I’d like to tell you a story about a friend of mine. His name is Jim. Jim is the head of a marketing team who have recently revamped their social media marketing initiatives. With Jim at the helm, the team smashed their projections and made great progress for the company in this field.

Now, with the inaugural period nearing completion and the project an unmitigated success, Jim needs to present the report to the board. Naturally, he’s excited, and he wants a striking set of infographics to really drive the message home. He spends time creating lush visuals with lots of shapes, charts and flashy colors. He even projects some of the data onto the outline of the Twitter logo to truly underline his message.

But, in the boardroom, he is met with silence and confused looks. The data he is demonstrating is all present and correct – the numbers are there and they make for great reading – but his message has become lost. The stark aesthetics and explosions of color have drowned out the data.

I’d also like to introduce another friend of mine; her name is Sophie. Sophie’s company have just launched a new software product with stunning results, and now she wants to create a piece of content to speak to the customers who are still on the fence.

She pores over the data, but she’s having trouble deciding what to leave out. It all seems too important, so she includes it all. However, weeks after the content launch, take-up rates have not increased. Then Sophie sees the comments left on the content article – “too confusing,” they say, “worthless”.

The stories of Jim and Sophie highlight the balancing act that we must perform each time we create a visual. Visualizations need great aesthetics, but also great insight. So what is the perfect ratio?

The Case for Aesthetics

We cannot simply say that Jim is wrong. He has shown that he understands the fundamental concept of data visualization; that the graphics presented must provide more than just a simple outline of the data. They must be striking and thought-provoking, and they must build a narrative which cannot be developed using raw data alone.

In this sense, aesthetics are not superficial; they are a key component of data visualization and play a major role in making great visualizations so successful. If we are presenting findings to a board or to investors, we need to make the right numbers stand out; if we are delivering content to leads and other users, we need to act quickly to secure their attention and custom.

We have looked at the right ways to present certain kinds of data, and aesthetics play a big part in this too. The colors we use, the configuration of the data we employ, the format of the visualizations we utilize; all of these are vital to the overall effect of the data we are presenting, underlining how important aesthetics are.

To put it simply; a great piece of data visualization must be a work of art in its own right. Without such attention to the visual effect, we might as well just revert to a table of figures with some text annotations.

The Case for Insight

But – by the same token – we cannot accuse Sophie of being wrong either. Sophie has recognized that, at the heart of any great visualization, there must be an exemplary source of data, something which can truly illustrate and illuminate a concept.

Perhaps if Sophie is guilty of one thing it is of indecision. When creating visualizations, we use the raw data as support and as a back-up to the visuals we are creating, but we must understand what to leave out and what to leave in. Make the raw data available to content users and other viewers by all means; but the visualizations themselves should be sharp, neat and tidy.

Of course, this is still a data-centric view. It is still the data that is speaking to the viewer, creating the narrative and proving the point, and not the aesthetics. Without a clear understanding of the data, and of the insight to be drawn from it, there can be no visualization. The same cannot be said about aesthetics; we do not need to hold Masters Degrees in Fine Art to produce effective data visualizations.

If we pursue this point, we see that data is highest in the visualization hierarchy, followed by the aesthetics. But is this necessarily true? How can we balance these two conflicting sides of visualization in an ideal ratio?

The Balanced Approach

The key is to remove the idea of a conflict altogether. When we create data visualizations, there should be no trade off between the visually stunning and the factually deep. There need be no compromise made in which portions of one aspect are removed in favor of the other.

Instead, the two aspects should become inextricably linked. There is no ratio to be achieved here – no mathematical formula which we can apply to visualization; instead we need to examine the data at hand and decide upon how to do it justice in visual form.

In this sense, we understand that the data is the most important feature of a visualization. The data should be the first thing that we consider when we approach the project, and this must inform the aesthetics. What narrative are we trying to build here? What do we want the content user to take from the piece? What is the overriding tone of the conclusions to be drawn, and how can this be represented?

These questions will lead you as you decide upon the best course of action for the data you are working with, creating visuals which are not only striking but which add nuance and character to the facts and figures.

Some visualization may be light on the ‘special effects’ and heavy on narrative, while others might use graphic cues to underline the main points. Whichever methods you choose, remember that there is no magic number to hit when it comes to visualization, simply make sure that the visuals are extensions of the data you have at hand.

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Image via Pixabay.

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