Thread by @KatGreenbrook

An introduction to Data Communication 🧵

In simple terms, communication is how you share information with someone else. Every day, you are receiving (and sending) communications using your senses: sight 👁️, hearing 👂, touch ✋, smell 👃, and taste 👅

Now, think about how you communicate your data. Chances are, you use just one of the five senses - the sense of sight 👁️. This is data visualization; how data is communicated visually 📊. But you are not limited to this sense in how you can communicate data.

If you use sound to communicate data – you’re practicing data sonification 🔊. You may change the words, volume, or frequency of sound to represent data. Using the sense of touch is data physicalisation ✋. Smell is data smellification 👃. Taste, data tastification 👅

It all sounds crazy! 🤯 But if you think about this in terms of creating accessible data communication, teaming up the senses is not a silly idea. Visualisation is just one way we can communicate data – albeit the most likely.

It’s no longer as simple as it once was to just create a graph – because today, not all graphs are created equally. To understand why you can’t create the same graph for everything, you need to understand the reasons to visualize data in the first place.

In business, there are three reasons to create a data visual: to discover, to inform, or to educate. If you understand your reason to visualize data, you can design something that’s more "fit for purpose".

You may know what it feels like to be "in-flow" when doing analysis. When your curiosity takes over and you’re creating graphs as fast as you’re thinking about them...

During the analysis process, you’re visualizing data to Discover🐈 – or to find data insights. These visuals are usually messy, and unordered, they may be rainbow coloured, and have no metadata. This kind of #dataviz (or chart vomit as it’s been termed) – it’s okay to make.

These visuals are okay to make because, for a Discover🐈 visual, you’re the only one who is going to see it. If you’re creating data visuals to discover insights, you are the audience. You can design to your own liking – there are no rules.

Discover🐈 visuals are the only type you create just for you. Discover visuals are part of the analytics process. The other two types (Inform🐓 and Educate🦉) are forms of data communication – because they’re created for someone else.

A common reason to create a visual when communicating data is to inform. Often, our data is kept in the dark - by either data noise or hard-to-navigate systems. If you’re creating an Inform🐓 visual, you’re essentially trying to make it easy for your data to see the light ☀️

Inform🐓 graphs are designed to make data easy to access. A dashboard is an example of a visualization created for this reason – where specific metrics are displayed in a very ordered way, making it easy for someone to see the latest measurement.

When communicating data, it’s common to OVER estimate an audience’s knowledge (by thinking they know more about the data than they actually do). The audience for Inform🐓 visuals needs to be able to understand the data without context.

If the audience you’re designing for are subject experts, an Inform🐓 visual is all they need. But, if your audience doesn’t know much about the data you’re communicating, (and you need to upskill them) then you’re looking at the last type of visual: designing to Educate🦉

Data has 4 stages: data, information, knowledge, and wisdom. Wisdom (the desired end state) is applied knowledge. In terms of communicating data, what you hope to achieve is for your audience to increase their knowledge.

When creating Educate🦉 visuals, your audience needs to understand why the data is important. Visualizing data to educate involves #datastorytelling, so you need to be clear on what YOU the storyteller want your audience to understand.

So, next time you go to create a data visual ask yourself: am I doing this to Discover🐈, to Inform🐓, or to Educate🦉? Sometimes, just being aware of your reason can help you create a better visual 📊

Here are a few #dataviz tips 👇

✏️ #dataviz tip 1: Know your reason. One of the biggest mistakes I see people make in terms of data communication is when they share their discovery visuals. Discover🐈 visuals can look however you like – because no one else is going to see them.

Discover🐈 visuals may look like this. You understand them because you’ve gone through the analysis process. But these visuals are not fit for data communication. You will often find you need to visualize the same data for more than one reason.

It’s okay to visualize the same data, differently. If fact, you probably should. Because using one design to try to Discover🐈, Inform🐓, and Educate🦉, will most likely not succeed for all reasons.

All three reasons to visualize data work together within the insight pathway. So, before you create a graph – know your reason for doing so.

✏️ #dataviz tip 2: Know your audience. This only applies to Inform🐓 and Educate🦉 visuals. As with any communication, understanding your audience is key to its success – and data communication is no different.

As a minimum, recognize that what you’re designing is not for you - so don’t design it as if it were. When you realize this, your data visual will get better because you’ll start to think more about who it is for and how they like to be communicated with.

I tool I use often, to get an idea about who I’m designing for, is a User Story. You try to fill out this template from the perspective of your audience. Depending on your type of visual – Inform🐓 or Educate🦉 – your User Stories will be written slightly differently.

It’s easiest to write User Stories for Inform🐓 visuals e.g. #dashboards. The “I want” identifies the data metric. The “So that” determines if the data metric matters. If the “So that” section is weak, it begs the question: does the data metric really need to be in the dashboard?

Writing user stories for Educate🦉 visuals is harder e.g. #datastorytelling. As the storyteller, you need to understand what YOU want your audience to do after viewing your visual because storytelling is about influence.

Disclaimer: User stories are not 100%. Just because you write them, doesn’t mean your audience thinks this way. But they make you actively think about who you’re designing for. And this helps you create a better visual.

✏️ #dataviz tip 3: Know your message. This only applies to Educate🦉 visuals because understanding your message is key to data storytelling. When creating these visuals, write the narrative first. And writing it down (not just thinking you know it) helps you clarify it.

✏️ #dataviz tip 4: Know your Design. A big mistake I see people make is trying to communicate using their discovery visuals. But it doesn’t take much to change a visual from one to Discover🐈 to one to Educate🦉

Ask yourself one question… “what do I want people to learn from your graph when they read it?” Put this in the title. This is called a takeaway title, and all Educate🦉 graphs should have one.

When you’ve identified your takeaway, make it super obvious in your graph. You can do this using design contrast. Educate🦉 graphs are forms of data communication. Discover🐈 graphs are tools for analysis.

In summary, understand your reason to visualise data. It will help you to create a better #dataviz 📊

I hope this thread sheds a little bit of light💡, on the visual side of data communication.

https://twitter.com/KatGreenbrook/status/1584687214016884736