The When in Data Journalism

As the old adage goes, timing is everything. It's a valuable skill to know how to refurbish old data so that it's interesting to a modern readership. Likewise, in data journalism, it's imperative to keep an eye on contextual relevancy and know when is the optimal time to craft and publish a particular story.

When as the context to your story

If you want to craft a data journalism piece that really garners a lot of respect and attention from your target audience, consider when — over what time period — your data is relevant. Stale, outdated data usually doesn't help the story make breaking news, and unfortunately you can find tons of old data out there. But if you're skillful with data, you can create data mashups that take trends in old datasets and present them in ways that are interesting to your present-day readership.

For example, take gender-based trends in 1940s employment data and do a mashup — integration, comparison, or contrast — of that data and employment data trends from the five years just previous to the current one. You could then use this combined dataset to support a truly dramatic story about how much things have changed or how little things have changed, depending on the angle you're after with your piece.

Returning once again to the issue of ethical responsibilities in journalism, as a data journalist you walk a fine line between finding datasets that most persuasively support your storyline and finding facts that support a factually challenged story you're trying to push. Journalists have an ethical responsibility to convey an honest message to their readers. When building a case to support your story, don't take things too far — in other words, don't take the information into the realm of fiction. There are a million facts that could be presented in countless ways to support any story you're looking to tell. Your story should be based in reality, and not be some divisive or fabricated story that you're trying to promote because you think your readers will like it.

You may sometimes have trouble finding interesting or compelling datasets to support your story. In these situations, look for ways to create data mashups that tie your less-interesting data into some data that's extremely interesting to your target audience. Use the combined dataset as a basis for your data-driven story.

When does the audience care the most?

If your goal is to publish a data journalism piece that goes viral, then you certainly want to consider the story's timeliness: When would be the prime time to publish an article on this particular topic?

For obvious reasons, you're not going to do well by publishing a story in 2017 about who won the 1984 election for U.S. president; everyone knows, and no one cares. Likewise, if a huge, present-day media scandal has already piqued the interest of your readership, it's not a bad idea to ride the tailwinds of that media hype and publish a related story. The story would likely perform pretty well, if it's interesting.

As a recent example, you could have created a data journalism piece on Internet user privacy assumptions and breaches thereof and then published it in the days just after news of the Edward Snowden/NSA controversy broke. Keeping relevant and timely publishing schedules is one way to ensure that your stories garner the attention they need to keep you employed.