How are the media adapting to the new digital technologies that power blogs, interactive graphics, and social networks? Quickly is one answer, but the advent of digital (what used to be called “new”) journalism is more complicated than that. Last weekend, the Georgia Institute of Technology held a two-day symposium titled, “Journalism 3G: The Future of Technology in the Field.” CJR’s Curtis Brainard talked to co-organizer Brad Stenger, who is also research director of Wired’s annual tech fair, NextFest, about the merger of computers and the press.
Curtis Brainard: There are a lot of very technical things that need to be done to accomplish this digital revolution in the media. Are these things that traditional journalists can learn and do themselves, or is this going to require a whole new subset of the newsroom that has special knowledge in computer science, coding, and programming?
Brad Stenger: There are two fronts to answering that question. One is the general question, which isn’t just why shouldn’t journalists program, but why shouldn’t everyone program? When you think about the positive effects of what just a little bit of programming know-how can do for any person in just about any field-productivity, the exposure to ideas, and what they’re able to learn and accomplish-it really makes sense for just about anyone to pick up programming on some level.
The second half is this: Is a division of labor in the newsroom okay, assuming that not everyone programs, and how does the division of labor work? Every news organization has an IT staff and one of the things that’s striking about what I saw at the conference was how IT staffs are there to maintain infrastructure; they’re not necessarily there to deal with data and to generate insight about vast amounts of data. It’s a completely different skill set. If you have someone who can do both of those things in an IT department, they’re not long for that department-they’re too valuable and too talented. So it seems like the division of labor question is being misaddressed by news organizations across the board-that IT and maintaining infrastructure is different than dealing with and processing news as data, especially for the purpose of getting insight out of it.
CB: So you believe that the best product will result from this new breed of journalist that is fluent in both reporting and writing and in creating the underlying package and distribution infrastructure?
BS: If you’ve got a journalist who is data-literate, then the division of labor with IT smoothes out a little bit and the productivity goes up. And if the journalist isn’t, then it’s frustrating. There’s the potential to have a second IT core that does more with data than with maintaining infrastructure, some sort of specialist. I wouldn’t want to rule that out, it’s something that seems like it would be valuable. But it’s hard to say if that’s a straighter line to a solution than getting journalists up to speed on computing and computation individually.
CB: It sounds like this type of journalist that’s fluent in both halves of the operation is still pretty rare. Did the Georgia Tech conference address that?
BS: Well, we didn’t know how rare. Yeah, I think it does bear out that it is, but no one has really checked to see. And the truth of it will materialize in the next three to six months, if we see the progress in actual projects and actual things that get done.
CB: The conference seemed to revolve around five major uses for computational journalism: newsgathering, speed and workflow, social networking, interactive and participatory multimedia, and data visualization. In which of these did the audience seemed most interested?
BS: It varied person to person, and on both sides of the fence, whether it was a computer person or a news person. But the rationale for the event came from the fact that there are analogous research subjects in computer science to all of those areas in journalism. Newsgathering corresponds pretty closely to an area of computer science known as sensemaking-how do you go from not understanding a problem to understanding it. An example is Google.