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Data journalism: A primer

For the uninitiated, data journalism can feel like "someone else's job" and getting started can be awfully intimidating. But the truth is using data to tell better and more important stories is critical today, and data journalism is a necessary skill for every reporter and for every beat.

You’ve probably seen plenty of exciting examples of data journalism. Maybe reporting from 538 or big interactive graphics from the New York Times. Those are certainly high-profile examples of data journalism, but they are hardly the only examples — or even the most common.

Data journalism includes a wide variety of work, from analyzing local budgets to tracking an area’s crimes to cleaning and posting government data, like the salaries of public officials. While data journalism certainly can — and often does — include tables, charts, graphics and interactive elements, the essence of data journalism is simply using and presenting quantifiable information through a rigorous and reproducible method to inform and buttress your reporting and inform readers.

Some journalists might think of data journalism as that thing someone else does. Maybe a nerd in the corner — the reporter everyone turns to when they have a computer question. But the truth is, data journalism is for every reporter and for every beat.

Any story that relies on quantitative information — budgets, taxes, election results, salaries — is a data journalism opportunity. Sports stories, for example, are already rife with statistics. Enterprising reporters who dig into the numbers can reveal interesting and surprising results. Buzzfeed’s investigation into tennis matches strongly suggested top games had been fixed.

Government data provides tremendous opportunities for reporters to find stories hidden in the numbers. This Marshall Project did just that with its “Crime in Context” story, which goes behind the FBI’s Crime Statistics.

But even beats that, at first glance, might not seem rich with data often are once you dig a little deeper and think creatively. For example, this deep dive into the rhyming structure of the musical “Hamilton” uses data to connect rhymes and detail how the Tony Award-winning musical was composed.

Data usually involves numbers, and numbers usually means math. For many journalists, that makes the prospect of using data intimidating. But it needn’t be. Reject the notion that “you’re bad at math.” Everyone is bad at everything until they practice, and math is no different. For most journalism, the math you should know is pretty minimal: percent change, absolute differences, and calculating rates will cover the vast majority of math you’ll use. The key is to practice. Find ways to use math in your everyday work and life. When you go out to eat, try to calculate tips in your head. The more you do it, the more confident and comfortable you’ll become.

Another intimidating factor in data journalism can be the software people use. But just like kids don’t start their baseball careers in the Major Leagues, you don’t need to start with the most complex software. A spreadsheet program like Microsoft Excel, Google Sheets or even Apple Numbers can handle most of your data journalism needs, especially in the beginning. Like with math, find reasons to use spreadsheets: track expenses, make budgets, create shopping lists. Demystifying the technology will make working with it easier and more satisfying.

Going to meetings with or talking to data journalists — especially if you’re new to the game — can seem like visiting a foreign land with its own language. From math terms to technical jargon, the language barrier can be off-putting or intimidating. But don’t let t be. Use the resources here on BetterNews to learn the terms, or simply ask questions. Often people don’t realize when they slip into jargon and are happy to explain what they mean if asked.

Finally, don’t think of data journalism as just being about numbers. Data journalism can come in many forms, from graphics to tables to beautifully written narratives. Some stories feature few numbers, but wouldn’t have been possible without thoughtful analysis of the underlying data. And often the only way to do that is to jump right in.