Data Literacy
In today's day and age it is (almost) impossible to avoid encountering data in everyday life. The ability to work with data used to be a skill reserved for the needs of statisticians or scientists, be it information scientists, sociologists, or researchers. However, nowadays it is something that is considered commonplace. The umbrella term for the abilities and competencies necessary to work with data is data literacy. Jarolímková [1] suggests skills like realizing the information need, creating one's own data, researching and retrieving already existing data, data management, and critical evaluation of data, including their source, as essential skills of a data literate person. Another important aspect of data literacy according to [2] is that these competencies and abilities enable one to transform data to information and knowledge.
Data literacy is viewed to be domain specific, meaning that the inclusion or exclusion of certain skills and competencies is dependent on the field for which we wish to define data literacy. For business students, data literacy includes data hierarchy and storing, understanding data in business context, data-driven decision-making, and effective communication and presentation of data [3]. Steinerová's [2] definition of scientific data literacy includes any skills and competencies connected to the use of data with regard to the context of a given discipline. A concept of design data literacy was also attempted [4] with the three keystones being data provenance, data tracking and tracing, and data sovereignty. The ability to work with statistical data, its interpretation, analysis, and evaluation is oftentimes also included in data literacy.
What used to be a specialized skillset is today a part of basic set of competencies that are expected from anyone who consumes information. Overall, competencies and skills included in data literacy can be summarized as those, which are necessary for understanding, interpreting, using, managing, sharing, and working with data.
Works Cited
[1] JAROLÍMKOVÁ, Adéla. (2017). Datová (informační) gramotnost a výzkumná data. Informačné Technológie a Knižnice, 04, 30–33. [cit. 2023–05–02]. Available from: https://itlib.cvtisr.sk/wp content/uploads/docs/30_datova%20infor%20gramotnost.pdf.
[2] STEINEROVÁ, Jela. INFORMAČNÉ PROSTREDIE A VEDECKÁ KOMUNIKÁCIA: ASPEKTY VEDECKEJ DÁTOVEJ GRAMOTNOSTI. ProInflow [online]. 2018, 10(2), 4–22. [cit. 2023–05–02]. ISSN 18042406. Available from: doi:10.5817/ProIn2018–2–2.
[3] CONDON, Patricia B. a Wendy Girven POTHIER. Advancing data literacy: Mapping business data literacy competencies to the ACRL framework. Journal of Business & Finance Librarianship [online]. 2022, 27(2), 104–126 [cit. 2023–05–02]. ISSN 0896–3568. Available: doi:10.1080/08963568.2022.2048168.
[4] GIESE, Tim G. a Reiner ANDERL. Design Data Literacy — Impact of Data Literacy in Virtual Product Development. In: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) [online]. IEEE, 2021, 2021–12–8, s. 1–8 [cit. 2023–05–02]. ISBN 978–1–6654–9552–3. Available from: doi:10.1109/CSDE53843.2021.9718430.
[5] CALZADA PRADO, Javier a Miguel Ángel MARZAL. Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents. Libri [online]. 2013, 63(2) [cit. 2023–05–02]. ISSN 1865–8423. Available from: doi:10.1515/libri-2013–0010.
Preparation for lecture no. 9.
