Think of Data Journalism as four dynamic areas

Introducing data journalism into a legacy media newsroom is hard work. As a New Media Editor, to avoid feeling overwhelmed, I conceptualise the enterprise as four dynamic, interconnected areas.

1. Data collection
2. Data processing
3. Data storage
4. Data communication

Each area has its own set of core demands, which I’ll describe below.

1. Data collection involves finding, organising and liberating data sets. In order to do this, a data journalist or New Media Editor must build trustworthy networks of sources. It is also useful to keep in mind that mainstream journalists are themselves an important and under-explored source network. Because of their position in the mainstream media, these journalists have continual access to large repositories of data, although they are often locked in print, audio or video formats. Those data need to be mined, aggregated into contiguous sets and liberated onto open data platforms such as

Q: What other important source networks do data journalists need to contact and befriend? What are some practical ways that data journalists can discover and identify other important source networks in their sphere? Feel free to leave your comments below.

2. Data processing involves parsing data in order to flag irregularities. Data journalists must work alongside expert networks, such as those in the university community, who are able to reliably point out inconsistencies in data sets. As recognised professionals in their respective fields, academics can verify large complex data sets and provide credible analysis from a local/regional perspective.

Q: Apart from academics, what other categories of people would you rely on to verify data? Can you think of five subject area experts who have earned your respect and some degree of public trust? Feel free to leave your comments below.

3. Data storage ensures that data maintains its integrity. Online open data repositories, such as, or, are preferred storage options for journalists seeking to make data freely available to others. Of course, private data storage options are available for data sets that confidential or sensitive.

4. Data communication can be a serious challenge for data journalists with a background is in legacy media. We need to build networks of developers and designers, who can build infographic or interactive online interfaces that visualise and animate data. This typically involves investigating data to discover the meaning that is masked behind the complexity and sheer size of large, complex data sets. Rather than work alone, data journalists should build networks of like-minded colleagues with the collective capacity to discern and distil stories from data. Journalist networks can specialise in the verification of facts, the crowdsourcing of opinion, and the curation and distribution of content in various forms across various platforms, providing timely commentary on matters of interest.


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