Harnessing data librarianship for big data in academic libraries

Abstract

Big Data has revolutionised the library, shifting the library processes, operations and services to data-driven oriented. Thus, Data Librarianship evolved as a response to this significant change in libraries, thereby helping libraries in harnessing the values of large data volumes in all data-related activities. Noting that academic libraries engaged in varying data-oriented activities underscores why this study explores harnessing Data Librarianship for Big Data in academic libraries. Typology design of conceptual research was adopted. The design allows this study to logically discuss the concepts of Big Data, Data Librarianship and Data Librarians, dissecting the qualities of Data Librarians in harnessing the potentials of Big Data, library operations and services that constitute Big Data and explaining the challenges associated with leveraging Big Data for library services. Libraries have been responding to the need of exploiting the values of Big Data by assigning Data Librarians who

are data literate and skillful in recognising various data sources and types, accurately organising, analysing, and interpreting data to create taxonomies and metadata systems, systematize retrieval procedures, smart libraries, research data management/services and circulation and cataloguing systems through OCLC and other comparable data-sharing organisations. Nonetheless, Data Librarians encountered many problems in harnessing data values including absence of data policies, the scarcity of opportunities for data librarians to receive training, the absence of extra financial benefits, the lack of infrastructure and systems and lack of organisational support for the launch of data-driven services. This study has tactfully argued that Big Data has given birth to a new field called Data Librarianship. Libraries and other similar information institutions may now take advantage of the value of their massive volumes of data by redesigning their decision-making and problem-solving processes using data-driven insights.