This post was originally published on the Urban Institute’s blog, Urban Wire on April 15, 2015.

You may have noticed that over the past couple of years the Urban Institute has accelerated its efforts to effectively and strategically visualize data and visually communicate research to a broader audience. Yesterday, we launched our brand new website, designed to more effectively communicate Urban’s research to a wider audience. A few weeks ago, we published “Nine Charts about Wealth Inequality in America,” which visually showed patterns in income inequality, earnings gaps, homeownership rates, and the like.

These products did not come about by hiring an external firm or asking existing research staff to learn new technologies and tools. Instead, we have made it a strategic goal to improve how we communicate our research. To do so, we have built out a strong, flexible digital team who work alongside writers, editors, and media relations teams to share Urban’s research. That collection of skillsets can make organizations more flexible to the changing demands of their audience or marketplace and, ultimately, more successful.

I’ve seen this team-building search most directly when I’m asked what tool or skill someone should learn to create better, more effective visualizations: “Should I learn R? What about JavaScript? Or maybe Tableau? Maybe I should just get better at Excel?” Unfortunately, I usually have to tell them that I just don’t know: I don’t know their existing skillset or their audience, and I’m not familiar with how they transition from working with data to visualizing data and publishing their analysis. And, importantly, I’m not familiar their organization’s, workflow.

One reason people ask me what tool or skill they should learn is because they want to be “the” person their organization asks to create great data visualization products. But creating a great data visualization is not just about knowing how to code in JavaScript, or choosing good colors or layout, or even knowing how to work with data: it’s all three. And people who are great at all three of those skills—programming, design, and statistics—just don’t exist. I call them the Unicorns.

unicorn in a venn diagramAt Urban, we have about 350 researchers and analysts, so we’re pretty set on the statistics part of this diagram. Over the past few years, we have grown our team of experts in design, programming, data visualization, communications, and dissemination. That team works closely with the researchers and analysts so that together, as an organization, we are the Unicorn.

What’s really interesting about the Unicorn Phenomenon—that is, the search for someone who can do everything and be everything—is that it’s not really new. (And, with the importance and value on data and data visualization today, it doesn’t seem too far of a stretch to call it a phenomenon, does it?) In The Idea Factory: Bell Labs and the Great Age of American Innovation,which tracks the lifecycle of AT&T’s Bell Labs in the 1940s and 50s, Jon Gertner writes, “An effective solid-state group, for example, required researchers with material processing skills, chemical skills, electrical measurement skills, theoretical physics skills, and so forth. It was exceedingly unlikely to find all those talents in a single person.” In other words, the Unicorn Phenomenon is not recent, it’s just different.

The solution to an organization’s growing analytic, visualization, and communication needs is not to look for one person to accomplish these tasks, but to build teams of people who, together, can be the Unicorn. They need people who can code in modern programming languages, people who can understand data, and people who can design, write, and communicate. Admittedly, this is a heavy and complex lift, but it’s wise to recognize that the center of an organization’s complexity should be people, not technology.

What’s more, these teams need to be closely tied with content specialists and decisionmakers. In their recent article on creating a data culture, DJ Patil, recently appointed the White House’s chief data scientist, and Hilary Mason, founder of Fast Forward Labs, note that, “If the data scientists are isolated in a group that has no real contact with the decision makers, your organization’s leadership will suffer from a lack of context and expertise.” And it’s broader than that. As an organization, create the Unicorn as a collection of people and then empower those teams to work with and communicate data and analytics to decisionmakers. This can help improve your internal communication and workflow and help you reach your external audience.