The Inapplicability of Objectivity: Understanding the Work of Data Journalism*

Data journalism is an emerging form of journalism, entailing the discovery of stories in data with the assistance of data algorithms. The burgeoning literature has largely interpreted the work of data journalism through the lens of objectivity. This paper, however, rejects the applicability of objectivity to data journalism. This inapplicability is the product of five factors: the extensive use of data and data algorithms in journalism; the difficulty in verifying data; the imbalance in data and data access; the uncertainty about if and to what extent data journalists can obtain sufficient knowledge of data contexts and algorithms; and their "design subjectivity" in the data processing process. Data reporting becomes a process of knowledge construction under the influence of these factors. The article argues that because of the social constructionist nature of data journalism, serving the public interest and democracy is a more appropriate principle than objectivity for data journalism. It suggests shifting academic attention from celebrating objectivity in data journalism to examining the epistemology of data journalists, the factors influencing data journalists' formation of knowledge in reporting, their defence of cultural authority, and the democratic meanings of data reports in future research. Such understanding also has implications for data journalism pedagogy and practice.