Our Stories Come As Code

Figure 25. <em>Airport noise map</em> (Taz.de)
Figure 25. Airport noise map (Taz.de)

OpenDataCity was founded towards the end of 2010. There was pretty much nothing that you could call data journalism happening in Germany at this time.

Why did we do this? Many times we heard people working for newspapers and broadcasters say: “No, we are not ready to start a dedicated data journalism unit in our newsroom. But we would be happy to outsource this to someone else.”

As far as we know, we are the only company specialising exclusively in data journalism in Germany. There are currently three of us: two of us with a journalism background and one with a deep understanding of code and visualization. We work with a handful of freelance hackers, designers and journalists.

In the last twelve month we have undertaken four data journalism projects with newspapers, and have offered training and consultancy to media workers, scientists and journalism schools. The first app we did was an interactive tool on airport noise around the the newly built airport in Berlin with TAZ. Our next notable project was an application about data retention of the mobile phone usage of a German politician with ZEIT Online. For this we won a Grimme Online Award and a Lead Award in Germany, and an Online Journalism Award by the Online Journalism Association in the US. At the time of writing, we are have several projects in the pipeline — ranging from simpler interactive infographics up to designing and developing a kind of data journalism middleware

Of course, winning prizes helps to built a reputation. But when we talk to the publishers, who have to approve the projects, our argument for investing into data journalism is not about winning prizes. Rather it is about getting attention over a longer period in a sustainable way. Building things for their long term impact, not for the scoop, which often is forgotten after a few days.

Here are three arguments which we have used to encourage publishers to undertake longer term projects:

Data projects don’t date

Depending on their design, new material can be added to data journalism apps. And they are not just for the users, but can be used internally for reporting and analysis. If you’re worried that this means that your competitors will also benefit from your investment, you could keep some features or some data for internal use only.

You can build on your past work

When undertaking a data project, you will often create bits of code which can be reused or updated. The next project might take half the time, because you know much better what to do (and what not to) and you have bits and pieces you can build on.

Data journalism pays for itself

Data driven projects are cheaper than traditional marketing campaigns. Online news outlets will often invest in things like Search Engine Optimization (SEO) and Search Engine Marketing (SEM). A executed data project will normally generate a lot of clicks and buzz, and may go viral. Publishers will typically pay less for this then trying to generate the same attention by clicks and links through SEM.

Our work is not very different from other new media agencies: providing applications or services for news outlets. But maybe we differ in that we think of ourselves first and foremost as journalists. In our eyes the products we deliver are articles or stories, albeit ones which are provided not in words and pictures, audio or video, but in code. When we are talking about data journalism we have to talk about technology, software, devices and how to tell a story with them.

To give an example: we just finished working on an application, which pulls in realtime data via a scraper from the German railway website. Thus enabling us to develop an interactive Train Monitor for Süddeutsche Zeitung, showing the delays of long-distance trains in realtime. The application data is updated every minute or so and we are providing an API for it, too. We started doing this several months ago, and have so far collected a huge dataset which grows every hour. By now it amounts to hundreds of thousands of rows of data. The project enables the user to explore this realtime data, and to do research in the archive of previous months. In the end the story we are telling will be significantly defined by the individual action of the users.

In traditional journalism, due to the linear character of written or broadcasted media, we have to think about a beginning, the end, the story arc and the length and angle of our piece. With data journalism things are different. There is a beginning, yes. People come to the website and get a first impression of the interface. But then they are on their own. Maybe they stay for a minute — or half an hour.

Our job as data journalists is to provide the framework or environment for this. As well as the coding and data management bits, we have to thing of clever ways to design experiences. The User Experience (UX) derives mostly from the (Graphical) User Interface (GUI). In the end this is the part which will make or break a project. You could have the best code working in the background handling an exiting dataset. But if the front-end sucks, nobody will care about it.

There is still a lot to learn about and to experiment with. But luckily there is the games industry, which has been innovating with respect to digital narratives, ecosystems and interfaces for several decades now. So when developing data journalism applications we should watch closely how game design works and how stories are told in games. Why are casual games like Tetris such fun? And what makes the open worlds of sandbox games like Grand Theft Auto or Skyrim rock?

We think that data journalism is here to stay. In a few years data journalism workflows will be quite naturally be embedded in newsrooms, because news websites will have to change. The amount of data that is publicly available will keep on increasing. But luckily new technologies will continue to enable us to find new ways of telling stories. Some of the stories will be driven by data and many of applications and services will have a journalistic character. The interesting question is: which strategy are newsrooms going to develop to foster this process? Are they going to build up teams of data journalists integrated into their newsroom? Will there be R&D departments, a bit like in-house startups? Or will parts of the work be outsourced to specialized companies? We are still right at the beginning and only time will tell.

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