Data literacy

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Data literacy is the ability to read, create and communicate data as information and has been formally described in varying ways. Discussion of the skills inherent to data literacy and possible instructional methods have emerged as data collection becomes routinized and talk of data analysis and big data has become commonplace in the news, business,[1] government[2] and society in countries across the world.[3]

Related terms[edit]

Data literacy focuses on the ability to build knowledge from data, and to communicate that meaning to others. It is related to other fields, including:


  • A traditional view emphasizes the numeric, statistical nature of data as information, including "... understanding what data mean, including how to read graphs and charts appropriately, draw correct conclusions from data, and recognize when data are being used in misleading or inappropriate ways."[4]
  • A more progressive view describes data literacy as "... the ability to: formulate and answer questions using data as part of evidence-based thinking; use appropriate data, tools, and representations to support this thinking;interpret information from data; develop and evaluate data-based inferences and explanations;and use data to solve real problems and communicate their solutions."[5]
  • A workforce-focused example includes varying technical and digital formats by describing data literacy as "... competence in finding, manipulating, managing, and interpreting data, including not just numbers but also text and images."[6]


List of libraries provided data literacy[edit]

The Massachusetts Institute of Technology’s (MIT) 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.


  1. ^ Hey, A. J., Tony Hey, Tansley, S. and Tolle, K., Eds. (2009). The fourth paradigm: data-intensive scientific discovery. Microsoft. 
  2. ^ "Open Data Philly". Retrieved 14 June 2013. 
  3. ^ 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). 
  4. ^ Carlson, J. R.; Fosmire, M.; Miller, C.; Sapp Nelson, M. (2011). "Determining Data Information Literacy Needs: A Study of Students and Research Faculty". Libraries Faculty and Staff Scholarship and Research. 23. 
  5. ^ Vahey, P.; Yarnall, L.; Patton, C.; Zalles, D. & Swan, K. (April 2006). "Mathematizing middle school: Results from a cross-disciplinary study of data literacy.". American Educators Research Association Annual Conference. 5. 
  6. ^ Harris, Jeanne. "Data Is Useless Without the Skills to Analyze It". Harvard Business Review. Retrieved 14 June 2013. 
  7. ^ "Become Data Literate in 3 Simple Steps". 
  8. ^ "Data Literacy". 
  9. ^ "Teacher Data Literacy: It's About Time" Data Quality Campaign