Data journalism can be much more than an impressive, interactive visualization or an inmmersive longform piece. There’s also the option of letting the data and the visualizations lead the storytelling, allowing for a much deeper comprehension of the subject at hand, as it’s the case with this work from the Tampa Bay Times. There, visualizations lead the story to show us the case they are investigating and explain to us why it’s important, taking us through each step.
Juan Francisco Caro (Extremadura en Datos), speaking at the II Jornadas de Periodismo de Datos in Barcelona:
Data journalism is not just getting data out there. You have to verify sources and master statistical concepts to avoid publishing mistaken assumptions and interpretations. If not, information becomes disinformation.
Nicola Hughes (The Times), speaking at the II Jornadas de Periodismo de Datos in Barcelona:
If you can write it, record it or film it it’s not from the web, you’re putting something else on the web
Data journalism is becoming too popular, in the sense that some people think that it’s enough to do some line charts, bar charts, just putting data out there, but they are not telling the story. There’s a need for storytelling.
The internet is transient, there’s no control over the tools you use, they can disappear. But knowing how to code solves that. And it also helps to document, backup, reproduce projects, and reuse tools in different projects.
The problem right now is not that information is scarce, it’s the opposite, organisations and institutions publish a ton of information, and because most journalists only look for press releases and copy to rewrite, interesting things become hidden in the deluge.
Advice to journalists: Take risks. Use your imagination. Think of yourself as a craftsman.
There’s no such thing as “I don’t know”, just “I haven’t googled it yet”.
Do one coding course, just one, and then start building things. You have to write a lot of bad poetry to start writing good poetry. it’s very much a craft.
I’m currently writing an article on digital currencies for a future, small-run magazine edited by Crazy Little Things. To complement the article, and to try to learn some new data-journalism skills I decided to do also a timeline of the most relevant digital currencies for the past 20+ years. This is the result:
How it’s done
I used Inkscape to draw the SVG file: timeline, bars and text, and create the layout. Then I added basic interactivity by hand using a text-editor. The content is based on my own research for the article. I plan to include in further releases a csv with the source data used in the timeline so it’s easier for others to replicate it using other tools.
Regarding interactivity, right now you can uncover some contextual information hovering your mouse over certain years, and click on the names of the digital currencies to go to their website or get more information. I plan to add more contextual information on the currencies, explaining the type of currency and the reason it dissapeared, if needed.
The project (just an SVG file) is hosted on Github. You can download it, fork it, open a new issue, send ideas or suggestions. The project is under a NC-BY-SA Creative Commons licence. This is my first time using Git and Github for a project like this, and I’ll share my experience in a separate post. I can tell you now that I’ll definitely keep using it.
I’m also open to criticism on the timeline content: Did I miss a critical digital currency project? Should I remove something from the timeline? Is any of the data wrong? I’m all ears.
Know that the most important part of data journalism is… journalism. Reporting. In other words, you know how to report a story, you understand how to treat data as a source. You know how to pick up a phone, and not just assume that everything you get in data form (especially government data) is complete and accurate.
You have at least basic data skills — meaning, you know your way around a spreadsheet. You can figure out for yourself how to import data, and do something with it. You also understand the basics of data analysis: rates, ratios, sums, averages, medians, and how to use them.
You have command of more advanced data analysis skills, such as GIS, basic statistics, advanced SQL, etc. You also may know some basic programming techniques (using the language of your choice… Python, Perl, Ruby. ILENE.. shoot, even .NET) to scrape the web, get and clean data.
You have some skills with a web framework (Django, Rails, Grails) in order to enhance your reporting online through data-driven applications that you create from scratch and host.