The next big thing in ‘big data’ jobs: Dataviz

May 2, 2014, 1:00 PM UTC

FORTUNE — Dear Annie: I’m graduating at the end of this month with a major in marketing, and I’ll be starting a great new job as a marketing and brand-management trainee at a big consumer-goods company. This is exciting, but I’m concerned about one thing. My future boss and some other people from the company took me and some other new hires out to lunch yesterday, and they kept talking about how we’re going to be using more and more dataviz, in presentations to people in other parts of the company, including division management, later this year and next year. I Googled dataviz, so I have some idea what it is, but I really feel like I need to find practical ways to use it in my job, or at least understand how other people are using it. Do you or your readers have any suggestions? — Cincinnati Kid

Dear C.K.: Sure. As a first step, get yourself a copy of a new book called The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions. In it, tech consultant and big data guru Phil Simon tells in detail how early adopters like eBay and Autodesk are using dataviz to help them make sense of vast, amorphous amounts of data — and to present the numbers in ways that are clear, colorful, and interactive.

We’d all be smart to get up to speed on this as soon as we can, according to Simon. As companies come to rely more and more on data-driven decisions, “numeracy will be as crucial as literacy for anyone who wants to stay employable,” he says. “If you really hate thinking about numbers and interpreting what they mean for your business, it will get harder and harder to hide.”

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Although the field of quantitative analytics is still in its infancy, the real stars at many companies are already “hybrid employees,” he says, who can use data to make better decisions in their current jobs (marketing, for instance) and who can translate the figures into graphics other people can get excited about. “Pie charts and bar graphs are too limiting, and they’re not interactive,” he notes. “In dataviz, interactivity is key.” He offers these tips on how to get started:

Learn a new tool or two. “It’s folly to think that Excel is the only means of representing data,” Simon says, noting that plenty of powerful dataviz software is available for free, including D3.js. “That’s popular, but it takes some tech chops, so you’re probably better off starting with Google Docs,” Simon says. “Datawrapper is another neat, free, and open-source tool for visualizing data.” He recommends experimenting with a couple of these until you’re comfortable with how they work and what they can do.

Brush off that dusty statistics book. With data rapidly becoming the lingua franca of business, “you might not have to create chi-square distributions as part of your marketing job, but a basic understanding of probability and statistics certainly won’t hurt,” Simon says. “For example, know what normal distribution is, and the difference between Type I and Type II errors.”

Play around with numbers. Practice looking at huge sets of figures and extracting a meaning from them that you can then represent visually, Simon suggests: “If you have access to an ERP [enterprise resource planning] solution in your company, or human resources data, or customer data, take a look. An amazing variety of data get published online by government agencies and others” — including sports teams. If you like basketball, check out Vorped for examples of how players’ stats stack up when translated into a data-visualization.

Learn a little about design. As a marketing maven, you no doubt already know many of the basics, like how different colors evoke specific responses, as do “subtle elements like fonts and spacing,” Simon notes. “Design is a sexy topic these days, especially in dataviz, where it’s essential to making information accessible.” He adds that “being able to speak intelligently about design, and showing that you know how to use it, puts you at an advantage over ‘pure quants.’”

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Know your audience. Once you’ve become adept at presenting figures in visual terms, you’ll find that “not everyone in an organization wants to accept what the data are saying, especially if they don’t like numbers or resist basing decisions on them,” Simon observes. “So you have to know how to tell a story, and how to ‘sell’ what your dataviz is showing.”

Consider your audience, too. “For a group of tech people or marketing colleagues, you can usually go into a lot more detail, because they are probably interested in exactly how you arrived at your conclusions,” Simon says. If you’re speaking to senior management, however, he recommends a different approach: “Have the supporting detail available, but don’t go into it unless you’re asked, or you’ll bore the hell out of a lot of high-level people.” Noted.

Talkback: If you’ve used dataviz in your job, beyond the traditional pie charts and spreadsheets, what helped you get conversant in it? Leave a comment below.