Project description

The Wall Street Journal, over 125 years old, continues to break new ground in the world of data journalism. In addition to our everyday coverage, which is frequently data-driven, our most ambitious data projects shed light on oddities in the cryptocurrency world, examined salary comparisons for hundreds of companies, estimated the impact of the new tax law on individuals, illustrated how Saudi Arabia props up its stock market and revealed how online companies help travelers evade customs. 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 specialized data/visual journalists. For ‘Buyer Beware’, data reporters analyzed more than 1,400 PDFs to identify duplicate language in white papers for initial coin offers, and identified fake team members by reverse image searching photos of people associated with 343 projects lacking key details about team members. The graphics team helped visualize the extent of these red flags.

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, our analysis of day-care centers listed on Care.com as state-licensed uncovered hundreds for which no record of a license could be found. The website scrubbed its site of tens of thousands of unverified listings just before our story published.

Source and methodology

We work with data from government organizations, 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.

Technologies Used

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.

Project members

WSJ staff

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