Monthly Archives: Sep 2020

Spaghetti Monster. Visualising multicategories.

When I say “multicategories”, I mean more than 4 categories. Sometimes, a challenge of visualizing multicategories is like an old polish proverb “eat a cookie and have a cookie”, which is hard to put into practice. I often observe how data analysts try to approach this challenge. Common scenarios are for products, countries, businesses, departments, teams, agents or cost centres. For all these data, they try to find out meaningful insights by depict patterns and highlight interesting points… mostly on one chart. That visual decision creates a beautiful piece of abstract art riched in colours, shapes, different sizes of objects, patterns or crossing lines.

Could you imagine how someone must be determined and persistent to look at and try to understand the column bar chart with 15 categories presented on 5 years horizon? Which category has upward and downward trend? Which is a leading one? And foremost which one is which?

When I think about “multicategories”, my first association popping into my head is “clutter”. The clutter is one of the greatest factors of cognitive overload. To understand clutter impact imagine that, you try to talk to your friend in a crowded space like a bar. You are all ears to follow her or his, and even then, you are not successful. The same effort your brain does, when it is exposed to the flashy visualization.

So how to overcome this challenge?

Both visualisation present the same information:

  • trends over time,
  • product comparison,
  • the best and the worst-selling products.
Spaghetti Monster
Clear view

Doesn’t it look like Spaghetti Monster? You rummage with a fork to find a juicy bit of meat. Similar is with decoding some information from this visualisation (line chart), it costs a lot of effort and time. Our brain decoding one information eg. line colours, then stores it in memory, then compares lines position on the chart, then look for trends for each of lines goes back and forward through the chart to make a sense of it.

On the second approach, I’m showing the different strategy to present the same data set. Splitting information into two visualisations gives clarity and ability to draw a conclusion. The left chart enables the receiver to compare sales amount between products and memorise them easily. The right chart provides information about particular product trend in comparison to average sales. In this approach, we guide the audience through data. We pay their attention to important points. We don’t leave them alone having hope that they draw a conclusion on their own.

We are the data storytellers.