If we asked all firms and industry professionals reading this right now to mention all the buzzwords they faced – and had to work with this year – the list would look almost identical. Between customer insights/ experience, consolidation and data, we might have it all already.
Speaking of data, this year saw a lot of good and bad press for it alone. As it is often said, though, no press is bad press. Data made something important of itself too, making sure it was not just a figurehead in the market.
However, it is impressive to note that we are nowhere near mining full potential of data yet. Discussing the full spectrum of data visualization and data storytelling, and how they can be combined for explosive effects, shows just that.
What is Data Visualization?
As the name implies, data visualization is the translation of obtained data into graphical resources which helps for better, more intense understanding and analytics of the data in question. It follows the application of elements such as graphs, charts, and maps to present other forms of data as cleanly as possible, ensuring a better representation for the intended audience.
While it may seem like just another excuse to bring art into data, data visualization is more important than what we give it credit for. With an MIT study confirming that 90% of all information captured by the brain is visual – and 50% of the brain is active in the art of visual processing – the importance of this process cannot be undermined.
What is Data Storytelling?
It is often said that a picture (as is it in the case of data storytelling) speaks a thousand words. For it to be likened to words means they are equally as important, and that is where data storytelling cuts.
Simply put, data storytelling involves the creative use of words in such a way that allows concepts and ideas around data to be brought into layman’s terms. In other words, data storytelling is the process of converting complex data and insights into simpler words and free-flowing text which can be better understood by a less analytical mind, making it more suitable for general consumption.
When looking at that definition above, it is easy to believe that data storytelling does not take as much effort as visualization, where graphics concepts and appropriate elements are used. That could not be farther from the truth.
Data storytelling is not just based on knowing what data to convey for the right purposes but requires a deeper understanding of the human language and interaction profile too. That is the only way the story being told can be expected to make the right impact as intended.
The Intersect – And Why You Should Care
When explaining data visualization, we made it clear that the brain captures information in a visual form about 90% of the time. While that is true, this piece of singular statistic does not tell the full story.
The full story is begged from the following question:
Of the 90% of data captured in visual form, how much does the brain understand?
Data is great when it comes to charts and graphs, but not everyone has the technical mind to mine out what they need from that field of provided data. The fact that data looks comprehensive on paper does not mean it makes any sense where it matters. By extension, a data campaign is a total failure if it cannot achieve its intended purpose.
That said, you need a way to ensure that said data is communicated and conveyed properly to the right sources. This is where data storytelling takes over the narrative from data visualization. Again, that is why you should care about making both a priority for your brand. This knowledge alone is far from enough, though.
Every good story tends to create images in the minds of the reader. In such stories, the author enjoys being able to create a scene that can be replicated in multiple ways when it gets to the reader’s mind.
In data storytelling merged with visualization, though, the publisher doesn’t enjoy such luxuries.
They now have to take charge of everything – from the quality of content to the exact images formed in the minds of readers – which is harder considering how technical it would be to find the single image all readers will agree upon.
Now that you know how well both can work for you, how do you go about it? Do you just take all your data and publish them in words? Do you keep some in charts and use words for the others? How do you merge both concepts to create a singularly flowing body of work which agrees with itself?
Implementing Visual Storytelling for Data
The challenge here isn’t that you might not be good with either of the two concepts we have expressed so far. Your firm could be the Shakespeare of storytelling or the Picasso of visualization, but how do you create the much-needed balance?
With the right steps, this still won’t be a walk in the park, but it would be a most rewarding journey for your consumer and client prospects.
1. Audience Research
Every company worth its salt has done some customer research and market targeting to know who its products and/ or services are for. This, however, does not hold much water when it comes to the audience you are creating the visual story for.
The reason for that is not far-fetched.
Research brings back a lot of data, even more than what was intended in the first place. At the same time, not all of your audience is interested in the same things. Thus, you would have to decide what data is more important than the others, and why.
Yes, why, because justification is all you need to ensure you are not just creating a story for the kind of data you like, but one your audience is truly invested and interested in.
2. Aggregate Data
Once you know the most important pieces of data, it is time to aggregate all related data for a more comprehensive feel.
This is more than just collecting the data which falls into your spec range, but categorizing different forms of data into the same group so that they can either be expressed as one – or an extension of the same set.
By so doing, you are not only sharing valuable insights but creating a thought-leadership approach which is most needed at this stage.
3. Determine Purpose
Data storytelling and visualization are independently about purpose, so it should not be lost here either.
Why are you sharing that piece of data? Will you just like to inform the audience, describe a situation, improve on a perspective – or any other thing?
This purpose goes on to inform your choice of words, tone of delivery and style of approach. It extends into your application of language too, creating the wholesome effect you desire.
4. Plan Approach
This is the final and most important step.
You know who the audience is, so how do you present the data to them? Now that you know what they want, how do you make it more appealing?
How would the body of text go in line with the graphical elements? Will they just be a better interpretation of what has been explained as text, act as a standalone in furtherance of the text, or both?
What goes into the graphics? Is it just the numbers, or some other hard facts too?
Decide on all of these, and you will have a better and clear approach to making that visual storytelling happen.
No matter what you do, always ensure your visualization-storytelling is kept in balance, never tending towards any of both while still making sure that they are both adequately represented.
Done right, you will have appealed to the greater sense of those who prefer either or both while also cultivating them for the kind of data representation they liked lesser. Little wonder why it is marketing gold right now, isn’t it?