Data Literate Talent

Cultivating a Data Literate Workforce: Considerations for Librarians Wendy Pothier (bio) and Patricia Condon (bio) Introduction An estimated 1.7 megabytes of data are generated per second per person on earth.1 Given the rapid growth of data creation, increasing automation, and expanding daily interactions with data, our collective need to become more data literate is imperative. As Sara Brown points out, “The tasks humans do often require judgment, which is improved by data literacy.”2 Academic librarians have long engaged with information literacy to help students prepare for success in both personal and professional pursuits. While librarians have not always found alignment with how the term information literacy is understood outside our profession, data literacy presents a different opportunity. The term data literacy has already been widely adopted in corporate and industry workplaces, as showcased through articles in many top business news and magazine venues. Data literacy is a rapidly evolving qualification in the workplace and, more generally, a broad organizational need. Companies have an obvious stake in fostering a data literate workforce as it creates a competitive advantage in doing business—the better a company can utilize data, the more power it can wield. Projections by Forrester Consulting, a global market research company, suggest that nearly 70 percent of the workforce would be expected to use data heavily in their work by 2025.3 Decision-makers understand that data literacy skills are a requirement for their employees, with 82 percent stating that they expect basic data literacy skills from all workers in their departments.4 However, Rasheed Sabar, the chief executive officer of Correlation One, notes that companies struggle to strengthen data literacy within their organizations5 and that the data literacy skills gap continues to expand.6 As companies increasingly adopt automation and artificial intelligence to tackle the growing volume of data, it is essential for data literacy to be demonstrated at all levels throughout the organization.7 Librarians have expressed interest in teaching data literacy but hope for partnership instead of taking on the work alone.8 Interest in the development of data literacy skills is [End Page 629] far ranging and includes both individuals and organizations. Higher education institutions, research organizations, libraries, industries, businesses, and governments all have a stake in the development of data literacy skills in the modern workforce. Individual students, alumni, faculty, campus administrators, managers, executives, employees, and, of course, librarians are some of the roles identified with particular interest. With a broad and varied range of relevant groups and individuals, it is important to understand their respective roles, perspectives, and the value they place on data literacy as an essential workplace skill. It is also valuable to examine the interactions between the relevant groups to understand tensions that may surround the conversations on data literacy. Understanding the current state of data literacy education and workforce training can guide and inspire librarianship’s response, with the aim of helping students develop needed data literacy skills for their career success. Data Literacy Education and Training Since most employees deal with data in some form in their work, development of data literacy skills will require attention from academic institutions and business organizations alike. Tension surrounds, however, the question of who is responsible for providing data literacy education and training—higher education, companies, or individual employees. Some note that higher education has not been responsive in this area.9 As reported in a 2021 study, higher education literature fails to address data literacy in business and workplace settings despite large-scale studies by market research firms such as Gartner and Accenture that express the strong need for these skills.10 A Harvard Business Review article notes that the slow response by higher education forces the organizations to take the lead for employee education and training.11 When students and alumni lack needed data literacy skills, they face challenges that will affect their personal and professional lives. A 2020 report by the consulting group Accenture found that only 25 percent of employees surveyed felt fully prepared to use data effectively when entering their current role.12 Additionally, the report found that 74 percent admit feeling overwhelmed or unhappy when working with data.13 Employees can...

The recent global business mania around adoption of Generative Artificial Intelligence (GenAI) has amplified interest in organizational data literacy efforts in industry. Data literacy, when scoped clearly and comprehensively, represents a spectrum inclusive of business, data and analytical acumen (inclusive of AI), as well as degrees of capability (literacy, fluency and mastery). In the face of the well-known change management challenges, we believe that current discussion focused on the executive suite is not sufficient and must extend to the primary execution arm, which is the middle management layer in most organizations.

This study aimed to develop a data literacy content framework for elementary school students to establish a foundation for systematic data literacy education. The research was conducted through a literature review and analysis of the 2022 Revised National Curriculum of Korea. Based on Ridsdale et al. (2015)'s framework, components of data literacy suitable for elementary students were derived, and curriculum achievement standards related to data literacy were analyzed to develop the content framework. The research identified eight key components of data literacy: understanding data, data collection, data evaluation, data organization, data analysis, data visualization, data-driven decision-making, and data ethics. Curriculum analysis revealed that science (36.3%) and social studies (32.7%) subjects contained the highest proportion of data literacy elements, with grades 5-6 (63.2%) including more achievement standards than grades 3-4 (36.8%). The developed framework is categorized into three domains: knowledge and understanding, processes and skills, and values and attitudes. It considers grade-level hierarchy by focusing on basic concepts and simple functions for grades 3-4, while emphasizing complex concepts and higher-order functions for grades 5-6. This study contributes to supporting systematic data literacy education in elementary schools by providing a content framework that considers students' cognitive developmental stages and is expected to foster future core competencies through practice-centered education. Further research is needed to verify practical applicability, develop teaching and learning methods, and strengthen connections between school levels.

PurposeThis research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).Design/methodology/approachThe study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.FindingsThe findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.Research limitations/implicationsBeyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.Originality/valueThis research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

Data literacy is a new requirement for teachers in the era of big data, and the data literacy of county secondary school teachers is one of the key links in promoting the digital transformation of basic education. Through a questionnaire survey on the status of data literacy of 187 secondary school teachers in 10 counties and cities in Huanggang, Hubei, the study found that: the overall level of data literacy of secondary school teachers in Huanggang county was average, and the development level of each dimension was poor; the level of teachers' data literacy was significantly and positively correlated with the number of training received; teachers' data literacy was significantly and positively correlated with each dimension and between dimensions. In view of the problems, the following policy recommendations were made to improve the data literacy of teachers in county secondary schools: data literacy training should be incorporated into the performance evaluation system of teachers in county secondary schools; the content of data literacy training for teachers in county secondary schools should be "three-dimensional and progressive"; the informatization of education management of data literacy training for teachers in county secondary schools should be implemented; Make full use of the new technology to improve the data literacy effect of county middle school teachers.

This study focuses on an important yet often neglected topic in public personnel competency studies: competencies required for digital government. It addresses the question: Which competencies do civil servants need for data-driven decision-making (DDDM) in local governments? Empirical data are obtained through a combination of 12 expert interviews and 22 Behavioral Event Interviews. Our analysis shows that DDDM as observed in this study is a hybrid process that contains elements of both “traditional” and “data-driven” decision-making. We identified eight competencies that are required in this process: data literacy, critical thinking, teamwork, domain expertise, data analytical skills, engaging stakeholders, innovativeness, and political astuteness. These competencies are also hybrid: a combination of more “traditional” (e.g., political astuteness) and more “innovative” (e.g., data literacy) competencies. We conclude that local governments need to invest resources in developing or selecting these competencies among their employees, to exploit the possibilities data offers in a responsible way.

Despite receiving increased attention from researchers in mathematics education, there is still no comprehensive understanding of the current level of data literacy in the teaching and learning of mathematics. To address this gap, this study pre­sents a review of 247 papers selected from the Sco­pus database between 2009 and 2024. The research aims to explore the following: (i) The overall vol­ume, geographic distribution, and development tra­jectory in the literature on data literacy in mathe­matics. (ii) The researchers and research collabora­tions that have had the greatest influence on the literature on data literacy in mathematics. (iii) The sources that have had the greatest influence on the literature on data literacy in mathematics. (iv) The most important topics in the literature on data literacy in mathematics. It was discovered that the number of publications involving data literacy in mathematics increased from 2016 to 2023. Authors from the Netherlands are the most active in the literature on data literacy in mathematics. The Teacher College Record had the highest number of citations. Lastly, the most important topics addressed in the literature on data literacy in mathematics were data use, data literacy, and data-based decision-making. This study has implications not only for mathematics education researchers but also for other stakeholders in the education sector, including school principals, policymakers, and mathematics teachers.

Data-driven decision-making (DDDM) has become a foundational strategic capability for modern business organizations, enabling evidence-based choices that enhance performance, competitiveness, and innovation. This review synthesizes contemporary academic and industry research on the mechanisms, enablers, and outcomes associated with DDDM, drawing from 25 peer-reviewed sources. The study examines the technological underpinnings of DDDM—including big data analytics, artificial intelligence, machine learning, cloud platforms, and business intelligence systems—and analyzes their integration into organizational processes. It evaluates cultural and structural factors such as data literacy, leadership support, analytical capability, and data governance. A comparative table contrasts traditional decision-making with DDDM frameworks, highlighting differences in speed, accuracy, scalability, and risk management. The analysis reveals that organizations adopting DDDM achieve improvements in operational efficiency, customer insights, innovation rates, and strategic agility. The paper concludes by addressing future challenges—including data ethics, algorithmic transparency, talent shortages, and security risks—while proposing pathways for maximizing the value of data-driven strategies.

PurposeThe purpose of this study is to explore open data portals as data literacy learning environments. The authors examined the obstacles faced and strategies used by university students as non-expert open data portal users with different levels of data literacy, to inform the design of portals intended to scaffold informal and situated learning.Design/methodology/approachThe authors conducted an observational user study, in which 14 student participants grouped by self-reported data literacy measures carried out assigned tasks in an open data portal. Data were collected through screen capture, think-aloud protocols and post-session interviews.FindingsParticipants experienced numerous challenges in finding and using data, with some variation shown between the different literacy groups. The higher data literacy group primarily faced challenges using unfamiliar tools, which may be addressed by improving system usability, while the lower data literacy group struggled due to gaps in basic understanding, which may be addressed by increasing point of need instruction and guidance. Participants used several learning strategies but primarily relied upon trial and error, which was less effective for low data literacy users.Originality/valueThis study is unique in comparing open data portal use among adult students across data literacy levels through an empirical user study. It contributes methodologically by proposing an instrument for data literacy assessment. It offers a novel perspective on information systems as sites for informal learning and skills development, beyond the immediate goals of system use, and offers concrete suggestions for the future design of open data portals for students and non-expert, citizen users.

The key technology in the 4th Industrial Revolution era is related to data. Data literacy, which can uncover various problems from raw data, interpret meanings, lead to more effective communication, and allow for people to make more rational decisions, is discussed as an essential skill in modern society. In this context, this study was conducted to consider the need for data literacy education, particularly in the field of liberal arts education, in order to validate a measurement tool for college students, to analyze the data literacy level of freshmen, and to provide implications for the future curriculum. Data were collected from 3,510 freshmen from A University in the metropolitan area, and the validity of the diagnostic tool was verified through exploratory factor analysis and confirmatory factor analysis. As a result, 19 items were finalized, and the instrument showed good reliability and validity. Following this, the level of data literacy of freshmen was analyzed with descriptive statistics, and a t-test and ANOVA were used to see if there was a significant difference in data literacy according to gender and colleges. The results showed that the data literacy of freshmen was not high, and the results were particularly low in data analysis and the utilization of the data analysis tool. There was no significant difference between genders, but there were significant differences among colleges. Data literacy of students in the arts, physical education, the humanities and the social sciences was lower than those of students in the natural sciences and engineering. Based on these findings, the necessity of data literacy education was discussed in liberal arts education, and suggestions were made to develop data analysis capabilities using certain tools.

Data drives today's world; hence, acquiring data literacy is essential for academic and professional success. However, students entering the university possess uncertain levels of data literacy skills. Therefore, this study assesses the perceived data literacy possessed by first-year university students in South Africa. The study adopted the cross-sectional survey research design, with a sample of 872 students across South African universities. A validated questionnaire with a reliability index of 0.96 was used for data collection. Descriptive statistics were used to answer the research question, while Analysis of Variance (ANOVA) was used to test the hypotheses at 0.05 significance level. The results show that the students had basic data literacy skills on graphs and types of data. However, they lack familiarity with the concept of visualization, communicating findings from data analysis, the concept of variables and observations. Overall, the students had a medium level of data literacy. Age and access to data significantly influence students' data literacy ratings. However, no significant differences were found among the different sexes. It was recommended that educational institutions integrate comprehensive data literacy programs for students, ensure equitable internet access, and provide continuous professional development programs for teachers.

Growing digitisation has made data ownership an important focus for institutions and students. Broadly, there are three issues, which require urgent attention to obtain data privacy, literacy, and utilisation. First, schools globally lack necessary frameworks for data interoperability, while maintaining privacy and control. Second, the scale, source, and nature of school data make its interoperability impractical, resulting in an inability to assess the true impact of educational technologies on learning. Third, while data supports teaching, an increasingly data-driven decision-making suggests that student participation in curriculum design becomes secondary. Finding a balance between data-driven decision-making and student voice is critical for an efficient school ecosystem. In this paper, we introduce a proof-of-concept for Kratos: an immutable decentralised data management system that enables privacy, applied data literacy while empowering students with a user interface for data governance and active participation in school processes.

This article examines the transformative impact of Data-Driven Decision Making (DDDM) on enterprise systems, focusing on its implementation, challenges, and organizational implications. The article explores how organizations leverage advanced analytics, machine learning, and artificial intelligence to enhance operational efficiency and strategic planning through systematic data utilization. This article presents a comprehensive framework for successful DDDM implementation in enterprise environments by analyzing the transition from traditional decision-making approaches to data-driven methodologies. The article addresses critical challenges, including data quality concerns, integration complexities, and organizational resistance, while highlighting the importance of robust data governance and cultural transformation. Furthermore, the article investigates the role of continuous feedback mechanisms and KPI monitoring in fostering organizational agility and informed decision-making across all hierarchical levels. The findings emphasize the significance of employee training, data literacy, and strategic infrastructure development in creating sustainable competitive advantages through DDDM adoption. This article contributes to the growing body of knowledge on enterprise system transformation. It provides practical insights for organizations seeking to enhance their decision-making capabilities in an increasingly data-driven business landscape.

Data use, whether through traditional methods in education or more sophisticated techniques such as learning analytics and educational data mining, has emerged as an important part of educational practice. Foundational to the use of data is data literacy; that is, educators’ ability to use data effectively and responsibly. A construct called data literacy for teachers has been operationalized and differs from assessment literacy to include the many diverse sources of data that educators now encounter. However, an issue, even with traditional data use is the extent to which educators have sufficient data literacy. The introduction of learning analytics presents the need for even more sophisticated data use capacity that may or may not be practical in most K-12 educational settings. This chapter explores the intersection of data literacy and learning analytics, and in doing so draws parallels between data use in the K-12 and post-secondary education settings, where data-driven decision making and learning analytics have traditionally been positioned. It provides a review of data literacy and the technologies that support data use. It discusses the practical challenges and constraints to transforming more traditional data use to include learning analytic strategies and how data literacy applies. The chapter then looks toward the opportunities and possibilities made possible by the sophisticated data use in learning analytics.

The research on the index system construction and model analysis of data literacy ability of university teachers has influenced the development of academic field, and the rapid development of information digitization technology has brought about a torrent of data. University teachers’ life, work and learning are profoundly affected by the big data environment, and the level of data literacy of university teachers will play a vital role in the development of information society. On this basis, the index system of teachers’ data literacy is introduced. The effectiveness of the data literacy model of university teachers will become an important index. According to the analysis results, the fuzzy comprehensive evaluation method is used to determine the index weight of teachers’ data literacy ability and the hierarchical model is established to analyze it. The scale of the ability index is calculated, and the weight of the teacher’s data literacy index is determined. The experimental results show that the proposed method is accurate and effective.