Opportunities and Challenges of Using Big Data Applications in Institutions of Higher Learning Libraries and Research Institutions

Opportunities and Challenges of Using Big Data Applications in Institutions of Higher Learning Libraries and Research Institutions

Josiline Phiri Chigwada
Copyright: © 2021 |Pages: 16
DOI: 10.4018/978-1-7998-3049-8.ch008
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The chapter documents opportunities and challenges experienced when using big data applications in libraries. The objective of the study was to examine the big data applications that are used in libraries. The big data concept is new, and some librarians are not aware of it while others do not have the knowledge and skills of using big data applications. A structured literature review was done to examine how libraries use big data. The search terms that were used were “big data AND libraries.” The findings revealed that libraries are generating big data. The challenges that are experienced include data accuracy, data confidentiality and security, lack of skills to deal with data reduction and compression, and the unavailability of big data processing systems and technology in libraries. The author recommends the up skilling of librarians so that they are able to deal with the challenges of working with big data applications.
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The development of information communication technologies (ICTs), digital information techniques, internet technology, internet of things and cloud computing have caused an increase in data generation and use (Alotaibi and Abdullah 2016). Institutions of higher learning and research institutions produce a lot of data in their day to day operations and their libraries are busy due to high usage be it on or off campus. Libraries have a virtual presence and users are able to use the information resources regardless of location and time. The data is coming from various sources such as sensors, smart devices, social networking interactions, web pages, videos, images, personal browsing history, and online transactions among other things (Wu et al 2014, Alotaibi and Abdullah 2016). As a result, the data is presented as structured, unstructured and semi structured (Bello-Orgaz et al 2016, Zaharia et al 2016). The literature indicates that the big data concept was started in 2001 by Laney in his research notes. Laney believes that an important feature of big data is that it cannot be processed effectively by traditional data management tools (Laney, 2001). Most big data-processing methods emphasize high-speed and efficient use of large amounts of data (Li et al 2019). It is also stated that big data is a collection of datasets so large and complex that it is difficult to use on-hand database management tools, or traditional data processing applications, for their processing that includes capturing, storage, search, sharing, transfer, analysis, and visualization (Li et al 2019).

The trend towards having and dealing with larger data sets produced by different actors is also due to the additional information derivable from the analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions. (Lu 2014). Zhan and Widen (2019) indicated that big data is data oriented and ability oriented. Data oriented shows that the data or information has certain features such as large volume and increasing rapidly while ability oriented highlights the need for technology to handle the data. In this study, a definition that was provided by Zhan and Widen (2019) was adopted. They stated that big data is “data with large size, fast growing speed, and various types which can complicate data handling techniques but also boost the creation of technological solutions. Value is generated by the proper operation and use of big data” (Zhan and Widen 2019: 563). It is in light of this background that the chapter seeks to address the following objectives:

  • 1.

    To examine the big data applications that can be used by libraries in institutions of higher learning.

  • 2.

    To document the opportunities of using big data applications in libraries in institutions of higher learning.

  • 3.

    To discuss the challenges faced when using big data applications in libraries in institutions of higher learning.


Problem Statement

The use of big data applications in libraries is a new field where research is limited as stated by Hashem et al (2015) and Kim and Choi (2016). There is also insufficient emphasis on the practical application of big data in libraries as stated by Li et al (2019) and Lu (2014). Zhan and Widen (2019) concluded that big data are rare in librarianship. As a result, the problem statement for this study is how libraries in research institutions and institutions of higher learning can take advantage of the opportunities and challenges that are offered by big data. A structured literature review and content analysis was employed to unpack the opportunities and challenges of using big data applications in institutions of higher learning and research institutions. Big data AND libraries were the search terms that were used to retrieve electronic information resources from academic databases that documents the application of big data in libraries. Content analysis was used to analyse the collected information which was presented in themes according to the objectives of the study.

Key Terms in this Chapter

Machine Learning: An application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Digital Library: A collection of information resources in digital formats and accessible by computers.

Data Librarian: An information professional responsible for developing technical expertise on practical solutions of data management, archiving and dissemination and on data mining and visualisation.

Curation: The process of selecting, organising, and looking after online content in a collection.

Research Data Life Cycle: It identifies the steps that are taken at the different stages of the research process to ensure successful data curation and preservation such as data creation, data processing, data analysis, data preservation, data access, and data reuse.

Artificial Intelligence: Development of computer systems that perform tasks normally requiring human intelligence, for example, speech recognition, decision making, visual perception and translations.

Big Data: Large data sets that cannot be processed and analysed using traditional processing systems.

Visualisation: The representation of an object, situation, or set of information as a chart or other image.

Cloud Computing: Using remote servers hosted on the Internet to store, manage, and process data.

Research Data Management: The organisation, storage, preservation, and sharing of data collected and used in a research project.

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