The need for technologists who can bring data to life via visualizations is growing. And no wonder: At most enterprises, data has become embedded in the decision-making process. Some estimates say the number of organizations that use data in developing their strategic plans is rising by 30 percent a year.

It follows, then, that executives, data experts and technology leaders all believe that businesses are searching for technologists who not only know how to manipulate and process data, but how to present it, as well. 

Specifically, people who can make complex data easier to digest are in demand. They accomplish this by presenting data visually, as opposed to dry tables of numbers and terms. However, creating data visualizations involves more than simply building charts and graphs, said Ben Yurchak, president of KnowClick, an analytics firm in Bryn Mawr, Pa.: “It’s also shorthand. It’s more about how data can be represented in a way that gets the meaning across to people pretty quickly.”

Most professionals in the field call this “telling a story.” Anand Chitale, director of product management at solutions provider SAS in Cary, N.C., describes data visualization as “a pictorial representation [of data points] with a common purpose of telling a story and conveying a message to its consumers.” Those messages, he explained, could include insights, topics that need attention, or simple answers to business questions.

Yurchak compares data and data visualization to MS-DOS and Microsoft Windows. “With Windows, you can see a point and click on it,” he said. “With DOS, you have to really know the programming language.” Use and visualization can be looked on the same way, he said. Visualization is about getting to the point where “when we show data it’s easy to understand, get context from, and use to determine implications and actions we need to take.”

Less Tech, More Communications

Before you consider pursuing opportunities in data visualization, it’s important to understand that many of the field’s challenges go beyond the technical. First and foremost, success requires communication skills and the ability to stay on-message. 

To Chitale, the most important aspect of data visualization is understanding the target audience. He recalls watching designers “getting side-tracked by the visual fluff of output” rather than prioritizing the output’s effectiveness in conveying information. 

Those who possess a clear view of the target audience are more efficient in designing solutions, no matter what tool they’re using, Chitale said. They’re better able to choose the right graphical approach as they consider features such as color, size, shape, animation and interactivity: “The challenge is less with the tools and technology and more with the mismatch between the use of visualization methods… and the needs of the target audience.”

Knowledge of analytics and how they’re used is likewise important. It’s not just about understanding the intricacies of, say, user flow or online analytical processing (“OLAP” for short). You must also recognize how people put data to use. When examining the same dataset to answer similar questions, Chitale said, executives, business analysts and data scientists require different levels of detail and will follow different paths through the information. Thus, each audience requires a tailored approach to visualization. 

“There are many tools out there which show how to build ‘cool’ visualizations,” said Kathleen Brunner, president and CEO of Acumen Analytics in Blue Bell, Pa. “However, the aim of data visualization is always to answer questions with relevant information.” 

Interface design is another critical aspect of data visualization. Again, the reason is clarity—making the data useful. That, said Chitale, “is more important than the tool at hand that will produce the visual.”

At the same time, focusing on design without context (how the data applies to the organization, the industry and the audience, for example) can have adverse consequences. Warned Chitale: “Purely focusing on the visual fluff will lead to incorrect information conveyed to hundreds and thousands of users, which can have a critical impact on the business.” 

Visualizations Need Technical Knowledge… and Soft Skills

Of course, a good mix of technical and business skills are necessary for any technologist approaching visualization. In addition to mathematics, problem-solving and art being useful, Brunner suggests domain expertise—in marketing, human resources, life sciences, sports and other verticals—is also important. 

“Not knowing the organization’s function and its data could lead to increased work efforts and unknowingly introducing bias in the analysis presented with visuals,” added Chitale. “Data visualization professionals should work very closely with all parts of the business, learning and sharing best practices across the organization and measuring the impact of data visualization and its effectiveness with business users.”

Though some might say that data management isn’t the same thing as data visualization, Brunner suggests that everyone on the data team must take care to ensure the dataset being used has been vetted. Data quality is a big challenge during the process of creating visualizations, she said.  “The first step is to clean the data.” That will ensure a clean, effective visual.