Category: Visualization

Soheil Bakkshi shows how we can conditionally format line and area charts with Power BI:

One of my customers asked me to show time series in line charts and area charts. But she want’s it to be conditionally formatted based on the average value over time. Let’s keep it simple, she wants to show “Sales by Year Month” in line chart, but, highlight the data points that are below “Average Sales per Year Month”. As you may know, we currently do not have the luxury of formatting line charts and area charts. But wait, this post is all about that. Let’s dig into it.

From the above scenario, you perhaps already guessed that we need to create a measure which defines the colour based on “Average Sales per Year Month” to be able to format the chart conditionally. If any data point is below the “Average Sales per Year Month” then we highlight it in Orange, if it is above the “Average Sales per Year Month” then we stick to the default colour.

Let’s do it.

This is definitely not straightforward, but once you see the process, it’s pretty neat.

Prathy Kamasani shares how to build a layered donut chart in Power BI:

You can view the full report here – https://prathy.com/portfolio/school-uk/

The tricky bit of any good data visualisation is finding interesting data, inspiration and story. In my report, my story was to show the comparison of Ofsted school ratings among the total number of schools. I also wanted to carry my story between report pages with colours.

So in this particular visual, I wanted to make it visually appealing, so I decided to show two metrics on each donut visual, the measure I want and the total number of schools. Then I resized each visual in a way so that they look like one visual. Those days there were no grouping, but now we can group them as one visual too. Also, when I first designed this report, default Power BI donut chart didn’t have an option to resize the ring, so I went for Circular gauge by MAQ software. Now we can use the default Donut chart too.

Click through for more details.

Kasper de Jonge shows how we can use Charticulator to build out custom visuals in Power BI:

Recently I attended an excellent session by Andy Kirk on the state of data visualization for 2019. One of the tools Andy is most excited about is called Charticulator. For those of you that haven’t heard about it, Charticulator is an open source project from Microsoft Research. Using a web UI you can design almost any charts by interactively specifying constraints (NO code).

The best news is that you can use these visuals directly in Power BI. You can even use your own data coming from the PBI data model. This came as news for many at Andy Kirk’s session so decided to do a quick post on it. Building very custom charts entices a lot of data artists but it is often hard to bring this to main stream BI product. The ease of the Charticulator and Power BI integration brings this type of data visualizations to a much broader audience.

This is a lot easier than telling people to learn D3, though Charticulator will necessarily have more limitations than writing all of the code yourself.

Jon Fletcher has a good tip for snazzing up a Power BI dashboard:

First question, why bother with layouts?
Using layouts in Power BI allows a user to make their visuals stand out better, the page looks professional and more appealing to its audience.

Second question, why PowerPoint?
The default page size in Power BI desktop is 16:9, (this trick doesn’t work for other Power BI page sizes), which is identical to a PowerPoint slide.
Therefore whatever is designed in PowerPoint will fit onto a Power BI page perfectly. Also PowerPoint is very easy to use; most people are familiar with it.

Click through for an example. It’s easy to go overboard with this, but Jon does a good job of using a muted color so that the edges don’t overwhelm your eyes. I might knock it down a shade or two further from that, but regardless, this is a nice tip.

Tomaz Kastrun takes us through a new visual in Power BI:

Decomposition tree is a data presentation of slicing and dicing of selected metrics based on the attributes of these metrics or with combination of other metrics. Another great aspect of this visual is to analyze the selected variable with many metrics or attributes (dimensions) as the same time.

It’s not the type of visual I’d want to see on a dashboard, but I can see it as quite useful in exploratory data analysis.