Successfully applying data science to the practice of journalism requires more than providing context and finding clarity in vasts amount of unstructured data: it will require media organizations to think differently about how they work and who they venerate. It will mean evolving towards a multidisciplinary approach to delivering stories, where reporters, videographers, news application developers, interactive designers, editors and community moderators collaborate on storytelling, instead of being segregated by departments or buildings.
The role models for this emerging practice of data journalism won’t be found on broadcast television or on the lists of the top journalists over the past century. They’re drawn from the increasing pool of people who are building new breeds of newsrooms and extending the practice of computational journalism. They see the reporting that provisions their journalism as data, a body of work that can itself can be collected, analyzed, shared and used to create longitudinal insights about the ways that society, industry or government are changing. (Or not, as the case may be.)
In a recent interview, Emily Bell (@EmilyBell), director of the Tow Center for Digital Journalism at the Columbia University School of Journalism, offered her perspective about what’s needed to train the data journalists of the future and the changes that still need to occur in media organizations to maximize their potential. In this context, while the role of institutions and “journalism education are themselves evolving, they both will still fundamentally matter for “what’s next,” as practitioners adapt to changing newsonomics.
Our discussion took place in the context of a notable investment in the future of data journalism: a $2 million research grant to Columbia University from the Knight Foundation to research and distribute best practices for digital reportage, data visualizations and measuring impact. Bell explained more about what how the research effort will help newsrooms determine what’s next on the Knight Foundation’s blog:
The knowledge gap that exists between the cutting edge of data science, how information spreads, its effects on people who consume information and the average newsroom is wide. We want to encourage those with the skills in these fields and an interest and knowledge in journalism to produce research projects and ideas that will both help explain this world and also provide guidance for journalism in the tricky area of ‘what next’. It is an aim to produce work which is widely accessible and immediately relevant to both those producing journalism and also those learning the skills of journalism.
We are focusing on funding research projects which relate to the transparency of public information and its intersection with journalism, research into what might broadly be termed data journalism, and the third area of ‘impact’ or, more simply put, what works and what doesn’t.
Our interview, lightly edited for content and clarity, follows.
What did you do before you became director of the Tow Center for Digital Journalism?
I spent ten years where I was editor-in-chief of The Guardian website. During the last four of those, I was also overall director of digital content for all The Guardian properties. That included things like mobile applications, et cetera, but from the editorial side.
Over the course of that decade, you saw one or two things change online, in terms of what journalists could do, the tools available to them and the news consumption habits of people. You also saw the media industry change, in terms of the business models and institutions that support journalism as we think of it. What are the biggest challenges and opportunities for the future journalism?
For newspapers, there was an early warning system: that newspaper circulation has not really consistently risen since the early 1980s. We had a long trajectory of increased production and actually, an overall systemic decline which has been masked by a very, very healthy advertising market, which really went on an incredible bull run with a more static pictures, and just “widen the pipe,” which I think fooled a lot of journalism outlets and publishers into thinking that that was the real disruption.
And, of course, it wasn’t.
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.
The challenge for sustainable journalism going forward is not so different from what exists in other industries: there’s a skills gap. Data scientists and data journalists use almost the exact same tools. What are the tools and skills that are needed to make sense of all of this data that you talked about? What will you do to catalog and educate students about them?
It’s interesting when you say that the skills of these clients are very similar, which is absolutely right. First of all, you have a basic level of numeracy needed – and maybe not just a basic level, but a more sophisticated understanding of statistical analysis. That’s not something which is routinely taught in journalism schools but that I think will increasingly have to be.
The second thing is having some coding skills or some computer science understanding to help with identifying the best, most efficient tools and the various ways that data is manipulated.
The third thing is that when you’re talking about ‘data scientists,’ it’s really a combination of those skills. Adding data doesn’t mean you don’t have to have other journalism skills which do not change: understanding context, understanding what the story might be, and knowing how to derive that from the data that you’re given or the data that exists. If it’s straightforward, how do you collect it? How do you analyze it? How do you interpret them and present it?
It’s easy to say, but it’s difficult to do. It’s particularly difficult to reorient the skillsets of an industry which have very much resided around the idea of a written story and an ability with editing. Even in the places where I would say there’s sophisticated use of data in journalism, it’s still a minority sport.
I’ve talked to several heads of data in large news organizations and they’ve said, “We have this huge skills gap because we can find plenty of people who can do the math; we can find plenty of people who are data scientists; we can’t find enough people who have those skills but also have a passion or an interest in telling stories in a journalistic context and making those relatable.”
You need a mindset which is about putting this in the context of the story and spotting stories, as well having creative and interesting ideas about how you can actually collect this material for your own stories. It’s not a passive kind of processing function if you’re a data journalist: it’s an active speaking, inquiring and discovery process. I think that that’s something which is actually available to all journalists.
Think about just local information and how local reporters go out and speak to people every day on the beat, collect information, et cetera. At the moment, most get from those entities don’t structure the information in a way that will help them find patterns and build new stories in the future.
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.
The other thing to say, which is really important, is there is currently a lack of both jobs and role models for people to point to and say, “I want to be that person.”
I think the final thing I would say to the industry is we’re getting a lot of smart journalists now. We are one of the schools where all of our digital concentrations from students this year include a basic grounding in data journalism. Every single one of them. We have an advanced course taught by Susan McGregor in data visualization. But we’re producing people from the school now, who are being hired to do these jobs, and the people who are hiring them are saying, “Write your own job description because we know we want you to do something, we just don’t quite know what it is. Can you tell us?”
You can’t cookie-cutter these people out of schools and drop them into existing roles in news trends because those are still developing. What we’re seeing are some very smart reporters with data-centric mindsets and also the ability to do these stories — but they want to be out reporting. They don’t want to be confined to a desk and a spreadsheet. Some editors usually find that very hard to understand, “Well, what does that job look like?”
I think that this is where working with the industry, we can start to figure some of these things out, produce some experimental work or stories, and do some of the thinking in the classroom that helps people figure out what this whole new world is going to look like.
What do journalism schools need to do to close this ‘skills gap?’ How do they need to respond to changing business models? What combination of education, training and hands-on experience must they provide?
One of the first things they need to do is identify the problem clearly and be honest about it. I like to think that we’ve done that at Columbia, although I’m not a data journalist. I don’t have a background in it. I’m a writer. I am, if you like, completely the old school.
But one of the things I did do at The Guardian was helped people who early on said to me, “Some of this transformation means that we have to think about data as being a core part of what we do.” Because of the political context and the position I was in, I was able to recognize that that was an important thing that they were saying and we could push through changes and adoption in those areas of the newsroom.
That’s how The Guardian became interested in data. It’s the same in journalism school. One of the early things that we talked about [at Columbia] was how we needed to shift some of what the school did on its axis and acknowledge that this was going to be key part of what we do in the future. Once we acknowledged that that is something we had to work towards, [we hired] Susan McGregor from the Wall Street Journal’s Interactive Team. She’s an expert in data journalism and has an MA in technology in education.
If you say to me, “Well, what’s the ground vision here?” I would say the same thing I would say to anybody: over time, and hopefully not too long a course of time, we want to attract a type of student that is interested and capable in this approach. That means getting out and motivating and talking to people. It means producing attractive examples which high school children and undergraduate programs think about [in their studies]. It means talking to the CS [computer science] programs — and, in fact, more about talking to those programs and math majors than you would be talking to the liberal arts professors or the historians or the lawyers or the people who have traditionally been involved.
I think that has an effect: it starts to show people who are oriented towards storytelling but have capabilities which are align more with data science skill sets that there’s a real task for them. We can’t message that early enough as an industry. We can’t message it early enough as an educator to get people into those tracks. We have to really make sure that the teaching is high quality and that we’re not just carried away with the idea of the new thing, we need to think pretty deeply about how we get those skills.
What sort of basic sort of statistical teaching do you need? What are the skills you need for data visualization? How do you need to introduce design as well as computer science skills into the classroom, in a way which makes sense for stories? How do you tier that understanding?
You’re always going to produce superstars. Hopefully, we’ll be producing superstars in this arena soon as well.
We need to take the mission seriously. Then we need to build resources around it. And that’s difficult for educational organizations because it takes time to introduce new courses. It takes time to signal that this is something you think is important.
I think we’ve done a reasonable job of that so far at Columbia, but we’ve got a lot further to go. It’s important that institutions like Columbia do take the lead and demonstrate that we think this is something that has to be a core curriculum component.
That’s hard, because journalism schools are known for producing writers. They’re known for different types of narratives. They are not necessarily lauded for producing math or computer science majors. That has to change.