Data Visualisation. Easy Way to Refine Your Dashboard
By: Mark Eltsefon
Tags: Visualization, Football, Tableau

Data scientists, analysts, product owners, and other people who use and present data struggle to enhance their dashboards, which colour to choose, what kind of plot is the most appropriate, what they should include in the background and a lot more. The pivotal moment here is not to forget that our primary goal is to get your message across in the most effective way.

I wasted a lot of time trying to solve this riddle. In the beginning, I was working in the dark so I started to accumulate all sorts of information from different places. Now I have refined this information and want to share with you three important but easy steps to improve your dashboard or presentation dramatically.

I am an avid football fan , hence i used for this presentation football events data from Kaggle.

As i previously mentioned , our goal is to effectively get across the message. Our first goal is to define it. Below there are 2 charts , that illustrate the same data and look similar but their messages are different.

Scored goals in a match

The first plot shows us the overall trend and total numbers .We can glean from this one that there are two peaks (45 and 90 minutes ) , two valleys (46 and 90+ minutes), and in average there are 220 goals, give or take, per every minute.Most of goals are scored with the right foot

The second graph also shows us trend and total numbers. However we derive an additional information

The magnitude of peaks and valleys are quite different

There are no intersections between lines. It’s always : right foot,left foot, head. However there are periods when they are close to each other. For example , at 2 ,45 , 57 minutes.

Let’s assume that Jose Mourinho , our coach, asked us two questions :

What’s the average number of scored goals for the first half?

Answer : we can see this in the first plot : about 210–220 goals

2. Who would be better to enter the pitch after 90 minutes — a left-footed football player or the tallest?

Answer : the answer can be found in the second plot. The slope for the left foot is much higher , so we should choose this option (of course there are many other features to keep an eye on and in real life we can’t judge it just by one plot)

The first conclusion : choose the plot based on your message

The second point in our crucial steps is choosing a colour. There are a lot of options here, but I want to focus just on a couple. Let’s take a look at the same plot, but different colour schemes.

Count of shots per the outcome

The first graph is known as complementary color scheme. It’s commonly used as a way to highlight one column/row/bar, etc.

The second plot is widely know as monochromatic. We use it just to for aesthetic reasons when we don’t want to stress any categories.

The third one is just a random choice of colors.

There are many resources that will help you to choose colors for these color schemes. My favourite one is Adobe.

The second conclusion : choose the colour based on your desire to highlight something, just illustrate the overall picture and you don’t want to stand some numbers or trends out or you want to oppose one category of data to another.

As for personal experience I don’t recommend using the random selections.

Many people underestimate the third and final step, but you shouldn’t. It’s a vital part of the improvement programme. It’s called the data-ink ratio. The concept is simple, and it’s just amount of ink that is used to present actual data divided by the total ink used to print/create the plot.

To illustrate the difference, we have two pictures above. The first one has a meagre data-ink ration, and the second plot has a high ratio. The clutter on the left in the second image prevents us from interpreting the data. We tend to make our graphs overcomplicated because we think we come across as a creative and innovative individual. It is a wrong assumption; all this creative clutter conceal the data and the main message from your audience. “Just be simple” is the prime advice.

The second conclusion : maximize data-ink ratio as much as possible

To create really cool, engage and meaningful visual aids you need to delve a lot deeper, but these three steps will ease your way .