Patterns in Award Winning Data Storytelling

Abstract

Data storytelling is rapidly gaining prominence as a characteristic activity of digital journalism with significant adoption by small and large media houses. While a handful of previous studies have examined what characterises aspects of data storytelling like narratives and visualisation or analysis based on single cases, we are yet to see a systematic effort to harness these available resources to gain better insight into what characterises good data stories and how these are created. This study analysed 44 cases of outstanding data storytelling practices comprising winning entries of the Global Editors Network's Data Journalism Award from 2013 to 2016 to bridge this knowledge gap. Based on a conceptual model we developed, we uniformly characterised each of the 44 cases and then proceeded to determine types of these stories and the nature of technologies employed in creating them. Our findings refine the traditional typology of data stories from the journalistic perspective and also identify core technologies and tools that appear central to good data journalism practice. We also discuss our findings in relations to the recently published 2017 winning entries. Our results have significant implications for the required competencies for data journalists in contemporary and future newsrooms.

Data Journalism Practices Globally: Skills, Education, Opportunities, and Values

“…The participant added that many organisations believe that data-research and visualisation is a service for journalists in the news organisation, and expressed the belief that in order to do excellent and original data journalism work, a team needs to work with the story from the beginning to the end-i.e., from the idea phase to visualisation and writing (participant, 500+ size organisation, Denmark). The idea that the best work emerges when data journalists or data teams are integrated in the newsroom as editorial staff (instead of as a service desk) is corroborated by studies on the characteristics of high quality data journalism (e.g., Ojo and Heravi 2018;Young et al 2018;Loosen et al 2017).…”

“…In addition to the above studies, there have been a smaller number of recent studies examining the characteristics of good or award-winning data journalism (Young et al 2018;Ojo and Heravi 2018;Loosen et al 2017). Amongst these, Ojo and Heravi (2018) examined the evolving skill set and competencies required for data journalism and their adoption pattern by winners of the Data Journalism Awards, and identified a set of core skills, technologies, and tools that appear central to award-winning data journalism practices.…”

“…Comparing these figures with the originating countries of the winning cases of the Data Journalism Awards (Ojo and Heravi 2018) provides an interesting insight. There were 44 winning cases at the Data Journalism Awards between 2013 and 2016, originating from 14 different countries.…”

Hybrids, materiality, and black boxes: Concepts of actor‐network theory in data journalism research

“…Hitherto, most analyses of data journalism artefacts resorted to descriptive accounts of chart types and degrees of interactivity or data providers. Most of these endeavours were either standalone quantitative analyses (see Appelgren, 2018;Knight, 2015;Lowrey & Hou, 2018;Ojo & Heravi, 2017;Reimer & Loosen, 2017;Stalph, 2017;Tandoc & Oh, 2017;Young, Hermida, & Fulda, 2018) or integrated within qualitative designs (see De Maeyer et al, 2015;Parasie, 2015;Parasie & Dagiral, 2013;Tabary, Provost, & Trottier, 2016;Young & Hermida, 2015;Zhang & Feng, 2018). Few of these studies, however, take into account sociomaterial underpinnings of such artefacts.…”

3Ws of Data Journalism Education

“…These include studying nominees or winners of various awards and competitions such as the Data Journalism Awards, e.g. (Loosen et al 2017;Ojo & Heravi 2017;Young et al 2017), or studies of specific news organisations data journalism and data visualisation practices, e.g. (Stalph 2017).…”