Patterns in Award Winning Data Storytelling: Story Types, Enabling Tools and Competences [review]

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Source: Adegboyega Ojo & Bahareh Heravi (2018)

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.

Keywords data-driven journalism; data journalism skills; data journalism tools; data story; data storytelling types; Global Editor Network

Introduction

Data journalism is an aspect of contemporary journalism in which techniques such as data analytics, programming and narrative visualisation are employed in additional to traditional journalistic methods to create data stories (Appelgren and Nygren 2014).

Data stories are artefacts for revealing and communicating insights gained from the analysis of data-sets obtained from the public domain, crowdsourcing or big data sources. Data storytelling (i.e. the practice of creating data stories) is a structured approach comprising data, visuals and narratives for communicating insights from data (Dykes 2016). The object of developing data stories is to give voice to the data to inform, explain, persuade or engage the target audience (Slaney 2012).

Despite the rapidly growing popularity of data journalism (Hewett 2015) and its adoption by large media organisations such as The Times, The Washington Post and The Guardian (Segel and Heer 2010), scholarly publications on data storytelling are limited. For instance, a search on Google Scholar for the term “data storytelling” in early July 2016 returned only 202 documents.

The same search returned 316 documents in April 2017, which illustrated the growing interest in the field in the previous year. A similar Google Scholar search on “data journalism” returned 2910 documents in April 2017, 639 of which are produced since the beginning of 2016. Similarly, a search on the Scopus bibliographic database in April 2017 returned only 13 documents listed as containing the phrase “data storytelling” in their titles, abstracts or keywords and 83 documents for those mentioning “data journalism”.

This compares to more than 68,000 publications returned by Google Scholar using the search terms “data analytics” and 148,000 for “data science” and just over 5000 documents and 1300 documents on Scopus, respectively. Thus, roughly 0.5–4 per cent of the research attention in data analytics and science is devoted to data journalism and data storytelling—arguably one of its most valuable aspects.

Despite this paucity, there are a few notable publications on data storytelling (Segel and Heer 2010) and (Lee et al. 2015) which identify core elements and rigorously describe the design space for data stories and narrative visualisations. Other works have attempted to prescribe good data story practices (Alexander and Vetere 2011) and analysed concrete data storytelling practices (Pouchard, Barton, and Zilinski 2014). In addition, few practitioner-directed articles such as (Stikeleather 2013) that have sought to contribute good storytelling practices.

The Global Editors Network (GEN) a cross-platform community for editors and media innovators (GEN 2016) has recognised outstanding practice in Data Journalism since 2012. The winning entries are presented on their community portal (community.- globalnetwork.org). In our opinion, this repository of good practice constitutes an invaluable source of information for deconstructing data storytelling to produce more systematised knowledge about options for developing different types of data stories.

Source: Adegboyega Ojo & Bahareh Heravi (2018). Incomplete view of Table 3 from the referred article.

One of the first steps in undertaking this challenge is to develop a conceptual framework for characterising data storytelling. Such a framework should enable the user to answer basic journalistic questions about the data story cases regarding “who, what, where, why, when and how” (5W-1H) from the resulting knowledgebase. In a recent work Young, Hermida, and Fulda (2017), the authors analysed the nature and quality of the subset of all Canadian finalists and winners in this repository between 2012 and 2015.

This study provides complementary analysis of GEN winning entries with the goal of better understanding the nature or type of data stories in the repository and how different technologies are being combined to create data stories. To this end, we developed a conceptual framework based on extant literature and then applied the framework to describe 44 data storytelling cases recognised as outstanding in Data Journalism (DJA) Award from 2012 to 2016.

The resulting repository of cases was analysed using a multi-case approach (Baxter and Jack 2008) and content analysis. Findings from our work refine the traditional “typology of intent” of data stories in particular inform and explain (Slaney 2012) from the journalistic perspective. The findings also provide a “Data Journalism Technology Competency Architecture” for training and development of the future data journalist, configuration of teams working and dynamics in contemporary and future newsrooms.

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Access to article

https://doi.org/10.1080/21670811.2017.1403291

Reference

Adegboyega Ojo & Bahareh Heravi (2018) «Patterns in Award Winning Data Storytelling: Story Types, Enabling Tools and Competences», Digital Journalism, 6:6, 693-718, DOI: 10.1080/21670811.2017.1403291

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