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Better Data Visualization Starts With Simplifying the Message

Better Data Visualization Starts With Simplifying the Message

Earlier this year we discussed the importance and role of data visualization. Data is the proverbial lifeblood of decision making in most organizations—whether its managing performance, identifying improvement opportunities, or driving strategic directions. Organizations rely on data visualizations to tell a story and help digital audiences make use of the data. But, despite widespread tools and an array of visualization techniques, digital barriers significantly limit the impact of data.   

I recently talked to Dr. Antonio Moneo, change management & advanced analytics senior manager at BBVA on the challenges of data visualization and how to address data overload and think beyond traditional visualization to drive action and decisions.  

What makes good data visualization difficult?

There are three main reasons why data visualizations are complex: 

Start with data availability. Good data for visualization is not always easy to find. Even when you find it, you still need data to be in an interoperable format, properly documented and licensed for reuse and ready to be used. Even more so if you are planning to create a visualization in real time. So, there's a data management element behind it and several things need to happen before the data is available to visualize. 

The design of the visualization is a second challenge. Not everyone can do visualizations right. It requires applying design and understanding the core elements of the design into a piece that is going to be informative and have a lot of uses. Design comes with user experience (UX) requirements and additional consideration around the technology incorporated in our browsers and digital tools.

So, not only do we have the skills limitation, but then we need to deploy these visualizations in a specific technological context. It's not the same when you're deploying a visualization on a laptop or a mobile phone or tablet. Different displays require different types of design. Even in large organizations, you don't always have those capabilities. Which is how you end up having data engineers or data scientists creating visualizations, even though that's not their core skill.

The final challenges relate to data accessibility and literacy, or how users access and understand the data visualization. 

Accessibility is a key aspect, because there are still many without access to the online space where visualizations exist. And there are many things that we forget in terms of accessibility, for example, users with color-blindness or other types of visual impairment. We need to design for all.

Even when we overcome the accessibility challenge, we need to make sure that users understand what’s presented. In many ways data visualizations have become too complex: there are too many charts, interactions, or elements in the visualization. We need to simplify the message.

What does embedded storytelling in data visualization mean to you?

For me, embedded storytelling also goes back to the tangible data project—because I am using the physical context to add meaning to the data. I was inspired by the example of the transportation agency here in Spain. Years ago, they started including markers on the roads indicating how many people died in that spot last year. So, in the flow of an activity—in this case driving—you realize, “This stretch of road is dangerous for me.” You get an immediate message while driving and react by changing behavior.

In the case of the Tangible Data project, I think about inserting data sculptures in the context where they can add more value, for example, representing the evolution of trust in a government and place it in front of an institutional building.

Changing behavior is what embedded storytelling is about. How can we use data to change a behavior? And the answer is providing more context, rather than making it prettier. It's about making the data more understandable or relatable. We don’t need to add text or complexity. Instead, place the data in a more powerful context.

One final element that attracts my attention is the human dimension. How can we compare the data with our own experiences and dimension? Leverage elements of our selves provides context and reinforces that storytelling.

What are the benefits of physical representations of data in 3D?

I believe it breaks the monotony and creates momentum, because people pay attention when you pull out a three-dimensional object. It also relies on the idea of simplicity. When I create 3D pieces I’m focusing attention on one element, one object. Something that people can touch, see, pass around, and interpret.

Check out the full interview Tangible Data: The Next Frontier in Data Visualization

For more process and performance management research and insights, follow me on twitter at @hlykehogland or connect with me on LinkedIn.