Visualisation techniques to better communicate your data

Data is often a great resource to find something that gives the customer added value, and even to find stories about your brand. You can also get a better understanding of the way your company performs, giving new directions for the company. This is all good. This you already know.

But displaying the data as is, will often prove meaningless to those who have not taken their time to sit down and really dig through it. And sometimes you don’t know what the data tells you. Visualising data interprets the data into something easier to understand, and which the brain can interpret way faster. Done right, that is.

I am an Art Director and a UI/UX Designer who has been visualising data in the context of newsrooms for some years now. My take on data visualisation has for the most part been in the context of telling a story. But lately, as I started working for Snowball I’ve also done visualisation of data connected to sales reporting and even a control panel.

I love thinking in terms of storytelling also when I’m creating products because I think it adds a different dimension to how we create and build products. It gives direction to what we create. I will not go deeper into that now, but I wanted to explain my continuous use of the term story.

I’ve rounded up five points I’ve found useful when exploring data to be visualized.

1. Exploring and structuring your data

Alright, so you’ve got your data. Where do you start? First things first: Figure out if you can get usable value from your data. In journalistic terms, you are searching for a story. Visualising data can be a fantastic tool to explore the data and get a greater understanding of it yourself. Sometimes you don’t know if your data has value to communicate. But if you try to visualise it in several different ways, the use of it might become clearer. Trends will become more evident. Or you might find that it is just a dead end.

Here are some ways to structuring your data:

  • Geolocation
  • Alphabetically
  • Chronologically
  • Value
  • Clusters

You can try putting your data in a visualisation tool. Just try the different graphs, and see what you can find. Here is an overview of a set of different tools to visualise data.

This one is the latest one I’ve been into.

2. Find what is best for your user

Maybe you found a lot in your data. Then it is time to narrow it down. Imagine you are telling a story through your data. Figure out the story. All you add should take the user in the direction of the storyline. Pinpoint what is important for the product, what is less important and what is not necessary at all. Don’t add as much as possible just because it could be interesting for a handful of people. It can easily become too crowded. Add too much and it will be difficult to navigate. 

3. Context and mode

What mode will the user be in as they are presented with the data? Or what mode do you want to put them in? Will they be in a “Quick mode” where they need to extract meaning as quickly as possible? Or will it be “Explore mode” where the variety of data is interesting and the user should be able to dig deep? Maybe you want to put them in a “Guide me mode” where the complexity of data can be somewhat hidden and somewhat portrayed as you guide your users through the data. Understanding your users’ mode will help you understand the complexity of data your user will be open for.

4. Enhance the story

Enhance the data your story needs, the data your user needs. 

To make sense in a world of many impressions, we as humans use gestalt principles to categorise what we see. There are five of them. I will mention two to make use of when you are visualising data:

  • Proximity
  • Similarity
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To visually group items you could put the items close together (proximity) or you can make them look similar. By using similarity and dissimilarity there is a set of concrete visualisation techniques to enhance what you need in your graph. It will guide your users to where you want them to look. (Actually these techniques work for any type of visualisation data or not. Useful, right?).

  • Size
  • Enclosure
  • Rotation/orientation
  • Stroke size
  • Position
  • Line Height
  • Added markers
  • Shape
  • Colour
  • Intensity of color
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Use these tools well. Do not over do it. For example, by adding too much color to distinguish everything from each other you will easily create chaos instead of clarity. If you seek to enhance everything - nothing will be enhanced. And it will be difficult for the viewer to know how to engage and understand.

5. The value of visual appeal

As the last point I would like to stress this: Do not to forget the visual appeal. Nice design gets attention. It is easier to engage in, it’s easier to sell. As put in a great book which greatly inspired this text: "Design is to data as cheese sauce is to broccoli."  - Lankow, J., Ritchie, J., Crooks, R. Infographics: the power of visual storytelling.

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