The following is Part 3 of 4 (read parts 1, 2, 4) in our series dissecting the role of a Data Journalist. In this post Liv Buli (@lbuli) – Data Journalist of music industry analytics and insights provider Next Big Sound – gives a brief recap of the history of Data Journalism along with her account of the first 3 months as a Data Journalist.
Three months in, and it is hard to imagine where all that time went, how little I have slept, and just how much I have learned. Summer being one of the busiest seasons in the industry, with festivals staged almost every weekend, outdoor concerts by the bushel, tours across the country and story ideas for a new data journalist popping up every minute.
Since taking on the role as resident data journalist, I have been fielding a lot of questions about what exactly this means. The concept of including this type of information as a basis for articles is anything but new, a widely cited example is the use of educational data for an article in the very first issue of the Guardian in 1821. What has revolutionized this field in recent years is the amount of data available, and the speed with which this data is generated and delivered. At Next Big Sound we are gathering an average of 175 million data points each day.
There are several great examples of journalists who use data heavily in their work. Some of the best stories to hit the press this past year are articles based on data findings, such as the investigative series on horse racing in the New York Times entitled Death and Disarray at America’s Racetracks. Data journalism can also come in different formats, for instance, the News Application team at the Chicago Tribune consists of a group of programmers embedded in the newsroom, assisting journalists in uncovering data and creating visualizations. The magnitude of information now being gathered and stored within most fields, from healthcare to consumer behavior to the various social sciences, serves as an invaluable resource to those writing the news.
At this point, I am only just beginning to comprehend the endless opportunities for what kind of stories I can write based on this massive amount of data, and just how important it is to fuse this type of information into an industry that can be reluctant to the idea of change, but is rapidly changing nonetheless. I feel more in the loop when it comes to the industry, understand the ins-and-outs of our platform, and am able to quickly determine what stories our audience will respond to and how.
At Next Big Sound, I have at my fingertips a platform that allows me to easily graph information in order to see correlations, as well as the ability to pull overview reports of relevant data. Telling great stories then simply becomes a matter of figuring out the right questions to ask of the data and combining this with relevant reported content. Working as an embedded data journalist with a company can of course be challenging without an editorial team around me to bounce off ideas. However, I often use my colleagues as a sounding board and given all the stories the data has to tell, have yet to come up empty-handed when deadlines roll around.
Another aspect of the position that has risen in importance in the past few months has been ensuring the distribution of our content, through more than one channel. I find myself being interviewed about this new type of role, speaking on panels about the future of the industry, building individual relationships with editors and journalists that are interested in applying new data to the questions they are posing. In addition to this I am now working with several online publications to further syndicate the content of our blog, among them the MTV O Music Awards blog, Sidewinder.fm, Hypebot and others. As we continue to grow, I plan to cultivate more and more of these relationships in order to ensure that our content and mindshare around the data we have available at Next Big Sound is widespread and becomes part of the daily conversation in the music industry.
From time to time, I will describe my job as basically “de-nerdifying” Next Big Sound. Working on such a technical level, it can be a challenge for colleagues to communicate in a simple terms what they are doing. Here is where my listening, comprehension and communication skills come in handy. I take the complicated data science projects that they are working on, such as how the concept Granger Causality can be used to calculate the causation between social media metrics and record sales, and explain the significance of this to an industry that can be hesitant of using data.
Stay tuned next week for the final Part of our series where I’ll share some tips/tricks of the trade!