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Big Data: Profile and Skills of the Information Professional.

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10th Qualitative and Quantitative Methods in Libraries QQML 2018.Chania, Creta: ISAST International Society for the Advancement of Science and Technology

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Big Data: Profile and Skills of the Information Professional.

  1. 1. BIG DATA: PROFILE AND SKILLS OF THE INFORMATION PROFESSIONAL Luísa Alvim University of Évora, CIDEHUS (Portugal) QQML 2018 22 - 25 MAY 2018 | CHANIA, CRETE, GREECE
  2. 2. Summary • Introduction • Definition of Big data; • Profile and skills of the information professional for Big Data management; EDISON Project; • Results and discussion; • Conclusions and future work. 2
  3. 3. Introduction 3 • Big Data • Profile and skills for Big Data management • Information Professional
  4. 4. Big data 4
  5. 5. Big Data 5 Source: http://bahia.ugr.es/bigdade/index.php/bigdata-snapshots/ 3vs • Volume • Velocity • Variety Doug Laney (2001)
  6. 6. Big Data 6 Source. https://www.m-brain.com/home/technology/big-data-with-8-vs/
  7. 7. Big data The different phases of the process Source: https://www.slideshare.net/cloudITbetter/big-data-building-blocks-with-aws-cloud/ 7 DECISION
  8. 8. Big Data Management Cycle 8 Source: http://www.sanborn.com/the-big-geospatial-data-management-lifecycle/
  9. 9. Big Data Management Data scientist European e-Competence Framework 9 The Skills Framework for the Information Age - SFIA
  10. 10. Source: http://edison-project.eu/ 10
  11. 11. Big Data management / EDISON 1. Data analysis; 2. Software and hardware development; 3. Skills and knowledge of scientific subjects (in the scope of business); 4. Data management and preservation; 5. Research methods. 11
  12. 12. Results and discussion EDISON Project – data scientist Information Professional: • it works for the quality of life of the data and of the knowledge; • it analyzes the data and facilitates organizations to generate knowledge for action through the creation of information products; • knowledge about legislation that affects data protection; • the ethical aspects of information use; • the knowledge about data preservation and recovery in user-oriented systems; • ease of access and usability. 12
  13. 13. Results and discussion Research data 13 Source: https://www.rd-alliance.org/ Source: http://forumgdi.rcaap.pt/ Source: http://www.dcc.ac.uk/events/research-data-management-forum-rdmf
  14. 14. 14 Source: http://www.datainfolit.org/ Results and discussion Data Literacy
  15. 15. Conclusions • Big Data creates new opportunities for the development of new skills of the information professional; • The profile of the manager can be considered to be under construction - EDISON; • Open data; • The information professional is an expert who may be of utmost importance for the management of Big Data; • The new profile and the new ways of acting of the professional of the information require a renewed academic and professional formation, oriented to the problematics of the scientific field in which the multiple tasks of the manager of Big Data are inserted. 15
  16. 16. References Alonso Arévalo, J., & Vásquez Vásquez, M. (2016). Big Data: la próxima «gran cosa» en la gestión de la información. BiD: Textos Universitaris de Biblioteconomia I Documentación, (36). Retrieved from http://bid.ub.edu/es/36/alonso.htm&gt CEN European Committee for Standardization. (2014). European e-Competence Framework 3.0. Retrieved from http://www.ecompetences.eu/wp- content/uploads/2014/02/European-e-Competence-Framework-3.0_CEN_CWA_16234-1_2014.pdf Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171–209. http://doi.org/10.1007/s11036-013-0489-0 Conselho Europeu das Associações de Informação e Documentação. (2005). Euro-Referencial I-D. Lisboa: INCITE. Costa, C., & Santos, M. (2017). The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age. International Journal of Information Management. Demchenko, I., & Belloum, A. (2016). Data Science competence framework: Approch and first working. EDISON Discussion Document. Retrieved from http://edison- project.eu/sites/edison-project.eu/files/attached_files/node-29/edison-cf-ds-draft-cc-v06.pdf Demchenko, I., & Belloum, A. (2017). EDISON: Discussion Document: Part 1. Data Science Competence Framework (CF-DS) release 2. Retrieved from http://edison- project.eu/sites/edison-project.eu/files/filefield_paths/edison_cf-ds-release2-v08_0.pdf EDISON Project UE. (2015). EDISON: Building the data science profession. Retrieved from http://edison-project.eu/ García Alsina, M. (2017). Big Data: Gestión y explotación de grandes volúmenes de datos. Barcelona: Editorial UOC. O’Reilly Media. (2017). Big Data Now. United States of America: O’Reilly Media. Ochôa, P. (2017). Painel Perfis e competências profissionais. In Encontro Curadoria Digital – Estratégias e experiências: atas. Príncipe, P., & Furtado, F. (2017). Relatório do 2o Fórum de Gestão de Dados de Informação. Retrieved from http://hdl.handle.net/1822/46338 Research Data Alliance. (2016). 23 coisas: Bibliotecas e Dados Científicos. SAS. (2013). Big Data Analytics: An assessment of demand for labour and skills, 2012-2017. London. Retrieved from file:///C:/Users/luisa/Downloads/BigDataAnalyticsAnassessmentofdemandforlabourandskills2012-2017 (1).pdf 16
  17. 17. Luísa Alvim mluisa.alvim@gmail.com Thank you Research work carried out within the scope of UID/HIS/00057/2013 (POCI-01- 0145-FEDER-007702), FCT/Portugal, COMPETE, FEDER, Portugal2020.

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