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Data literacy

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Short description: Transformation of data to understandable forms

Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data.[1] It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data.[2]

Data literacy refers to the ability to understand, interpret, critically evaluate, and effectively communicate data in context to inform decisions and drive action. It is not a technical skill but a fundamental capability for everyone, encompassing the skills and mindset necessary to transform raw data into meaningful insights and apply these insights within real-world scenarios.[3]

In recent years, data literacy has become recognized as a vital skill for everyone, not just data scientists or researchers. As data shapes more aspects of daily life: like social media, online shopping, and personal decision making, being able to interpret and use data helps people make smarter choices and engage in a data driven world.[4]

Background

As data collection and data sharing become routine and data analysis and big data become common ideas in the news, business,[5] government[6] and society,[7] it becomes more and more important for students, citizens, and readers to have some data literacy. The concept is associated with data science, which is concerned with data analysis, usually through automated means, and the interpretation and application of the results.[8]

The idea of data literacy has changed over time, first gaining attention in academic and professional settings before spreading to everyday life.[9] The rise of digital technology, access to large volumes of data, and the growing necessity for critical decision making have all contributed to its importance.[4] Today, data literacy matters in school, at work, and even in daily routine, helping explain why its become such an important and relevant topic.[4]

Data literacy is distinguished from statistical literacy since it involves understanding what data means, including the ability to read graphs and charts as well as draw conclusions from data.[10] Statistical literacy, on the other hand, refers to the "ability to read and interpret summary statistics in everyday media" such as graphs, tables, statements, surveys, and studies.[10]

Practical framework

Recent work has shown the value of using practical framework to develop data literacy. The OODA (Observe, Orient, Decide, Act), is an example, which originated in military strategy is now used to teach data driven thinking and decision making in various settings.[9] These frameworks help people use data literacy to solve real world problems and make informed choices, making the concept more practical and useful.[11]

Workplace and everyday life

Employers across many fields are seeking candidates who are comfortable working with data.[12] Data literacy is increasingly valued in the workforce, influencing hiring and training practices.[13] Universities and colleges are responding by developing new courses, curricula, and programs focused on building students data literacy skills, preparing them for careers in a data driven economy.[12]

Data literacy is not important only in academic or professional environments; it is also relevant for daily life. People use data skills when planning trips, comparing products online, filtering travel options, or checking personal app statistics.[11] These real world examples show how data literacy helps individuals make more informed decisions and navigate digital platforms.[11]

Datafication

The process of datafication, which is turning more aspects of life into data, has wide reaching effects. Data literacy now means understanding how data shapes online experiences, like algorithmic recommendations, targeted ads, and feedback loops.[14] It also involves awareness of privacy risk, bias in data, and broader social and ethical questions raised by the increased reliance on data for decision making.[9]

Role of libraries and librarians

As guides for finding and using information, librarians lead workshops on data literacy for students and researchers, and also work on developing their own data literacy skills.[15]

A set of core competencies and contents that can be used as an adaptable common framework of reference in library instructional programs across institutions and disciplines has been proposed.[16]

Resources created by librarians include MIT's Data Management and Publishing tutorial, the EDINA Research Data Management Training (MANTRA), the University of Edinburgh's Data Library and the University of Minnesota libraries' Data Management Course for Structural Engineers.

See also

References

  1. ↑ Acker, Amelia; Bowler, Leanne; Pangrazio, Luci (2024). "Guest editorial: Special issue – perspectives on data literacies". Information and Learning Sciences 125 (3/4): 157–162. doi:10.1108/ILS-03-2024-266. 
  2. ↑ Baykoucheva, Svetla (2015). Managing Scientific Information and Research Data. Waltham, MA: Chandos Publishing. p. 80. ISBN 978-0-08-100195-0. 
  3. ↑ Hanegan, Kevin (January 10, 2021) (in English). Turning Data into Wisdom: How We Can Collaborate with Data to Change Ourselves, Our Organizations, and Even the World. Kevin Hanegan. pp. 31, 232. ISBN 978-0-578-63987-1. 
  4. ↑ 4.0 4.1 4.2 Getz, Kelly, ed (2022). The data literacy cookbook. Chicago: Association of College and Research Libraries. ISBN 978-0-8389-3925-3. 
  5. ↑ Hey, A. J., ed (2009). The fourth paradigm: data-intensive scientific discovery.. Microsoft. 
  6. ↑ "Open Data Philly". http://opendataphilly.org/. 
  7. ↑ Na, L.; Yan, Z. (2013). "Promote Data-intensive Scientific Discovery, Enhance Scientific and Technological Innovation Capability: New Model, New Method, and New Challenges Comments on" The Fourth Paradigm: Data-intensive Scientific Discovery". Bulletin of Chinese Academy of Sciences 1 (16). 
  8. ↑ Stanley, Deborah B. (2018-07-11). Practical Steps to Digital Research: Strategies and Skills For School Libraries. Santa Barbara, CA: ABC-CLIO. p. 275. ISBN 978-1-4408-5672-3. 
  9. ↑ 9.0 9.1 9.2 Jones, Michael (2022) (in en). More Judgment Than Data: Data Literacy and Decision-Making. Cham: Springer International Publishing. doi:10.1007/978-3-030-99472-3. ISBN 978-3-030-99471-6. https://link.springer.com/10.1007/978-3-030-99472-3. 
  10. ↑ 10.0 10.1 Carlson, Jake; Johnston, Lisa (2015). Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers. West Lafayette, Indiana: Purdue University Press. p. 15. ISBN 978-1-55753-696-9. 
  11. ↑ 11.0 11.1 11.2 Klidas, Angelika (2022). Data Literacy in Practice: A complete guide to data literacy and making smarter decisions with data through intelligent actions. Kevin Hanegan (1 ed.). Birmingham: Packt Publishing Limited. ISBN 978-1-80323-235-5. 
  12. ↑ 12.0 12.1 Kim, Jeonghyun; Hong, Lingzi; Yoon, Ayoung (2025-04-28). Ahmad, Yasir. ed. "University students' self-assessment of data literacy: A validation study" (in en). PLOS One 20 (4). doi:10.1371/journal.pone.0322104. ISSN 1932-6203. PMID 40294036. Bibcode: 2025PLoSO..2022104K. 
  13. ↑ Ghodoosi, Bahareh; Torrisi-Steele, Geraldine; West, Tracey; Heidari, Maryam (September 2025). "Perceptions of data literacy and data literacy education" (in en). Journal of Librarianship and Information Science 57 (3): 822–832. doi:10.1177/09610006241246789. ISSN 0961-0006. https://journals.sagepub.com/doi/10.1177/09610006241246789. 
  14. ↑ Pangrazio, Luci; Sefton-Green, Julian (2020-04-02). "The social utility of 'data literacy'" (in en). Learning, Media and Technology 45 (2): 208–220. doi:10.1080/17439884.2020.1707223. ISSN 1743-9884. https://www.tandfonline.com/doi/full/10.1080/17439884.2020.1707223. 
  15. ↑ Koltay, Tibor (2015). "Data literacy for researchers and data librarians". Journal of Librarianship and Information Science 49 (1): 3–14. doi:10.1177/0961000615616450. http://publikacio.uni-eszterhazy.hu/249/1/JOLIS_Data.pdf. 
  16. ↑ Calzada-Prado, Francisco-Javier; Marzal, Miguel-Angel (2013). "Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents". Libri 63 (2): 123–134. doi:10.1515/libri-2013-0010. 




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