On Data Journalism: Everything is changing, changing all the time

One of the lingering dilemmas, since Craigslist shattered the classifieds and the Internet splintered the advertising feed: We need journalism, but what are we willing to pay for it?

And how will they figure out how best to do it in a way that both feeds us key info and gets us to pay for it?

One opportunity could be journalists who are good at collecting and distilling and interpreting the mind-boggling amount of data that’s out there about everything, every day, all the time (even this post in itself is createing still more data).

The whole skill about identifying what is news and why is not new — but how to master it in this era is different. From an interview about data journalism:

The real disruption was the ability of anybody anywhere to upload multimedia content and share it with anybody else who was on a connected device. That was the thing that really hit hard, when you look at 2004 onwards.

What journalism has to do is reinvent its processes, its business models and its skillsets to function in a world where human capital does not scale well, in terms of sifting, presenting and explaining all of this information. That’s really the key to it.

The skills that journalists need to do that — including identifying a story, knowing why something is important and putting it in context — are incredibly important. But how you do that, which particular elements you now use to tell that story are changing.

Those now include the skills of understanding the platform that you’re operating on and the technologies which are shaping your audiences’ behaviors and the world of data.

By data, I don’t just mean large caches of numbers you might be given or might be released by institutions: I mean that the data thrown off by all of our activity, all the time, is simply transforming the speed and the scope of what can be explained and reported on and identified as stories at a really astonishing speed. If you don’t have the fundamental tools to understand why that change is important and you don’t have the tools to help you interpret and get those stories out to a wide public, then you’re going to struggle to be a sustainable journalist.

One of the things that has changed: In the past, you went to an “expert” for quotes and to frame the story, or you pored through reams and reams of public documents to collect your own data to fuel the story (e.g. “Through a Freedom Of Information Act request, Times reporters reviewed 3,205 public documents to determine…” etc.).

Now there is an almost overwhelming amount of data to review if you know where to look and what to look for.

This is not just about an amazing graphic that the New York Times does with census data over the past 150 years. This is about almost every story. Almost every story has some component of reusability or a component where you can collect the data in a way that helps your reporting in the future. To do that requires a level of knowledge about the tools that you’re using, like coding, Google Refine or Fusion Tables. There are lots of freely available tools out there that are making this easier. But, if you don’t have the mindset that approaches, understands and knows why this is going to help you and make you a better reporter, then it’s sometimes hard to motivate journalists to see why they might want to grab on.

It’s not just capturing that data once, for one story. It’s capturing it in a way that you can add to it for story after story as conditions change — or, to put it another way, as new data is added.

I see this with sports coverage, ironically. Whereas the narrative used to be carried by the flowery columnist who could turn a phrase and talk to a coach or two over a beer about a player, now there is tons of on-field or on-ice data that helps sift away biases and expose what a player really does or doesn’t do on the field or on the ice.

For many sports fans, this has changed how the game is perceived — and, in some cases, removes some of the mystery and subjectivity. (Think of the book or movie “Moneyball” and the stat analysis that it chronicled. Except now, instead of one team having that new insight, everyone does — teams, fans, even players if they pay attention.)

As a result, the model for understanding sports even on a rec level has almost flipped on its head. For almost every sports story, inquisitive fans will demand data to back up conclusions — and fans who prefer the old way, the old “innocent” times when a guy who hit the game-winning hit really was “clutch” in our eyes and the guy who was a “good guy who sacrifices himself for the team” really was good and essential in our eyes, instead of talent-limited but doing every last thing to try to retain a job and be somewhat useful to the team.

Tellingly, the old-school sports journalists who really rant about bloggers the most — “some kid with a computer in his mama’s basement” — are often reacting against this trend: They are actually compensated reporters using data and methods of analysis that the “old school” columnists do not understand and often do not want to understand. So they dismiss the analysis and paint all “bloggers” with the same brush, as if they’re all anonymous message board rumor-mongers, when in fact more and more of them have attained positions writing for outlets all over the world specifically because they have mastered these new skills.


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