By Lilian H. Hill
Data literacy is a fundamental skill set that entails the ability to read, write, understand, and communicate data in context effectively. It empowers individuals and organizations to derive meaning from data, make informed decisions, and solve problems. Data literacy is an interdisciplinary competency that integrates elements of mathematics, science, and information technology. Data literacy requires understanding data sources and constructs, analytical methods, and AI techniques (Stobierski, 2021). Data literacy is not about being a data scientist; it's about having a general understanding of data concepts and how to apply them effectively.
The rapid expansion of digital information in today’s world has triggered a significant shift in how knowledge and skills are valued, making the ability to understand, interpret, and extract meaningful insights from data a vital competency. Schenck and Duschl (2024) comment that data increasingly drive decisions across all sectors of society, and promoting data literacy has become essential to preparing individuals to participate actively and thoughtfully in the digital age. In education, this changing environment calls for a reimagined approach that goes beyond conventional literacies, positioning data literacy as a core skill necessary for future success.
Skills of Data Literacy
Building data literacy skills is an essential process in today’s data-driven world. It begins with learning the fundamentals of data, including understanding different types such as quantitative versus qualitative data, and recognizing basic statistical concepts like mean, median, standard deviation, and correlation. Familiarity with common data formats (e.g., CSV, JSON, Excel files) lays the groundwork for deeper analytical work (Mandinach & Gummer, 2016). Introductory courses from platforms like Coursera or edX, as well as open-access tutorials and videos, offer accessible entry points for building this foundational knowledge.
To apply data literacy practically, individuals should become familiar with commonly used tools. Beginners might start with spreadsheets like Microsoft Excel or Google Sheets to learn basic data manipulation and chart creation. As comfort grows, they can explore more advanced platforms such as Tableau or Power BI for data visualization or learn coding languages like Python (using libraries such as Pandas) and SQL for deeper analysis. Practicing with real-world data available from open sources like government portals or World Bank Open Data helps bridge theory and application.
A crucial next step is learning to interpret data visualizations. Charts, graphs, and dashboards are the primary means of communicating data, and understanding how to read them critically is crucial for avoiding misinterpretation. Tools such as Gapminder or data stories from Our World in Data provide engaging ways to practice understanding patterns and trends visually (Knaflic, 2015).
Equally important is the development of critical thinking skills about data itself. This means asking questions such as: Where did the data come from? Is the sample size sufficient? Is there potential for bias or missing information? Cultivating skepticism and inquiry when reviewing data sources helps prevent the spread and influence of misinformation (Bhargava et al., 2021).
Communication is another fundamental part of data literacy. It’s not enough to understand data. The ability to clearly and ethically explain insights is equally important. This involves selecting appropriate visuals, simplifying complex ideas, and telling compelling data-driven stories (Knaflic, 2015). Platforms like Flourish or Datawrapper can help users experiment with design and narrative techniques that enhance data communication.
Ultimately, data literacy must be maintained and continually updated through ongoing learning. Schenk and Duschl (2024) call for a transformative change in educational practices, recommending a move away from formal, theory-first instruction toward contextual, inquiry-based learning. This change is viewed as crucial for equipping students with the practical skills necessary to apply data literacy effectively in real-world situations. Data literacy is not only a technical skill but also a civic and ethical one, enabling people to make informed decisions and engage in democratic processes.
Data Literacy and Social Justice
One of the core connections between big data analytics and data literacy lies in the ability to manage and critically evaluate the quality and relevance of data. Big data involves massive, unstructured datasets sourced from sensors, social media, transactional records, and more. This can introduce biases, inconsistencies, and privacy risks. Data-literate individuals are better equipped to ask critical questions: Where does the data come from? Is it representative? What algorithms are being applied? Who might be harmed by this analysis? These questions are especially important in fields like healthcare, criminal justice, education, and marketing, where big data can amplify existing societal inequities if not interpreted responsibly (boyd & Crawford, 2012).
Data justice aims to ensure that data practices do not perpetuate or exacerbate structural inequities and social injustices, but instead promote human rights, dignity, and democratic participation (Dencik & Sanchez-Monedero, 2022). The increasing dependence on data-driven technologies in all aspects of social life is a driving force behind major shifts in science, government, business, and civil society. While these changes are frequently promoted for their potential to improve efficiency and decision-making, they also introduce profound societal challenges. Data justice refers to the fair and equitable treatment of individuals and communities in the collection, analysis, use, and governance of data. It emphasizes that data are not neutral. How data are gathered, interpreted, and applied often reflect existing power structures, biases, and inequalities. Data justice has emerged as a critical framework for addressing these challenges through a lens centered on social justice. For example, if a predictive policing algorithm unfairly targets neighborhoods based on biased crime data, it may lead to over-policing in communities of color. A data justice approach would question the assumptions behind the data, advocate for community oversight, and explore alternative models that prioritize community safety without reinforcing systemic bias.
Finally, data literacy supports democratic participation in a big data society. As governments and corporations increasingly rely on data to guide decisions, including pandemic response, urban planning, and surveillance, citizens need the skills to engage with data-related policies, challenge unfair uses, and advocate for transparency and accountability. Without broad-based data literacy, power becomes concentrated in the hands of a few data-literate experts and institutions, potentially reinforcing social and economic inequalities (D’Ignazio & Klein, 2020).
References
Bhargava, R., Kadouaki, R., Bhargava, E., Castro, G., & D’Ignazio, C. (2021). Data murals: Using the arts to build data literacy. The Journal of Community Informatics, 17(1), 1–15. https://doi.org/10.15353/joci.v17i1.4602
boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
Dencik, L., & Sanchez-Monedero, J. (2022). Data justice. Internet Policy Review, 11(1). https://doi.org/10.14763/2022.1.1615
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.
Jones, B. (2025). Data literacy fundamentals: Understanding the power and value of data (2nd ed.). Data Literacy Press.
Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.
Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for educators: Making it count in teacher preparation and practice. Teachers College Press.
Schenck, K. E., & Duschl, R. A. (2024). Context, language, and technology in data literacy. Routledge Open Research, 3(19).
(https://doi.org/10.12688/routledgeopenres.18160.1)
Stobierski, T. (2021). Data literacy: An introduction for business. Harvard Business Review Online. https://online.hbs.edu/blog/post/data-literacy
Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2). https://doi.org/10.1177/2053951717736335
