Data Journalism 101: A Brief Survey

July 19, 2016 by Eileen Li

Welcome to the first post in our summer data journalism research series!

To kick things off, we started with a background survey of the topic. This meant doing some online digging to figure out who data journalists are, what issues they typically report on, and where to find some notable examples of their work. Here’s what we learned!

About our survey

In our research, we unearthed an abundance of guides for producing quality data journalism. For example, one of the great resources we found was The Art and Science of Data-Driven Journalism from the Tow Center for Digital Journalism (Columbia University), which gave us a comprehensive overview of the field. We also read through award-winning pieces of data-driven journalism, such as those by Buzzfeed and La Nacion. We learned about everything from police misconduct claims to college debt to air pollution levels in India, and ultimately gained a sense of what data journalism looked and read like.

So, what did we find?

That data journalism is not a monolith. Data journalists constitute an incredibly diverse community and, as a result, do a variety of different data tasks. Some journalists construct databases from scratch. Others make detailed visualizations that illuminate hidden patterns. They come from a range of technical backgrounds, from computer programmers to old-school, shoe-leather investigative reporters.

Some initial highlights

We discovered that each data project has many internal steps, starting with collecting the data and concluding by generalizing the results. Data journalists often divide their work into three categories:

Collection: Data journalists can take advantage of new ways of collecting information, such as by using civic open data portals, scraping data from websites, and compiling their own databases.

Organization: Data journalists can organize information by placing it into a relational database or by building a data visualization.

Explanation: This step involves going beyond the who, what, where and when questions to those of why and how. Data journalists often do so by running various types of statistical tests to look for relationships in the data.

A brief summary (in slides)

We’ve summarized our findings in this set of slides here, entitled “Data Journalism 101: A Brief Survey”. And now that we’ve covered the basics, we’ll be jumping right into interviewing data journalists. Look for our upcoming post, “Alex Richards: On data tools, challenges, and being skeptical.”