Data-Driven Paradigm Shifts: Emerging Information Scientists’ approaches to Information Analysis

Abstract

The research achieved the objectives of exploring the information mapping practice of information scientist as well as the tools employed by information scientist amidst the big data chaos. Quantitative research methodology and exploratory research design was employed to explore the novel research area, while a questionnaire was used as instrument of data collection guided by theVisual Communication theoretical framework. The population of the study was three hundred and thirty six (336) final year Bachelor of Technology/ Bachelor of Science students (B.Tech/Bsc), Master of Technology/ Science Students (M.Tech/MLIS) and Doctor of Philosophy Scholars of Library & Information in Specialised Federal Universities in Nigeria. Total enumeration was adopted as a sampling technique and 290 responded. The study revealed a paradigm shift in sense-making methods and tools by emerging information scientist that are engrossed in the big data space. The result shows a significant evolution in how data is analyzed. This paradigm shift involves adopting new methodologies and technologies to handle complex and large-scale data where traditional methods are gradually being substituted by machine learning algorithms and visualizations. This paradigm shift buttresses a more sophisticated approach to big data sense making. The research recommends that academic institutions/LIS schools and organisations should develop a more apt and standardized training for information scientists on diverse information mapping techniques to ensure a more effective adaptability to emerging challenges in the big data space as well as sense making of online debates. Also to address the observed variability in tools usage, it is recommended that a more unified framework for information mapping tools be developed. This framework should integrate the strengths of various tools and address the inconsistencies and accessibility challenges noted among different user groups