Visualizing data with the ‘what, so what, now what’ framework

Have you ever sat through a presentation full of fancy, detailed graphs that did nothing save give you a splitting headache?

I’m convinced that many people put complicated data visualisations into their decks just to confuse us. I’m not sure whether it’s their strategy for making us accept their flawed arguments, or rather they just want to show how academic they are. It’s true that statistics persuade us, but it’s important that they do this through highlighting key data and not just through pretty colours.

Today I implore you, please don’t be one of these people! Use your data wisely!

When considering whether to add data into your presentation, think carefully about how you will visualise the information.

A framework that I often follow and derive great value from is the ‘what, so what, now what’ model. Let’s go through each of those ‘what’s’ in more detail.

What

The ‘what’ is the presentation of the data. Should it be shown as charts, data tables, pictographs, infographics, prose, or images? There are several directions you can head when displaying data, but the primary goal is to make the data easy to understand. You need to communicate the message of the data in the simplest way possible for people to comprehend.

To test the clarity of your visualisation, place the charts in front of someone and without giving them any context, ask them to explain what the visualisation communicates to them.

The ‘what’ establishes the context for the ‘so what’ and ‘now what’ to then build upon.

So what

The ‘so what’ is all about the explanation and insights that have been presented as per the ‘what’ section. You would very rarely present a graph without any explanation as to why it’s being displayed. In the ‘so what’ we need to highlight the key information. The audience should not be having to translate the meaning or find the insights themselves. You should be calling out the key points and explaining their meaning. The ‘what’ is acting as a data ‘proof point’ to backup the message that you are communicating in the ‘so what’.

A key question to answer in the ‘so what’ is why this should matter to the audience. In other words, the response to the query ‘why are you showing me this?’ and ‘why does this matter to me?’

• Use frames of reference to help communicate the data, especially when the numbers are either really big or really small (e.g. this number is equivalent to 25 stadiums full of people)
• Pick out key facts and use them to tell stories. We are emotional beings and numerous studies have shown that we respond more strongly to emotional stories rather than numbers on a page.

Now what

The ‘now what’ is the fun part. This is where you provide recommendations on how to respond to the insight you’ve shared. Should we take on a new course of action, or should we pause and monitor the situation before acting.

Data and reports should always have an objective. The reason for showing the data should not be ‘because it’s interesting’. But rather It should have an impact on decision-making. Should wee do another project. Should we pivot our funding. What experiments might we run?

When proposing future actions on the back of data analysis a common next step is to put your lab coat on and run some experiments. Start with hypotheses based on the observed data and try to construct cause and effect relationships. Design experiments to test these cause and effect relationships, then execute them and measure the results. This may sound familiar to you as the ‘build, measure, learn’ methodology used as a feedback loop for iterative learning and development.

Remember, “without an opinion, you’re just another person with data!”

Go forth and visualise

Statistics and good data visualisation are not topics that can be learnt in a few hundred words. If you’re interested in this topic, I encourage you to read further and continue to develop your skills in the area. The what, so what, now what model is a great, simple tool that can help guide your data visualisation at a high level.