Data Visualization Design Process: A Primer

It's no secret that data visualization is hot right now. How hot? Just check out the latest research from MSDS.

 
In all seriousness, data is hot. And it should be…from the public sector to non-profits and private enterprise, data is remaking our world. This once hallowed refuge for geeks, nerds and accountants has become a genuine hotspot. And perhaps nowhere is data hotter than on the fevered pitch of data visualization, where design-minded professionals and amateurs alike dedicate themselves to charting anything that isn't nailed to a spreadsheet. All of which begs the inevitable question, is data visualization just a trend? A passing fad, geared to dress up otherwise unappealing information? Or does it underscore our deeper need to simplify information, organize it and make it more accessible? 
 
This tension between form and function isn't likely to be resolved anytime soon—nor should it. Form pushes us to make information more visual and interesting to those who would not otherwise find it so. Function demands we make information more meaningful and useful. And therein lies design's promise. Design serves both form and function. Designing data visualization that delivers more relevant, more meaningful, more effective solutions requires balancing our design choices between these two priorities.
 
We've spent years designing data visualization, and so in the spirit of evolution, I wanted to hopefully demystify what can be a daunting exercise by sharing some of the things we've learned in this time refining our process for designing with data for clients.
 

Start By Asking One Fundamental Question

It's important first to not lump all data visualizations into one undifferentiated heap. Some are simple, geometric abstractions like pie charts, scatter diagrams or area charts that help clarify phenomena from a data set. Others, such as infographics can take a more iconic, or illustrative approach in order to promote greater understanding of a complex process or system. What is important is to approach each one with an eye towards answering one simple question: "Why is this information meaningful to the audience?" You've got to take the complexity of the numbers, process or ecosystem and boil it down to this one essential idea. Everything else is secondary. 

Our process for designing with data is rooted in the strategic approach we take to designing for any medium or content, which like many design processes, consists of four phases: Research, Strategy, Exploration, and Execution. For data design, we simply take it one step further by digging into the numbers, making sense of them, then find ways to present them that speed understanding, strengthen brand narratives, and ultimately motivate audiences to action.
 

Step 1: Research

Effective design research, like any other, means asking the right questions. Start with the basics: discuss the project with the client, identify the goals for the project and how the project fits into their broader organizational goals. Digging deeper: What information are we trying to communicate? Is it organized in a way that's easy to understand? What are the top-level takeaways, and how might we translate them into a visualization? Who are the core audiences and what do they already know about the subject matter? What do they read, what's their graphic literacy, and how sophisticated should the visualizations be? How is the data structured, and how flexibly can we apply it? The goal is to immerse ourselves, absorb everything we can, then approach solving the problem to unite the interests of the brand and the audience.
  • Discuss project
  • Identify strategic goals
  • Read everything from the client
  • Perform independent research
  • Understand the audience

Step 2: Strategy

Once you've absorbed everything we can about the information and the audience, it's time to focus to developing the strategy. Start with communication strategy: what's the most effective means of delivering the experience? Where will the audience be? Will they have time to do a deep dive, or is it more of a quick take at a high level? Then, content strategy: how can the information be edited and organized to deliver a more salient message? How can we simplify ideas and concepts to their essential truths? And finally, design strategy: what techniques can we use to create a more engaging presentation? What are the brand considerations in design execution? If it's an interactive visualization, which of the countless tools out there is the best one for the job? These strategies must often be reconciled, and an effective approach takes them all into account, then strikes a balance between them.
  • Content strategy: clarify the narrative, create hierarchy
  • Design strategy: address presentation, structure, technical issues 
  • Communication strategy: what is the medium, how is the message delivered?

Step 3: Exploration

So we've got a good idea of what the issues and ideas are, why they're important, and how they're going to be used. It's time to get our hands dirty and come up with design concepts! We prefer rapid prototyping to get as many ideas out there quickly, so this means starting with some pencil sketches to flesh out ideas. When visualizations or the project in which they will live are more complex, we may turn to full-blown information architecture and wireframes to develop more refined concepts. Now we're ready to jump into look and feel, adding stylistic elements such as fonts, colors, iconography, etc. The key here is to work from the outside in; start with basic, high-level concepting, collaborate to develop the structural elements, then gradually hone in on the details. 
  • Sketch out broad concepts
  • Develop an information architecture / wireframe to establish structure
  • Create a design mockup
  • Refine as needed

Step 4: Execution

As they say, without proper execution, even the best strategy is useless. Making sure you have the right team in place to execute the project is critical, particularly for interactive visualizations that may require integration with outside data systems or custom development. Execution follows the same tried-and-tested approach that much design development takes: design it, refine it, repeat, focusing heavily on design intent, content accuracy, and legibility. Then, be sure you test it: if we're working in print, reproduce it in as close to the final format as possible, and if it's interactive, employ a standard QA process. We should also design for flexibility to extend our work across other parts of a client's brand, so plan ahead and consider how visualizations might be exported or adjusted to be used in other mediums. 
  • Production design for final comp
  • Prep for print and/or web
  • Quality assurance and testing
  • Refine as needed
So that's our approach to designing data visualizations, and we've found it consistently yields the best results for our clients and ourselves. Of course, there are a number of software applications that promise to make producing data visualizations easy, from Excel to Google Charts to Tableau and beyond. And by and large, these are excellent tools. But if there's any one takeaway I'd like to leave you with, it would be this: great data visualizations don't make themselves. Short-cuts usually just short-change your results. So if creating data visualizations with impact is your highest priority, whether you're doing the work yourself or partnering with someone do it for you, you would do well to commit to a strategic design process.

 

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Posted by Ryan Reynolds

September 26, 2012

Categories: Branding, Client Engagement, Communications, Data Visualization, Design, Process