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The purpose of this study is to introduce the concept of big data and provide a comprehensive overview to readers to understand big data application framework in libraries.

The authors first used the text analysis and inductive analysis method to understand the concept of big data, summarize the challenges and opportunities of applying big data in libraries and further propose the big data application framework in libraries. Then they used questionnaire survey method to collect data from librarians to assess the feasibility of applying big data application framework in libraries.

The challenges of applying big data in libraries mainly include data accuracy, data reduction and compression, data confidentiality and security and big data processing system and technology. The opportunities of applying big data in libraries mainly include enrich the library database, enhance the skills of librarians, promote interlibrary loan service and provide personalized knowledge service. Big data application framework in libraries can be considered from five dimensions: human resource, literature resource, technology support, service innovation and infrastructure construction. Most libraries think that the big data application framework is feasible and tend to apply big data application framework. The main obstacles to prevent them from applying big data application framework is the human resource and information technology level.

This research offers several implications and practical solutions for libraries to apply big data application framework.

The paper examined big data and academic libraries and emphasized whether it is big for something or nothing.

A conceptual and review analysis of documents was adopted to determine the concept of big data, the sources, the features, the relevance to academic libraries, specific case studies from around the world that have made use of big data, uses of big data in academic libraries, a review of best practices in the use of big data in academic libraries and the challenges.

The paper reports that although big data is indeed very big in academic libraries because there are evidences of its adoption and best practices in its use in academic libraries across the world, available challenges can render it big for nothing.

This study is limited in terms of using literature review approach to discuss big data and academic libraries. The study is also limited in terms of focusing academic libraries and not taken other types of libraries into consideration.

The study has created awareness on the part of academic libraries stakeholders including authorities, librarians and users on the relevance of big data in academic and how big indeed it is in academic library landscape. The study also implied future related studies can borrow ideas from the current studies, which will inform whether an empirical evaluation is possible on the subject matter.

The paper is the original idea by the author, and it is to emphasize the relevance of big data in academic libraries and to prepare academic libraries that have not been tapping the opportunities of big data to get ready.

This paper aims to highlight the impact of big data on library services. This study highlights the required skills of librarians and the application of big data analytics.

An analysis of the literature was also used to identify the various applications implemented in library services across the globe.

This study’s findings reveal that the role of big data remained limited because of a lack of knowledge and skills. Big data’s significant challenges include inadequate technical support, untrained librarians and financial constraints to meet these requirements. This paper highlighted the challenges and remedial measures that can be taken while adopting this technology in library services.

This paper is significant for librarians, practitioners and stakeholders of the various organization who desire to implement this technology in their respective libraries.

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

A qualitative methodology was used, involving the administration of open-ended questionnaires to librarians from six selected federal universities located in Southwest Nigeria.

The findings of this research highlight that a significant proportion of librarians are well-acquainted with the relevance of big data and its potential to positively revolutionize library services. Librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study exclusively focuses on the Nigerian context, overlooking insights from other African countries. As a result, it may not be possible to generalize the study’s findings to the broader African library community.

To the best of the authors’ knowledge, this study is unique because the paper reported that librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.

This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.

The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.

This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.

The purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation.

The Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019).

It can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well.

This paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library.

Digital library database resources have a significant impact on stimulating the research culture in higher education. The use of digital databases makes it possible to understand intellectual growth, research productivity, planning and identification of user information needs. Evaluating the effectiveness of user database resource utilization and research, the purpose of this study is to assist management in developing an excellent academic policy.

This study establishes a quantitative method to analyze the productivity of academic research using digital databases. The secondary data extracted from the databases of 52 universities provided by Higher Education Commission (HEC) and the literature published on the Institute of Scientific Information (ISI) Web of Science. The statistical technique simple linear regression was used to analyze the data for understanding the impact of independent variables the “digital databases” on the dependent variable “research productivity”.

The result of the coefficient of multiple determination, R-squared, R² 0.679, indicated 67 per cent impact of the predictor on the outcome variable. However, the standardized coefficient Beta 0.824 revealed 82 per cent impact of the individual predictor on the outcome variable. Overall, the result of linear regression showed a significant effect of independent variables on the dependent variable. Besides, the result of correlation and the strength of association between the database resources and the academic publication was significant (p < 0.005).

This research work is a supportive tool for managing gaps and promoting the development of necessary measures to develop strategies and solutions to create a better academic environment. The ultimate use of standard database resources can foster higher academic research to develop innovative ideas and improve researchers’ cognitive abilities.

From Pakistan’s point of view, this study is the first one that gives insight into the intellectual growth of young researchers in higher education. The study provides first-hand information on the use of database resources and their significant impact on the productivity of academic research.

Digital libraries promote and accelerate scientific research in academic institutions. The subscribed database resources of digital libraries have become an increasingly valuable asset for researchers. Database resources help generate new ideas, determine research directions and promote productive academic interaction between teachers and students in the information age. The purpose of this study is to examine the use of electronic resources by students in various databases, the research productivity of the faculty in the science network and the number of students who graduate each year.

This study uses a quantitative method to collect secondary data from the central database of the Higher Education Commission (HEC) for the population of 26 universities for 2 years (2015–2016). In addition to the HEC digital library, data was also collected from the Web of Science to determine the quality academic performance of faculty and researchers. Moreover, in the study, the total strength of teaching staff and doctoral faculty was extracted from the HEC website for investigation. The authors applied the Spearman’s correlation test to the secondary data using Statistical Package for Social Sciences version 25.

The correlation results of the enrolled students and the downloaded papers from various databases were statistically insignificant (p > 0.05). However, the result showed a positive correlation (p < 0.05) between the use of selected/known databases from a number of databases accessed by the HEC. More importantly, it turns out that the faculty’s productivity in the scientific network and the number of students who graduated from public and private universities are found to be insignificant (p > 0.05). However, the authors found a positive correlation (p < 0.05) between doctoral and non-doctoral faculties, which show that a significant number of non-doctoral faculties are still actively involved in teaching and research.

Research based on academic activities by faculties and students, performed for the first time on the basis of secondary data, will help the HEC and university management to determine the right direction and develop plans to improve academic performance and research quality.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.