"Explainer journalism" is pretty popular right now. Vox.com has "decks" where you can learn about the background of any subject currently making headlines. The New York Times also has an explainer arm. And my perennial favorite, Slate.com, has a regular explainer column. But these verticals are largely backward-looking. Instead, I think the next big trend could be "predictive" journalism. Using the "know-it-all" mentality of explainer journalism, journalists might instead try to predict what will actually happen.
To give you an idea of what I mean, I'm drafting short (fictional) news items:
Expect Back-Ups On I-96 Today
Driver on I-96 in Ingham County should be prepared for slow-moving traffic this morning. Snow is on the way, and traffic data from the east side of the state shows a Taylor Swift concert snarled traffic last night, and traffic patterns have not quite recovered. Ingham County drivers can expect the impact to be felt locally until this afternoon.
Or perhaps more in-line with what we talked about last week with crowd-sourced data about Boko Haram attacks:
Terrorists Likely To Strike North American City in (Year)
Increased terror chatter and networking math shows a terror cell is planning an attack in a North American city. Based on public data about national security resources, current computer models show the US does not have the staff to stop it.
Just today, I read this article myth-busting some ideas about modern journalism: http://www.poynter.org/2016/shorter-isnt-better-photos-arent-always-allu... Among the key take-aways: investigative journalism continues to be a traffic-driver. Predictive journalism, if tied with investigative journalism, could boost traffic numbers.
I think it also applies to the less wonky reader or journalism consumer. For example, in local TV news, you're encouraged to think about "New, Now, Next" and to remember that a lot of the reason why people read the news is to prepare themselves for their day. They want to know about inclement weather and bad traffic--but they also want to know when is a good time to try new restaurants or if property values in a given neighborhood will stay steady.
By marrying crowd-sourced and social media data with data journalism, we could start to deliver those stories locally every day. What I'm not sure about is how likely we are to get that kind of access; for all of the lack of privacy of modern life, there's also a pretty strong movement working to protect privacy.
As to whether predictive journalism is already a thing: I think so. So does Neiman Labs: http://www.niemanlab.org/2015/12/big-data-triggers-predictive-journalism/ (I found this article just before I published this post).
I think the values that drive predictive journalism are already firmly in place. Future-oriented verticals are also already established (Slate has Future Tense, and there are Gawker sites like Gizmodo and i09 that publish items about new technology).
There are also some ethical things to consider. We have a professional responsibility not to broadcase rampant speculation. The ethical norms now mostly say that if you want to write a story like anything I just mentioned above, you need to have a source who is sounding the call. But with publically available data, maybe the ethical norm will shift where your (the journalist's) analysis of the data is adequate sourcing for the headline. It's also possible that to protect journalistic integrity, news stations might start subscribing to some sort of system that allows them to make predictions like these. Maybe one local newsroom uses a Google predictions product and another local newsroom uses a Yahoo predictions product. Journalists ask the systems questions, then cite the data in their news copy.