How to organize a data journalism team

bySérgio Spagnuolo
Dec 4, 2019 in Data Journalism
Computers

1. Determining a data team's role

Dedicated data teams must develop ongoing, diversified tasks characterized by three main activities, which can be mutually exclusive:

  • Your own data-driven content production
  • Response to news events
  • Assisting other editorial areas
Your own production

The content production program should include, but not be limited to:

  • Reporting, analysis and investigations
  • Data visualizations (graphs, maps, networks, timelines, etc.)
  • Special applications (interactive databases, quizzes, etc.)
  • Content for various editorial products
  • Stories of the week
  • Data panel
  • Production of tools and processes for data collection, extraction and analysis, including open source
News response

The data team must be ready to respond quickly to urgent events and have rapid response protocols in preparation for any expected or imminent news, in order to create as much impact as possible. 

In order to become more responsive to news events, the data team should:

  • Observe government events, procedures and calendars
  • Keep databases up to date with useful data ready to be employed
  • Keep applications fit for reference
  • Develop reusable templates and codes
  • Create data visualization templates
  • Produce content in advance, anticipating foreseeable events
  • Document data work (scrapers, analyses) to facilitate code and tool reuse
  • Prepare Freedom of Information Act (FOIA) requests
  • Create partnerships and agreements with other parties
Assisting other areas

While the data team should be seen as an autonomous unit and not as a “service provider,” it can assist other departments. For example, it can help

  • Cover events that require complex news efforts
  • Lend technical skills to another area
  • Prepare data content for reports and specials
  • Analyze data for other areas
  • Provide training and knowledge transfer

The team can also assist other departments in their investigative and special content efforts, but the priority will depend on the agreement between management and the data editor based on urgency, importance, timeliness and complexity. 

2. The team

The data journalism team should be

  • Multidisciplinary and diverse
  • Technically versatile
  • Responsive to current events and scenarios
  • Autonomous but collaborative

The data editor's job is to make sure that the work is performed properly and efficiently while maintaining quality standards, news flow and timelines.

Assignments need to be clearly established in order to avoid duplication of effort, task conflict and inefficiency of internal processes and protocols.

In any case, it is encouraged that all team members learn tasks and responsibilities from their peers, and eventually expand their knowledge to become better professionals.

Data teams must attend to the demands traditionally assigned, including data extraction, organization, analysis and visualization.

3. Specific roles and job descriptions

The data editor
  • Manages the data team
  • Communicates with the team about the adoption of new ways or the demands from other departments
  • Prepares the agenda of the week
  • Edits texts for publication
  • Reports, analyses and writes stories as needed
  • Collects data and assists in the production of applications as needed
The news reporter
  • Follows the data agenda of the week to allow the best possible preparation for events (for example, GDP reports, etc.)
  • Conducts investigations (collection of data, documents, interviews)
  • Prepares texts for data-driven reporting
  • Pitches special stories and news coverage
  • Works together with data analyst
The data analyst
  • Works in data extraction and collection
  • Conducts statistical analysis of data collected
  • Creates methodologies and approaches for data processing and analysis
  • Prepares reports that will support reports, views and other content
  • Reports, analyses and writes texts
The application analyst
  • Produces scrapers, crawlers, monitoring tools, packages and libraries for the purpose of automating data collection
  • Creates methodologies and approaches for data processing and analysis
  • Manages the database, development environment and data preparation for the production of special content, new coverage and other department’s demands
  • Elaborates reports that will support stories, visualizations and other content
  • Develops tools
  • Reports, analyses and writes texts as needed
  • Prepares data visualizations as needed
The visual reporter
  • Creates data visualization and designs applications
  • Processes final data for input to specific visualization tools
  • Develops data visualization methodologies, based on various available tools
  • Implements interactivity in visualizations
  • Elaborates reports that will support stories, visualizations and other content
  • Reports, analyses and writes texts

4. Data sources

One of the main activities of data editors is to identify and study the data provided by the institutions and topics they are supposed to cover. This means unraveling APIs from government agencies and keeping an eye on international organizations such as the World Bank, the Organisation for Economic Co-operation and Development (OECD), UN, International Monetary Fund (IMF) and others that often release data.

Other sources, including from businesses, social networks, leading consultancies and nonprofit organizations, should also be on the radar.

Additionally, teams must be encouraged to create their own sources through surveys, data crossovers, indexing, scrapings and more.

5. Quality policy

A good job is a job whose processes are efficiently reviewed and tested. Mistakes are inevitable, but they should be avoided as much as possible by designing processes and methods that include diligent reviewing and editing.

Any errors that may be published should be publicly corrected, followed by explanations.

The data team must explain references and methods adopted, such as best practices for transparency in data-driven journalism.

6. Formats

Engagement

The team must constantly think about formats, methodologies and content that can generate reader engagement. Whether it's breaking a story, producing an interactive chart, a quiz, or even answering reader comments, this is a goal that should always be part of the scope of the group.

Open data

The data team must have specific open source and data repositories to ensure data transparency, especially when using public data. Ideally, all published content should have its data and code subsequently added to the repository.

Template creation

The team must also produce templates to make the work easier — whether they are data templates, methods, publishing formats or visualizations that can be easily replicated, especially in the case they need to react quickly to the news.

Experimentation

The team should think about experiments that can be applied to the news, such as specific libraries or tools that can be used not only by the team or internally, but also by the reader.

7. Data as a resource, not a universal solution

Finally, data editors are successful only when they use data as a resource for journalism, not as a universal solution for reporting.

Although stories may be based solely on data, they are often accompanied by many validations with other types of sources, such as FOIA requests, documents, authorities, institutions and recognized experts.

Data can answer "what," "who," "when" and "where," but only with reporting do we answer "why" something really matters.


Sérgio Spagnuolo, former TruthBuzz fellow at the International Center for Journalists (ICFJ), is the editor and founder of Volt Data Lab and editor of Vortex Media

This article was originally published on the Medium under Creative Commons license.

Main image CC-licensed by Unsplash via Annie Spratt