Data visualization is in rude health in 2017. The public’s appetite for insight is growing, recognition of its power is becoming more widespread, and more and more of us are tuning into how versatile the medium can be.

But something else is changing too; audiences are growing savvier and more clued-up. Time is up for unscrupulous visualizers who try to trick or dupe the public; instead, truth, honesty and transparency are the orders of the day. Fail to achieve this, and your visual will not go viral.

Read on for our top tips for creating viral data visualizations in 2017, and for engaging effectively with an intelligent audience.

First things first, begin with the data

The data must always be the starting point for your visualization. If you get ahead of yourself and start mapping out weird and wonderful shapes, configurations and formats, before you have analyzed and understood the data, it is basically game over.

Have the data lead you every step of the way, informing both the aesthetic and the informative aspects of your work. Remember that you are visualizing insight from the data; you are not creating your own insight.

Define your progression

What do you want your audience to take from your work? What is the key insight you want your audience to share? In some cases, the answer is obvious. If your data is progressing over time, then visualize this. In other cases, it is trickier; for example, in Shirley Wu’s Hamilton visual she is emphasizing connection rather than linear progression.

You need to decide on this early on. Integrate the progression (or connection) into your planning sessions and make sure that your viral visualization is geared towards emphasizing this.

Have the format support the insight

The aesthetics of the visual must support the insight you are delivering. They must not mask or oppose it in any way. We see this with alarming regularity; a great bit of insight is destroyed by some heavy-handed visuals which dulls the impact of the piece and leaves the audience confused. Make sure that your format is supporting the information you provide.

Less is generally more

Similar to the above, when it comes to presentation, less is usually more. For example, if you want to plot multiple lines on a graph, go ahead by all means, but make sure that those lines can be easily disentangled from each other. If you have many different parameters, consider allowing the audience to drill down into those parameters, accessing a dedicated screen for each graph.

The ‘model’ model

Princeton University’s Ocean Salinity visual is a prime example of what happens when a data visualization becomes more than just a presentation of existing facts. The creators of this visualization used high quality data to create a progressively unfolding model of ocean salinity levels. This enables scientists not only to understand the facts as they are at present, but also to understand how they will develop moving forward. If you can acquire data of this quality, this is how to create outstanding visuals.

Don’t be afraid to collect more data

Which leads us handily onto our next point. There is no such thing as too much data – just as there is no such thing as too much insight – so keep collecting it. If you discover more data pertaining to the visual you want to create, if you need to revise an earlier point, or if you simply want to update an older visual, collect the data and do it. This is part of the adventure of data visualization; the data is taking you on a ride you won’t forget in a hurry.

Don’t be afraid to start again

The flipside of the above is that, sometimes, you might find yourself high-tailing it back to the drawing board. Don’t be afraid of this. It may be a little frustrating, and perhaps you feel that you have wasted your time, but still, embrace it. Data visualization is a science, not an art. You can’t manipulate the data to suit an insight which is not there, and if you feel yourself needing to do this, simply start again. Don’t try to blag it; your audience will see straight through this.

Learn from the best

Just like in any discipline, we learn our skills by studying and researching the best in the business. A budding novelist might pore over Phillip Roth, Tolstoy or Jane Austen; a trainee filmmaker might analyze how the likes of Spielberg and Scorsese achieve a certain effect; but what about us? Who are the leading lights of the data vis scene? Who is achieving virality time and time again?

Let me tell you; they are all around you. Subscribe to data visualization blogs, set yourself up with some Google News alerts, and learn from the most exciting visualizers currently in business.

Get the right tools

We cannot visualize well without the correct tools at our disposal. Fortunately, there are a vast array of tools just waiting for you to put them to good use within your visualization work, and many of these tools are free to try!

Some of the most impressive viral visualizations of recent years have been created not by the big media outlets or data science departments at universities, but by ordinary people using platforms like Tableau. I recently published a list of some of the best tools for data visualizers of all levels and in all fields; browse the list and start to develop your own data visualization tool box.

Keep at it

It might seem obvious, but Rome was not built in a day. Creating great, insightful, influential and far-reaching visuals takes skill and technique; two things which can only be built up over time. With this in mind, keep focussing on improvement and keep analyzing how your next visual can be better than those which went before it. Practice makes perfect, and perfection has to be your goal.

Hopefully these tips have inspired you to create something truly stunning, and something which stands a great chance of achieving the viral exposure it deserves. I’m looking forward to including your visual on my Best of 2017 end of year round-up!

(Visited 375 times, 1 visits today)