Data Journalism Made Easier, Faster, and More Collaborative

2 min readNov 16, 2017


Data journalism needs help.

In most small newsrooms, working with data is still much harder than it should be. A majority of reporters do not possess the technical skills needed to work with data, and too many important stories remain out of reach.

Meanwhile, this is a time when the facts are continuously challenged and denied to support partisan narratives. It is more important than ever to give journalists the tools they need not just to work with the data, but to show what they did and how they arrived at their conclusions.

This is why we are building Workbench. You can sign up now.

Workbench is an integrated data journalism platform that makes it easy to assemble data scraping, cleaning, analysis and visualization tasks without any coding. You can load live data from the sources you already use, including open government portals, social media, SQL Databases, and Google Drive. Completed workflows can be shared along with the story to show audiences the story behind the chart.

Sharing the workflow also helps other journalists to learn how to produce similar work. Anyone can duplicate a live workflow to create an editable copy, much like “forking” a github repo.

Workbench is an open source project, begun at Columbia Journalism School last fall by veteran computational journalist Jonathan Stray. Since then we’ve added product designer Pierre Forcioli-Conti and developer Matt Gerring. Founding funds were provided by Krishna Bharat, and today we are very pleased to announce that Workbench has received the support of the Knight Foundation, with a $250,000 grant.

Many people have tried to make data journalism easier, but open-source journalism software is incredibly fragmented. Good tools are hard to find and harder to use, most end up as abandoned Github repos. We need a different approach: an extendible platform where friendly data journalism tools can live.

Workbench is that platform: an accessible, transparent, and extensible system for every step of data journalism production.

This modular, open source system is built specifically for journalism and the news developer community. Unique stories have unique demands, which is why the workbench can be extended with Python code. You can use it to create workbook-style mixes of descriptive text and code, similar to the Jupyter platform. Then you can save your best code as a pre-packaged module that anyone else on the platform can use — through a visual interface — making it easy for programmers in one newsroom to share tools with journalists in another organization.

We believe the platform and tools we are building are also useful for other communities and industries. We are very excited about the role workbench can have in further establishing data storytelling as an essential cultural medium.




The data journalism platform with built-in training. A project of Columbia Journalism School