The Wall Street Journal, over 125 years old, continues to break new ground in the worlds of data journalism. In addition to our everyday coverage, which is frequently data-driven, our most ambitious data projects shed light on Russian trolls, odd market trading, homicides and suicides and infrastructure. We write for a large mainstream audience and a subscriber base that expects unique insight into current affairs as well as market and finance coverage.
What makes this project innovative?
With deep roots as a business publication, WSJ reporters and editors are by default data-savvy and chart-literate. One key upshot is frequent collaboration between traditional reporters and specialised data/visual journalists. For \'The Morningstar Mirage\', the data group compiled a 3-million-record data set and carried out a sophisticated analysis of Morningstar\'s ratings. Graphics staff then worked with those reporters to produce innovative visualizations to help the reader understand the complex story in a meaningful way.
What was the impact of your project? How did you measure it?
Our data-driven projects resonate deeply with readers, which is evident from engagement and subscription data. The stories can have wide-reaching implications in politics and policy, business and finance. For instance, during our investigation into Russian disinformation campaigns, we found that many of the users who seemed to be involved were still active on Twitter. After we determined some of these accounts had stolen their profile information from actual American users, Twitter “restricted” the accounts—and eventually suspended them. Collectively, the Journal’s stories on information attacks by the Russians were viewed more than 900,000 times.
Source and methodology
We work with data from government organisations, analysts, our internal Market Data Group, and sometimes data we compile ourselves. When it comes to data quality and reliability, at WSJ we have extremely high standards, and sourcing data can sometimes take as long as the analysis or visualization.
We use a huge range of technologies to assist our reporting and data visualization, including Python/Pandas, SQL, R, Microsoft Excel, D3.js, Adobe Illustrator, Three.js and ai2html, plus several internal tools.