Module 1: The Foundations of Data Literacy

Learning Objectives

• Define data literacy and explain its key components.
• Understand the growing importance of data literacy in education and professional life.
• Identify strategies for developing and strengthening data literacy skills.

What is Data Literacy?

Data literacy can be defined as the ability to read, understand, utilize, ethically evaluate, and communicate data and data-informed insights [1].

In our modern data and information-rich environment that is ruled by large language models and misleading data interpretations, this skill set is not only essential for researchers and data scientists but also students, administrators, and all members of the public. Data literacy prepares members of the public to engage with the various forms of data that shape decisions in education, business, healthcare, public policy, and modern everyday life. In order to be considered “data literate,” one may be expected to possess the ability to:

• Interpret data within charts and tables
• Evaluate the reliability of data sources
• Draw meaningful insights from datasets
• Understand ethical considerations
• Communicate data findings clearly

Data literacy requires both technical skills, such as the ability to interpret common data visualizations and use basic analytical tools, as well as cognitive abilities like questioning assumptions, recognizing bias, and applying an ethical judgment [2].

Why is Data Literacy Important?

Data increasingly drives major decisions in all facets of our lives. Our ability to understand and use data responsibly is necessary to distinguish between credible information and mis/disinformation, make informed decisions, and correctly interpret trends and findings in learning, professional, and daily-living contexts. Several recent studies have shown that competency in data literacy concepts is a strong predictor of subjective well-being [3].

Within the higher education context, data literacy is critical for supporting student learning outcomes as well as enhancing the quality of faculty research, informing institutional planning, and guiding assessment. This skill set enables students to think critically and analytically in all disciplines, not just those that are data-intensive. Among university faculty, staff, and administrators, these skills promote evidence-based decision-making that enhances transparency and drives efforts for continuous improvement.

Data literacy is also vital in empowering individuals to participate more fully in a democratic society. The abilities to read tables, figures, and charts in news stories or understand health trends and election data are fundamental to developing citizens that are engaged in our civic discourses and advocacy. It is necessary for us to develop skills in our students that enable them to be not just passive consumers of data in their world, but active and engaged participants in the data ecosystem.

Becoming Data Literate

It is a popular misconception (perhaps owing to the name of “data literacy”) that one must master complex statistics or be a master coder in order to become data literate. While those can be useful skills for anyone to possess, data literacy focuses much more on developing the skills necessary to understand data when it is presented and communicate the significance of data-based findings to others. You can become data literate through engaging with the following concepts and activities:

• Understanding the basics of data: This research guide from Tulane University discusses the basics of what data is and its various forms. This will also be discussed further in our Data and Data Sources module.
• Interpreting data visualizations: This research guide from Yale University explains the uses and distinctions among various types of data visualizations. This will also be explored further in our Making Data Insightful and Actionable module.
• Drawing conclusions from data: This learning resource from Insight7 describes the process of turning data into actionable insights. This will also be explored further in our Making Data Insightful and Actionable module.
• Communicating data-informed findings: This informational article from the Pragmatic Institute discusses the importance and process of communicating data insights to non-data professionals. This will also be explored further in our Making Data Insightful and Actionable module.
• Engaging in ethical and critical thinking: This research guide from North Carolina A&T University presents data ethics from the perspective of a research library. This will also be explored further in our Managing Data Responsibly and Ethically module.

In order to enhance data literacy on your campus, it is important to first become data literate yourself. This does not require a formal education or degree. Just informal learning and practice through toolkits like this one can prepare you with the skills you need to be a data literacy instructor and advocate!

Case Example

The University of South Florida-St. Petersburg engaged in the creation of a series of data literacy workshops and initiatives to build on existing, successful information literacy programs. The outcomes of these programs for enhancing data literacy skills is discussed in a 2020 article by Burress et al. [4]. This program was driven by an interdisciplinary and cross-campus collaboration to expand data literacy skills that could be applied across disciplines. The initiative centered on a framework for data literacy that discussed data in all its forms but particularly emphasized research data skills. The success of this program allowed it to be integrated into the general education curriculum, engaging hundreds of students each year in data instruction.

Reflection Activity

In a notebook or digital document, reflect on:

• What does data literacy mean to you?
• Think of a recent encounter with data—how confident were you in interpreting it?
• Which data literacy skills do you feel confident in?

Then, create an action plan with specific steps and a timeline to improve your data literacy skills.

Summary

The module has introduced the essential concepts of data literacy and their significance and practical applications in higher education contexts. From this content, we have learned that data literacy does not mean being a data scientist or programmer but rather understanding how to interpret, evaluate, and communicate data ethically and effectively. From this introductory understanding of data literacy, we can now proceed to discuss how these principles can become integrated into your campus and instructional activities.

Additional Resources

• IBM Technology: What is Data Literacy (video)
• American Library Association: Data Literacy Guide
• Kim, Hong, & Evans (2024): Toward Measuring Data Literacy for Higher Education

References

1. Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30-37.
2. Kim, J., Hong, L., & Yoon, A. (2025). University students’ self-assessment of data literacy: A validation study. PLoS One, 20(4), e0322104.
3. He, C. et al. (2025). Impact of digital literacy on rural residents’ subjective well-being: An empirical study in China. Agriculture, 15(6), article 586.
4. Burress, T. et al. (2020). Exploring data literacy via a librarian-faculty learning community: A case study. The Journal of Academic Librarianship, 46(1), article 102076.