It’s no secret that data visualization is hot right now. And it should be…from the public sector to nonprofits and private enterprise, data and how it is used is remaking our world. Perhaps nowhere is data more popular than data visualization, where the design-minded dedicate themselves to charting just about anything. All of which begs the inevitable question, is data visualization a passing fad, geared to dress up otherwise unappealing information? Or does it underscore our deeper need for people to simplify information, organize it and make it more accessible so that we can increase our understanding of the world around us?
Put me the latter camp. Data visualization and data design are about the age-old search to effectively balance form and function. Form (in this case, how our data is designed) challenges us to make information more visual and interesting to those who would not otherwise find it so. And that, in turn, increases understanding. Function (in this case, how that data can be put to use) demands that we make the information we design meaningful and useful. That’s the beauty of design—it serves both form and function. And successfully designing data visualizations that delivers relevant, meaningful, and useful experiences with dense information requires balancing our design choices between these two priorities.
At Constructive, we’ve been fortunate to work with some really amazing organizations doing ambitious things with data—from measuring global environmental performance and exploring the impact of climate change on economic output to tracking the global response to tuberculosis. Based on these and other experiences I wanted to hopefully demystify what can be a daunting exercise by sharing some of the things we’ve learned over the years as we continue on our never-ending quest to improve our process for designing with data for clients.
Start by Asking One Fundamental Question
Every data visualization challenge is different. Some are simple, geometric abstractions like pie charts, scatter diagrams or area charts that help clarify phenomena from a larger data set. Other visualizations such as narrative infographics are less about a purely objective look at the data, taking an illustrative approach to promote greater understanding of a complex process or system. Whatever the case may be, it’s helpful to start starting with one simple question: “Why is this information meaningful to the audience?” Because ultimately, it’s about meeting the audience where they are. As communicators and designers, we need to take the complexity of the numbers, process or ecosystem and distill it into something engaging and meaningful for the audience.
Constructive’s 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, the key is to be not just willing, but eager to dig into the numbers and make sense of them. To embrace their complexity so that we can work with our clients to find effective ways to present them so that they can use their data to increase understanding, strengthen their brand narrative, and ultimately motivate audiences to action.
Once we’ve started with this simple question and used it to contextualize our thinking and inform our decisions, here’s how we tailor our design process to create effective communications and experiences with data.
Step 1: Design Research
As with all research, effective design research requires that we ask the right questions. So, start by taking a step back from the data itself and discuss the project with stakeholders to identify broader goals for the work. What information are we trying to communicate? How does it fit into achieving broader organizational goals? Who are our audiences and what are their levels of expertise with our subject matter? What’s their level of data literacy and visual literacy?
Once you have answers to top-level strategic questions like these you can dig deeper into researching to find answers to data-specific questions: Is our data currently organized in a way that’s easy to understand? What are the top-level takeaways we need to extract and elevate? What are the different types of visualizations we can use to to communicate our ideas? How is the data structured, and how flexibly can we apply it? The goal of this research is to immerse ourselves—to understand our organizational goals and our audience, absorb everything we can, and then approach designing solutions in ways that align the interests of the brand and the audience.
Step 2: Design Strategy
Now we’re ready to focus on developing a design strategy that follows through on our research. First, start by creating a communication strategy: define the purpose of the work based on our research and what factors will go into designing and delivering a meaningful experience? Where will our audience be? Will they have time to do a deep dive into our data or is it more of a high-level quick take? Develop a content strategy: how should our information be organized or edited to make it more accessible and understandable for our audience? Can we simplify ideas and concepts without compromising the integrity of our data? And finally, develop a design strategy: techniques can we use to create a more engaging presentation? How does the design of our brand impact design execution? And if it’s an interactive visualization, how do we want audiences to interact with out data? And don’t forget technology strategy: What systems are used to produce or store the data? What are the right digital tools to deliver them? The goal is to think holistically across multiple strategies that all influence how our data will be experienced and what that means for both our audience and our brand—taking each into account and finding the right mix to effectively balance them.
Step 3: Design 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. We have a sense of what design approaches are likely to work best. It’s finally time to start iterating on design concepts that make our ideas tangible! At Constructive, we prefer rapid prototyping to get as many ideas out there quickly—because especially when designing and building with data, the costs of going down the wrong path can be significant. For us, this means starting with pencil sketches to quickly get our ideas out so they can be discussed. For projects with more complex data visualizations or data tools, we usually create information architecture and wireframes to that provide a greater level of detail and interaction. Once we’ve got our structure, hierarchy, and functionality in place, we’re ready to jump into look and feel, adding visual style by choosing the appropriate color, fonts, 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.
Step 4: Design Execution
As they say, without proper execution, even the best strategy is useless. So, making sure you have the right team in place to execute the project is critical—particularly for interactive visualizations that require integration with outside data systems and complex development. Execution for data design projects follows the same approach that all design work takes: design, refine, produce. In the case of designing data visualizations, it’s incredibly important to emphasize content accuracy and legibility—especially because data design can require communicating complex information in tight spaces or with a lot of competing elements. We should also design for flexibility to extend our work across other parts of our communications and brand, so plan ahead and consider how visualizations might be exported or adjusted to be used in other mediums. And be sure you test your work! If we’re working in print, reproduce it in as close to the final format as possible. And if it’s interactive, make sure you allocate extra time to the QA process if there’s much interactivity with your data.
Wrapping it Up
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.
