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February 5, 2015

Filed under: journalism»industry

What is Data Journalism?

This week, if you want to be horrified by our grim meathook future, check out these posts from Seattle Times news librarian Gene Balk on vaccination rates at Washington State schools. There's a searchable data table and a map, but I'll spoil it for you: a large proportion of parents should probably pack surgical masks and antibiotics with their kids' lunches, because herd immunity is basically a thing of the past.

This kind of database-driven reporting is a staple of Gene's "FYI Guy" blog, and readers seem to enjoy it. Done right, it can help flesh out local coverage in interesting ways, explore topics that are off the beaten path, and find connections that we might otherwise miss. That said, I don't think you can stress enough how much of that depends on the quality of the reporter: Gene is a great researcher, and not everyone has his skills and experience.

By coincidence, yesterday Melissa Bell at Vox announced that they're (re)entering the field of data journalism in a almost parodically-titled post. I'm a little confused about the timing, since I thought data journalism was a part of their whole raison d'etre, but maybe I'm confusing them with a different scrappy, SEO-oriented news startup. Regardless, welcome to the party! After name-checking Philip Meyer's Precision Journalism, Bell adds a list of nine basic guidelines they plan to use. It's not a bad list, although several items are inoffensively bland (has anyone ever aspired to produce content that isn't "relevant and useful?").

  1. Vox will work to provide the most relevant and useful data behind the news, when you need it, in ways that help you understand the stories that matter most.
  2. We will work to make all the data behind our stories available to you to download and play with for yourself.
  3. We want you to improve on what we’ve done, to play with the data, visualize it, and help us analyze it — and make our work better.
  4. We will prioritize building data sets that can feed many stories, rather than focusing on one-off projects.
  5. Our data visualizations will be clear, concise, and deep — to help you understand our editorial better. They will adhere to design rules which ensure their accuracy and transparency.
  6. In the event we make a mistake (they do happen), we will swiftly and clearly clarify, correct, and communicate that as transparently as we can.
  7. We will curate and showcase the best data infographics and visualizations on the web.
  8. Visualizations we produce in-house will work well on as many platforms as possible: if you view it on a smartphone, it will function as well as it does on web.
  9. We will curate and publish the best content that our community of readers produces. Our data journalism is as much about you, the community, as it is about us: this is a partnership.

Some of these goals are particularly strong, and we share them at the Seattle Times. Take #2, for example: not only do I think it's important that we publish the data on which our visualizations are built whenever possible, but we also open-source our graphics so that people can see the methodology we used. It's also just good sense to be mobile-friendly (#8), although I personally believe that there are some times when a story simply can't be fully told on a 4" screen.

I'm less sure about curation, either from readers (#9) or around the web(#7), particularly in conjunction with accuracy and corrections (#6). One of the strengths of a newsroom is supposed to be fact-checking, but it's not clear to me what the process is for verification of third-party visualizations, or if Vox plans to do so at all (it hasn't been evident to me as a reader that they do it now). Which is too bad, because I think a kind of real-time "Snopes for bad reporting" is a site I'd definitely support.

But I'm really most skeptical of #4, which Bell elsewhere refers to as "finding, cleaning, and setting up data streams so that they can be the source for repeated stories." It's not that I think it's necessarily a stupid idea. I'm just not sure that it's effective, based on my experience. Data stories are just reporting. Data streams are reporting on top of engineering on top of reporting.

CQ's Economy Tracker, for example, was my team's attempt at a reusable data API, but it turned out to be a frustrating experience to keep it topped off with up-to-date content, the architecture was a hard problem to solve, and the number of stories we pulled out of it probably didn't justify the effort. It turns out that it's hard to find a data set that can actually support a series of articles.

(You may say, at this point, hang on a minute: wasn't Congressional Quarterly an example of exactly what we're talking about? It's a large, data-oriented news organization that sold access to data streams, and maintained datasets that were used to build stories and interactives via the multimedia team. Which is true, but it elides a number of factors: CQ was a single-purpose news site — congress and legislation only — with a huge number of reporters feeding the beast and a large technical staff to tend to it. Vox does not have those advantages, since it's a general-audience, international news site with a much smaller staff.)

More importantly, a "data stream," like an API, demands maintenance which quickly becomes a drag on the amount of time that can be spent on efforts outside those streams. That's doubly true if you make them public, and people start relying on them. Will will Vox sunset these data streams, if they stop being useful internally? What are the cutoff criteria? How will they let people know before the source is shut down? Most importantly, how much time will be taken away from reporting to maintain the data products?

When I joined at the Seattle Times, I made a pitch to editors that was a little different: instead of designing long-running services, we generally build news apps that are scoped to a specific point in time. In other words, we make stories, the same as the rest of the newsroom does. And just as you wouldn't normally ask a reporter to go back and update all their old stories when new events happen, we don't maintain news apps more than a week or two after publication (barring, of course, normal corrections and serious bug-fixes). Our entire development stack, in fact, is based on this assumption — that's why we publish static files to S3 (which is cheap and easy), instead of running a Rails/Laravel/Node server (which is expensive and hard).

Maybe for Vox, this isn't a problem. After all, they're the people with the "poor man's Wikipedia" card stacks that they maintain for topics over many months, and the evergreen experiments. At the very least, though, it does highlight a very real distinction that goes (in my opinion) beyond "data journalism" and to the core of the digital news mission. Are we building general systems and tools to cover unique stories? Or are we optimizing for semi-predictable products built around APIs and data sources? I'm leaning toward the former because I think it's a better match for a messy, unpredictable, human world. But best of luck to Vox with the latter.

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