Big Data in academic libraries: literature review and future research directions

Abstract

Recently, Big Data studies have attracted considerable attention. However, Big Data analytics in academic libraries confront two fundamental challenges: the huge volume, velocity, and variety of data and the complexity of its techniques and algorithms. The primary aim of this study is to explore which techniques and tools can be applied in academic libraries in order to analyze Big Data, and then determine its profits in academic libraries. In addition, this study attempts to answer the following research questions: how should librarians be made to involve in Big Data? What are the future research developments in Big Data? What are the gaps in Big Data studies related to academic libraries? To provide a considerably better understanding of the advantages of Big Data in academic libraries and their future research directions, a comprehensive literature review of Big Data analysis of academic libraries over the last seven years was conducted. The results yielded a total of 37 papers related to Big Data in academic libraries. These results indicated that despite the large amount of research conducted on this topic, only a few studies discussed the implication of Big Data in academic libraries, including the analyzing tools and techniques. The benefits of Big Data in academic libraries and its implications on methodology in future studies are discussed. The present study also highlights the evolving field of Big Data research in academic libraries.

Business intelligence in academic libraries in Jordan: Opportunities and challenges

“…Bronzo et al (2013) and Vukšić et al (2013) showed that business intelligence leads to enhancing overall organizational performance. Kumar and Priyadarsini (2016) and Al-Barashdi and Al-Karousi (2019) confirmed that data analytics help libraries to enhance the quality of their services, as they are harnessing data for more informed planning and decision-making. The participants emphasized that business intelligence is changing library strategies and objectives based on accurate statistics (82%) that guide the work of the library to achieve its existing goals.…”

“…Academic libraries have a clear role in data analytics to help them enhance the quality of their services (Al-Barashdi and Al-Karousi, 2019; Kumar and Priyadarsini, 2016). Accordingly, libraries are harnessing their data for more informed planning and decision-making.…”

Design on Big data Platform-based in Higher Education Institute

“…and unstructured data includes information, images, audio, video, and comments via social media. (Kumar & Singh, 2019;Hariri et al, 2019;Altaye & Nixon, 2019;Rizk et al, 2019;Al-Barashdi & Al-Karousi, 2019) presented that Big Data requires high velocity and variety so the Veracity of data should have the reliability of the data source and the accuracy of the dataset with a process for checking and confirming the accuracy of the information which is directly related to the results of data analysis. To maintain accurate data without any duplication of the dataset is certainly the hardest and the most time-consuming.…”

“…Characteristics of Big Data (Daniel, 2014;Songsangyos & Nilsook, 2015;Kumar & Singh, 2019;Hariri et al, 2019;Altaye & Nixon, 2019;Rizk et al, 2019;Al-Barashdi & Al-Karousi, 2019) states that the 5V characteristics of big data consists of Volume which is a quantity of accurate data for an analysis, Velocity is the ability of consequence analysis for making a decision and responding promptly to a variety of circumstances. Variety is diversified structured and unstructured data in the form of either RDBMS, text, XML, JSON or Image, Veracity is data with multiple quality and value levels for an analysis, and Value is worthwhile data adoption when receive an accurate data leads to development that can measure success concretely.…”

Big Data Analytics For Organizations: Challenges and Opportunities and Its Effect on International Business Education

“…A Past research work Laney in 2001 [4] focused on big data description based on the 3Vs (volume, velocity, and variety). Sagiroglu and Sinanc in 2013 [5] ulterior presented a big data research review and observed its security issues, while Lomotey et al in 2014 [6] defined big data by 5Vs, extending the work done by Laney in 2001 from 3Vs to include value, and veracity [7]. A Ren et al in 2019 [8] thus recently developed a set of up-to-date big data definitions, in Figure 1 shows predictions of global data volume provided by International Data Corporation (IDC) [9].…”