global leaders need to enforce rapid changes to environmental policies to minimise our plastic pollution rates. The numbers are highly alarming.

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