An Exploration of the Definition of Data Literacy in the Academic and Public Domains

Introduction

The digital transformation of economies world-wide is well underway. Data literacy is one of the most important components of the digital literacy necessary to “realize the full potential of digital infrastructure” and “excel the economic growth…[data literacy] is a skill useful not only for daily use but also for work-related tasks” (Damuri et al., 2022, p. 33). These are the views expressed in the “G20 Toolkit for Measuring Digital Skills and Digital Literacy: Framework and Approach” (Damuri et al, 2022). Similar views are expressed in the UNESCO 2018 Digital Literacy Global Framework and the Digital Literacy Index by the Indonesian Communications and Information Ministry 2020 (Damuri et al., 2022). Despite the acknowledged importance of data literacy, needed data literacy capabilities in organizations are not being met. Alarmingly, only 21% of more than 9000 employees surveyed by Accenture were confident of their data literacy skills, and “many developing countries are struggling to improve their data literacy skills” (Damuri, et al., 2022, p. 33).

Data literacy is imperative for organizations, but organizations are grappling with a widening data literacy gap (Forbes Councils, 2019). In a highly competitive, globalized economy organizations depend on data for decision-making, and for businesses to take advantage of new business intelligence techniques in machine learning and AI, businesses must develop a strong data literacy culture (Johnson, 2019). According to Johnson (2019), around 50% of organizations lack the necessary “AI and data skills to achieve business value”. The growing urgency to develop data literacy is documented widely in literature (e.g., Grillenberger & Romeike, 2018; Gummer & Mandinach, 2015; Kjelvik & Schultheis, 2019; Ridsdale et al., 2015; Wolff et al., 2016).

Data literacy is unquestionably important. But what is data literacy? Definitions of data literacy are plentiful but there is no single agreed upon definition. Bhargava et al. mentioned “Despite data literacy’s growing popularity as a much-needed “bottom-up” solution, data literacy is ill-defined or ambiguous at best” (Bhargava et al., 2015). The authors recognize that understanding of data literacy is necessarily varied. Hence the authors’ aim is not to find or present a singular definition of data literacy. Rather the aim is to explore the various conceptualizations of data literacy in two domains: the academic (academic peer-reviewed journals) and the public domain (industry, organizational sites, and non-academic blogs/Wikis etc). The line of argument that provides a rationale for exploring the definitions of data literacy in the academic and public domain is as follows:

• 1.

  Definitions are important because they represent our conceptual understanding of a topic and provide the necessary common language for analysis and discussion (Podsakoff, MacKenzie, & Podsakoff, 2016).

• 2.

  One source of talent to help organizations fill the data literate talent must be university graduates (Winterberry, 2018; New Vantage Partners, 2019; Pothier, 2019; Panetta, 2021). But, despite no shortage of graduates, organizations are unable to fill the required data-literate talent.

• 3.

  There is therefore an apparent gap between the data literacy expectations/needs of organizations and the data literacy capabilities that graduates develop during their university education (Bersin & Zao-Sanders, 2020; Forbes Councils, 2019).

• 4.

  Since definitions are important (because they represent conceptual understanding which in turn forms a basis for action) an exploration and comparison of data literacy definitions in the academic and public domain provides some insight into why the disconnect between university education and business exists. These insights can provide a launching point for improving educational approaches to bring data literacy education of graduates closer to the expectations of the organisation.