Data Journalism as an Innovative Journalism Practice
Originally Published on March 11, 2023
Data journalism is widely regarded as an innovative form of journalism. In one of my earlier posts, I discussed key terms such as data journalism, big data, and open data to provide foundational insights into this evolving practice. Unlike traditional reporting, data journalism starts with the dataset. The process of story discovery differs significantly from routine journalism. In this approach, instead of initially posing questions to human sources, journalists begin by interrogating the dataset—technically speaking, not literally. This method can uncover new angles and generate diverse story ideas.
The process involves skimming and scanning through the dataset repeatedly. Each pass can reveal new data points and patterns that require further analysis. This iterative engagement with the data is at the core of data journalism.
While the concept of data journalism is relatively recent, its roots can be traced back to Philip Meyer’s book Precision Journalism, published in 1991. Meyer highlighted the potential of statistics and data analysis in improving news reporting. In the United States, the increasing availability of computers led to the digitisation of data, allowing journalists to analyse information on a much larger scale than was previously possible. This shift from manual to digital storage laid the groundwork for data-driven reporting.
Over time, data journalism has developed distinct models and workflows. While these vary by region and newsroom size, they share common elements. Notably, there is no single, universal model for producing a data story.
For instance, Paul Bradshaw (2010) proposed a simplified yet influential model consisting of the following steps:
Step 1- Finding the data
Step 2- Interrogating data
Step 3- Visualising data
Step 4- Mashing data
Similarly, Alexander Howard (2014) explains the flow of Data journalism as “gathering, cleaning, organizing, analysing, visualizing, and publishing data to support the creation of acts of journalism”
Major news organisations such as The Guardian have developed workflows based on their newsrooms. However, many steps remain consistent across contexts, such as data gathering and cleaning. As part of my doctoral research, I studied data journalism in the Indian context. This included analysing published data stories and conducting interviews with data journalists across India to understand the workflow they follow. Based on this research, the typical workflow in Indian newsrooms includes the following steps:
Story Idea/Finding the source of data
Data Extraction
Data Cleaning
Analysing Data
Interpreting Data
Reporting (Asking for opinions/comments)
Visualizing data
Writing news story
The above process is based on the research conducted as part of the PhD Program, and his model reflects the practical realities and constraints of Indian newsrooms, offering a contextual understanding of how data journalism is practiced on the ground.
