When in doubt, make a map

I love maps. From John Speed to the OS, and best of all the beautiful Swiss maps, I’ve spent many happy hours pouring over sheets of paper that bring the world to life.

Maps are also the secret to a trick shared by workshop facilitators, qualitative researchers, and dataviz types: if you want to think through ideas, look for patterns, or identify contrasts, make a map.

A map has two dimensions: up/down and left/right, and plotting objects against those dimensions allows us to understand them more clearly because we can take advantage of what our brains do well (seeing) instead of relying on things they do badly (thinking).

When we think of maps, we tend to think of geographic maps. In this case the dimensions are North/South and East/West, like this:

We can look at that and immediately see that the position of cities in the plot reflects their position in the real world. It allows us to see which cities are close together, and to judge the relative distances between them fairly accurately.

Maps aren’t just for geography

But the reason maps are such a powerful tool for thinking is that the two dimensions can be anything we want them to be. We can map any variables we like onto those dimensions, and when we do so we’ve invented the scatterplot! We could choose, for example, population and average house price:

Once again the position of the cities in the plot lets us see which are close together, which are far apart, and to judge just how far apart they are. That corresponds to real facts in the real world, and visualising those relationships in this way makes them easier to think about. Scatterplot “maps” are, to my mind, a chronically under-used tool for looking at relationships, spotting patterns, and identifying outliers.

But we’re not limited to quantitative maps, those dimensions can as easily be based on judgement as measurement.

A consensus map

Maps are one of my favourite tools to use in workshops, because those dimensions can be literally anything. Working together we can agree where items should lie along each dimension without needing any kind of quantitative measure.

It’s a great way, for instance, to help people prioritise ideas based on how achievable they are versus how impactful they would be:

This is a simple tool that I come back to again and again because it works so well in almost any situation. In fact it’s one of a handful of exercises that I keep in my back pocket knowing that they can inject life into a workshop that’s flagging.

It’s a good illustration of how easily visual thinking can be incorporated into your day-to-day work of thinking or facilitation, without any need to worry about whether or not you can draw. Simple frameworks like this allow us to tap into our visual skills, and create a shared picture of the world.

A map of your thinking

And there’s no need for collaboration, maps are just as useful when it comes to getting your own thinking in order, and especially when it comes to explaining that thinking to other people.

Let’s say I want to summarise different research methods in terms of the tradeoff between the amount of insight you get from each person (“depth”) and the cost/difficulty of getting a large sample (“scalability”). I might come up with something like this:

Clearly there’s nothing quantitative about this. Each of those points could be put in a different place based on different (equally valid) opinions and criteria, but it helps to quickly communicate how I think about the subject.

Maps, as I hope I’ve convinced you, are one of the most powerful tools we’ve invented to help us think about, communicate, and document the relationships between things.

They have a place when we’re trying to articulate our own thoughts, when we’re trying to achieve consensus to make a decision, and when we’re analysing and presenting data; and I think we don’t use them anywhere near as often as we should.

When in doubt, why not try a map?

The power of metaphors

Customer Journey Mapping is, as I tell delegates on my course, just a metaphor.

Then again, the Beatles were “just a band” (at least according to Scroobius Pip).

Metaphors can be incredibly powerful, but also incredibly useful. They help us to understand each other, to reason about things, and to get things done.

To quote the classic Metaphors We Live By:

“…the way we think, what we experience, and what we do every day is very much a matter of metaphor.”

One of the lessons of the book is about the “conduit metaphor” of communication (that our language is a container into which we put meaning for others to extract). This is important because it supports uses of language which don’t make much sense from a purely logical point of view (e.g. the metaphor “more of form is more of content” leads to phrases such as “he is very very very tall”, which we all understand to imply intensification).

The metaphors we use have an impact on what we think and do. What if we choose a different metaphor? In a classic paper, Michael Reddy suggests that a “toolmaker’s paradigm” would be more helpful, underpinning the importance of mutual effort to communicate ideas effectively. As he says in the paper:

“Human communication will almost always go astray unless real energy is expended” 

These metaphors are normally applied to language, but a similar approach could be taken to visual communication. In a fascinating post, Robert Kosara critiques the “Encoding-Decoding” paradigm for data visualisation.

It’s fairly clear that just like the useful, but flawed, conduit metaphor for language, there may be more than one metaphor for how visual communication works. Kosara explains how people actually read visualisations:

“What do we decode? We like to assume that decoding just reverses the encoding: we read the values from the visualization. But not only don’t we do that, we do many other things that are surprisingly poorly understood.”

In other words, the conduit metaphor for dataviz tends to overlook the active role of the person reading it. Studying how people actually use visualisations may help us to build a better metaphor.

Storytelling and visual communication is not a one-way act – we need a metaphor to reflect the active role of our audiences. 

Telling the story with data

This is a diagram from my course about data presentation and infographics.

I use it as a starting point to discuss the skills you need to do the job well, summarised as “telling a compelling story with integrity”.

The idea of the diagram is that too much or too little of any of the three axes tends to be a bad thing.

For instance, too heavy on the “statistician” axis might mean that your charts are accurate and robust, but impenetrable to many people. Too light on the same axis, and you might be committing basic analytical mistakes (perhaps ignoring random measurement error).

It’s a rare person who embodies all of those skills to a truly expert level, which is one reason the best infographics often involve a team of people.

 

Finding your audience

It isn’t necessarily a case of shooting for the middle of the triangle. There’s a zone of acceptable variation around the middle in which competent and engaging data storytelling happens.

What’s appropriate for a scientific publication is not appropriate for your board, or for frontline staff. It’s all about getting the balance right for your audience.

Obvious? Yes, but it’s worth thinking about what it means in practice. Which “rules” of data storytelling are unbreakable, and which need to be tailored according to your audience?

 

How much do we know about what works?

Stephen Few takes a dim view of infographics which he sees as prioritising shallow gimmicks over effective visual communication. David McCandless has been on the receiving end of severe critiques.

He also points out that more work needs to be done to test which graphic forms are most effective, rather than relying on opinion. I agree – we can’t begin to pretend we’re working in a serious field until we approach these questions scientifically.

Robert Kosara has published interesting work showing that pie charts, much derided by experts, are more effective than we thought.

But is communication our only aim? Not always.

 

Telling the story

The science of which data graphics work most effectively is only part of the equation. The best graphic in the world is wasted if no one looks at it.

Let’s go back to the idea of storytelling.

What makes a story? Dave Trott, in one of his excellent blog posts, quotes Steven Pressfield’s simple version. A story consists of Hook, Build, and Payoff.

If we apply that to data storytelling I think it makes it easier for us to choose our place in the triangle.

• Hook: we need to capture the attention of our audience, with something relevant and/or fascinating. This is where McCandless excels.
• Build: there should be enough depth to reward engagement with the data.
• Payoff: there’s got to be a reason for looking. What am I going to do differently as a result of spending time with this data?