We live in a world in which it is increasingly important to understand complex socio-economical and ecological phenomena to make well-informed decisions. Consequently, it has become crucial for journalists to use elements of data science in their work. A new field has emerged: Data-driven Journalism (DDJ), which involves computer-supported, data-based reasoning and interactive visualization.

The data-driven journalism community is growing all over the world and has found its way into a number of well-known news organizations such as The New York Times or The Guardian. But the majority of journalists still faces significant obstacles. Three main gaps hinder journalists from utilizing data for their work:

The Usage Gap: finding usable systems
Journalists are often not trained in programming and in data analysis, which makes it difficult for them to use tools that require advanced technical expertise.

The Technology Gap: dealing with heterogeneous data
Journalistic work deals with complex, heterogeneous data sources. Most available analysis techniques focus on specific data structures and cannot deal with more complex heterogeneous data sources. This is also a major challenge in Visual Analytics (VA).

The Workflow Gap: encouraging DDJ in daily workflows
Journalists are supported by IT infrastructure and follow a specific workflow in the news production process under tight pressure of time and resources. DDJ is not well covered by this workflow and not supported by the IT systems in the background.

Project goals

In our project, we will bridge these gaps by

• following a user-centered and problem-driven research process
• designing techniques to support data journalists in dealing with complex heterogeneous data
• developing a set of guidelines and best practices for DDJ workflows.

We are focusing on two types of data, that will be embedded in two sample scenarios, pursued throughout the project:

• textual data over time and
• dynamic networks combined with quantitative flows.