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Impact of big data and data analytics on the provision of data services in academic libraries
Abstract
Purpose
The purpose of this study is to determine the level of awareness among library and information science (LIS) professionals regarding the perceived utility of big data (BD) and data analytics (DA) in academic libraries, as well as their influence on the provision of data services (DSs).
Design/methodology/approach
A cross-sectional survey was carried out to collect the data for this study. The population of this study comprised LIS professionals working in public sector university libraries. A four-factor measurement model estimating the influence of BD and DA on the provision of DSs in academic libraries was tested using the structural equation modelling.
Findings
The findings revealed that awareness (AW) ( β = 0.141, CR = 2.534, p = 0.011) demonstrated a significant positive influence on the provision of DSs. The perceived utility of BD ( β = 0.058, CR = 0.582, p = 0.561), and perceived utility of DA ( β = 0.141, CR = 2.534, p = 0.905) exhibits a positive but statistically non-significant impact on the provision of DSs ( β = 0.010, CR = 0.120, p = 0.905). The goodness of fit indices suggest a favourable fit for the model, as evidenced by the following values: χ ² = 1.400, DF = 164; p = 0.001; IFI = 0.954; TLI = 0.946; CFI = 0.953; GFI = 0.906; and RMSEA = 0.043.
Originality/value
A new perspective on the use of BD and DA in academic libraries is presented in this study. It presents a four-factor measurement model on the influence of BD and DA on the provision of DSs in university libraries.
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Big data has gained significant attention from both academics and practitioners of a number of disciplines including library and information science (LIS). This paper intends to explore the perceptions of LIS communities towards big data management and the possible challenges of its implementation in libraries with a special reference to Bangladesh. The subjective opinions and deep insights of fifteen (15) experts of the LIS domain in Bangladesh have comprehended the present standing of the information sectors. The expert interviews of both groups suggest that big data management is still in its infancy in library practices and is limited to the exploration and conceptualization stage. Libraries have an opportunity to be involved in big data management and to apply allied technologies in their operations and services. The study participants are hopeful, enthusiastic, and at the same time skeptical about its practical implications due to the technological and infrastructural constraints along with the funding and competencies issues. The findings suggest no libraries in Bangladesh formally practice big data-based librarianship; however, the professionals’ theoretical and conceptual knowledge proved adequate with an occasional inconsistency and knowledge gap. The perceived competencies level lacks awareness, proper education and experiences. LIS community’s views of the immediate effect on such technologies are not supported by the findings, and hence policy intervention and a long-term road map were suggested.
A library has a lot of primary and secondary data with a wealth of information content. The diversity and improvement of the content can be attributed to the data that is freely available online. These include customer data, service data (loan and return), research data, citation data and other data. Therefore, libraries need to analyze these data as it provides many benefits and added value to the continuity of their services. This systematic review examines the determinants to adopt big data analytic (BDA) in organizations including libraries. The review analyses international literature on determinants to adopt BDA between January 2010 to December 2019 from databases including Emerald, ProQuest, Sage, Science Direct, Scopus, Springer, Web of Science (WOS) and Google Scholar. It uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Literary quality assessments were conducted on BDA concepts based on previous theories and articles. Twenty (20) papers from countries all over the world met the inclusion criteria. The review identified three-concept of BDA in library services; BDA readiness in libraries, model to measure BDA adoption and its implication in the library, and limitations of the existing model or concept of BDA for the library. Most studies reported those BDA concepts are new concepts; data science and libraries; fundamental building block of machine learning and the software instruction set for AI, hard characteristics of data, such as the big data V-characteristics; adoption readiness; and potential to exploit BDA. Future research should focus on a variety of rigorous large-scale methodological studies. Studies should use the quantitative approach to explore specific factors related to promoting BDA adoption. This review can help academics and practitioners understand the key factors for adopting BDA in a library so that BDA activities that enable a library to make practical and strategic preparation can be effectively implemented.
Information professionals such as librarians are rapidly moving to streamline the resources and services related to Covid-19 for healthcare professionals and the general public through online and virtual services. The study attempts to assess library and information services delivery status and the role of librarians in service delivery during the COVID-19 pandemic. We adopted an embedded approach of mixed-method design to conduct the study. We assessed the academic libraries' websites of the top five affected countries by a coronavirus to determine the ways of service delivery and evaluate the role of LIS professionals towards library & information service delivery in the COVID-19 scenario. Libraries may assume a significant job responsibility in giving trustworthy services in the Covid-19 pandemics. The results concluded that due to lock-down, librarians are following work from home policy and providing their services through online and virtual ways.
The concept of big data has been extensively considered as a technological modernisation in organisations and educational institutes. Thus, the purpose of this study is to determine whether the modified technology acceptance model (MTAM) is viable for evaluating the performance of librarians in the use of big data analytics in academic libraries. This study used an empirical research method for collecting data from 211 librarians working in Pakistan's universities. On the basis of the findings of the MTAM analysis by structural equation modelling, the performances of the academic libraries were comprehended through the process of big data. The main influential components of the performance analysis in this study were the big data analytics capabilities, perceived ease of access and the usefulness of big data practices in academic libraries. Subsequently, the utilisation of big data was significantly affected by skills, perceived ease of access and the usefulness of academic libraries. The results also suggested that the various components of the academic libraries lead to effective organisational performance when linked to big data analytics.
Libraries hold large amounts of data, which can contribute to improvements in the quality of library services. Data resources of modern library have the characteristics of big-data, where library can use big-data methods to achieve reform and innovation, including resource transferring, resource utilization, social identity, thinking innovation. Librarians are well aware that big data can lead to better informed decisions and can transform the user’s experience. This work investigates the concept of big data from the perspective of Information Technology department staff at three major university libraries in Jordan. This work attempts to uncover big data, analytics and challenges in academic libraries in Jordan. A review of research work on big data in libraries was conducted, and a summary of the applications and research directions in this field is presented. The status of big data in libraries in Jordan is discussed, and the challenges associated with it are also explored.
INTRODUCTION Since the 2000s, interest in research data management (RDM) has grown considerably. As a result, a large body of literature has discussed a broad variety of aspects related to data management. But, few studies have examined and also interpreted from visual perception the intellectual structure and progressive development of the existing literature on RDM. METHODS Guided by five research questions, this study employed bibliometric techniques and a visualization tool (CiteSpace) to identify and analyze the patterns of the scholarly publications about RDM. RESULTS Through CiteSpace’s modeling and computing, the knowledge (or network) structures, significant studies, notable topics, and development trends in the literature of RDM were revealed. DISCUSSION The majority of the literature pertinent to RDM was published after 2002. Major research clusters within this interdisciplinary field include “scientific collaboration,” “research support service,” and “data literacy,” while the “scientific collaboration” research cluster was the most active. Topics such as “digital curation” and “information processing” appeared most frequently in the RDM literature. Additionally, there was a sharp increase in several specific topics, such as “digital library,” “big data,” and “data sharing.” CONCLUSION By looking into the “profile” of the literature on RDM, in terms of knowledge structure, evolving trends, and important topics in the domain, this work will add new information to current discussions about RDM, new service development, and future research focuses in this area.
Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions.
Purpose
The purpose of this paper is to analyze the views and capabilities of librarians for the implementation of Big Data analytics in academic libraries of Pakistan. The study also sets out to check the relationship between the required skills of librarians and the application of Big Data analytics.
Design/methodology/approach
A survey was conducted to gather the required data from the targeted audience. The targeted population of the study was Head/In charge library managers of Pakistani university libraries, which were 173 in total. All the respondents (academic librarians) were invited through an e-mail to respond to the survey voluntarily. Out of 173 respondents from higher education commission of Pakistan chartered university libraries, 118 librarians (68.2 percent) completed the survey that was finally considered, and after checking data, recommendation for analysis was made. To analyze the collected data, statistical technique Pearson correlation was applied using statistical package for social science version 25 to know the strength of the mutual correlation of variables.
Findings
The findings of the study show a strong correlation between the required competencies and skills of librarians for the implementation of Big Data analytics in academic libraries. In all variables of the study, the correlation was highly significant, except two of the variables, including “concept of Big Data” and “different forms of data.” The study also reveals that most of the respondents were well aware of the concept of Big Data analytics. Moreover, they were using a large amount of data to carry out various library operations, including the acquisition, preservation, curation and analysis of data.
Originality/value
This study is significant in the sense that it fills a substantial gap in the literature regarding the perspective of librarians on Big Data analytics.
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.
Based on the investigation of the position of user service for constructing digital libraries in the big data era, this paper points out that not only data resources of modern digital library have the characteristics of big data, but also the existing library services need to use big data methods to achieve reform and innovation, including resource transferring, resource utilization, social identity, thinking innovation. We focus on the importance of user services and types of big data resources that digital libraries can utilize, which include big data within libraries such as user behavior data and digital literature resource, and other big data outside libraries such as scholarly big data. We also examine the problems and potential of digital libraries in the age of big data relative to data, technology, services, and users. Using existing big data resources and considering the characteristics of current users’ needs from the perspective of users, more effective ideas and methods to improve existing services in digital library can be put forward. At the same time, it is the personalized need of users in the age of big data that constitute the driving factor for the development of digital library from resource-sharing service to user-oriented service.
Forward by Henrik von Scheel.
Convert the promise of big data into real world results There is so much buzz around big data. We all need to know what it is and how it works - that much is obvious. But is a basic understanding of the theory enough to hold your own in strategy meetings? Probably. But what will set you apart from the rest is actually knowing how to USE big data to get solid, real-world business results - and putting that in place to improve performance. Big Data will give you a clear understanding, blueprint, and step-by-step approach to building your own big data strategy.
This is a well-needed practical introduction to actually putting the topic into practice. Illustrated with numerous real-world examples from a cross section of companies and organisations,
Big Data will take you through the five steps of the SMART model: Start with Strategy, Measure Metrics and Data, Apply Analytics, Report Results, Transform. Discusses how companies need to clearly define what it is they need to know - Outlines how companies can collect relevant data and measure the metrics that will help them answer their most important business questions Addresses how the results of big data analytics can be visualised and communicated to ensure key decisions-makers understand them - Discusses how companies need to clearly define what it is they need to know - Includes many high-profile case studies from the author's work with some of the world's best known brands
The extent to which teachers adopt technology in their teaching practice has long been in the focus of research. Indeed, a plethora of models exist explaining influential factors and mechanisms of technology use in classrooms, one of which—the Technology Acceptance Model (TAM) and versions thereof—has dominated the field. Although consensus exists about which factors in the TAM might predict teachers’ technology adoption, the current field abounds in some controversies and inconsistent findings. This meta-analysis seeks to clarify some of these issues by combining meta-analysis with structural equation modeling approaches. Specifically, we synthesized 124 correlation matrices from 114 empirical TAM studies (N = 34,357 teachers) and tested the fit of the TAM and its versions. Overall, the TAM explains technology acceptance well; yet, the role of certain key constructs and the importance of external variables contrast some existing beliefs about the TAM. Implications for research and practice are discussed.
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The library which is the gate of the University should be supported by the existence of an adequate information system, to provide excellent service and optimal to every user. Library management system that has been in existence since 2009 needs to be re-evaluated so that the system can meet the needs of both operator and Unnes user in particular, and users from outside Unnes in general. This study aims to evaluate and improve the existing library management system to produce a system that is accountable and able to meet the needs of end users, as well as produce a library management system that is integrated Unnes. Research is directed to produce evaluation report with Technology Acceptance Model (TAM) approach and library management system integrated with the national standard.
Purpose
The purpose of the presented study is to explore the roles of public libraries in the context of Big Data.
Design/methodology/approach
A mixed method approach was used and had two main data collection phases. A survey of public libraries was used to generate an overview of which professional roles connect public libraries with Big Data. Eight roles were identified, namely: educator, marketer, data organiser, data container, advocator, advisor, developer, and organisation server. Semi-structured interviews with library directors and managers were then conducted to gain a deeper understanding of these roles and how they connect to the library’s overall functions.
Findings
Results of the survey indicated that librarians lack a proper comprehension of and a pragmatic application of Big Data. Their opinions on the eight roles are slightly stronger than neutral. However, they do not demonstrate any strong agreement on these eight roles. In the interviews, the eight roles attained more clear support and are classified into two groups: service-oriented and system-oriented roles.
Originality/value
As an emerging research field, Big Data is not widely discussed in the library context, especially in public libraries. Therefore, a research gap between public libraries and Big Data is filled by this study. In addition, Big Data in public libraries could be well managed and readily approached by citizens in undertaking such roles, which entails that public libraries will eventually benefit from the Big Data era.
Developing Big Data applications has become increasingly important in the last few years. In fact, several organizations from different sectors depend increasingly on knowledge extracted from huge volumes of data. However, in Big Data context, traditional data techniques and platforms are less efficient. They show a slow responsiveness and lack of scalability, performance and accuracy. To face the complex Big Data challenges, much work has been carried out. As a result, various types of distributions and technologies have been developed. This paper is a review that survey recent technologies developed for Big Data. It aims to help to select and adopt the right combination of different Big Data technologies according to their technological needs and specific applications’ requirements. It provides not only a global view of main Big Data technologies but also comparisons according to different system layers such as Data Storage Layer, Data Processing Layer, Data Querying Layer, Data Access Layer and Management Layer. It categorizes and discusses main technologies features, advantages, limits and usages.
Purpose
The purpose of this paper is to provide a conceptual model for the transformation of big data sets into actionable knowledge. The model introduces a framework for converting data to actionable knowledge and mitigating potential risk to the organization. A case utilizing a dashboard provides a practical application for analysis of big data.
Design/methodology/approach
The model can be used both by scholars and practitioners in business process management. This paper builds and extends theories in the discipline, specifically related to taking action using big data analytics with tools such as dashboards.
Findings
The authors’ model made use of industry experience and network resources to gain valuable insights into effective business process management related to big data analytics. Cases have been provided to highlight the use of dashboards as a visual tool within the conceptual framework.
Practical implications
The literature review cites articles that have used big data analytics in practice. The transitions required to reach the actionable knowledge state and dashboard visualization tools can all be deployed by practitioners. A specific case example from ESP International is provided to illustrate the applicability of the model.
Social implications
Information assurance, security, and the risk of large-scale data breaches are a contemporary problem in society today. These topics have been considered and addressed within the model framework.
Originality/value
The paper presents a unique and novel approach for parsing data into actionable knowledge items, identification of viruses, an application of visual dashboards for identification of problems, and a formal discussion of risk inherent with big data.
Purpose – The purpose of this paper is to identify and describe the most prominent research areas
connected with “Big Data” and propose a thorough definition of the term.
Design/methodology/approach – The authors have analysed a conspicuous corpus of industry and
academia articles linked with Big Data to find commonalities among the topics they treated. The
authors have also compiled a survey of existing definitions with a view of generating a more solid one
that encompasses most of the work happening in the field.
Findings – The main themes of Big Data are: information, technology, methods and impact. The
authors propose a new definition for the term that reads as follows: “Big Data is the Information asset
characterized by such a High Volume, Velocity and Variety to require specific Technology and
Analytical Methods for its transformation into Value.”
Practical implications – The formal definition that is proposed can enable a more coherent
development of the concept of Big Data, as it solely relies on the essential strands of current
state-of-the-art and is coherent with the most popular definitions currently used.
Originality/value – This is among the first structured attempts of building a convincing
definition of Big Data. It also contains an original exploration of the topic in connection with library
management.
The biggest challenge for the use of ''big data'' in health care is social, not technical. Data-intensive approaches to medicine based on predictive modeling hold enormous potential for solving some of the biggest and most intractable problems of health care. The challenge now is figuring out how people, both patients and providers, will actually use data in practice. To understand how data-intensive solutions could have an impact on health care, our research team talked to frontline providers in impoverished and rural areas, technology enthusiasts in mobile health and health IT startups, clinicians and researchers in major research hospitals, Quantified Self members at data-driven meetup presentations of massive amounts of tracking data, and attendees at the growing number of conferences for health technology and innovation up and down both coasts. I found the buzz as feverishly loud around health information innovation as it was during my research on the first dot-com boom.
Big data is a collection of massive and complex data sets and data volume that include the huge quantities of data, data management capabilities, social media analytics and real-time data. Big data analytics is the process of examining large amounts of data. There exist large amounts of heterogeneous digital data. Big data is about data volume and large data set's measured in terms of terabytes or petabytes. This phenomenon is called Bigdata. After examining of Bigdata, the data has been launched as Big Data analytics. In this paper, presenting the 5Vs characteristics of big data and the technique and technology used to handle big data.
Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions for these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited high convergent, discriminant, and factorial validity. Perceived usefulness was significantly correlated with both self-reported current usage (r=.63, Study 1) and self-predicted future usage (r =.85, Study 2). Perceived ease of use was also significantly correlated with current usage (r=.45, Study 1) and future usage (r=.59, Study 2). In both studies, usefulness had a significantly greater correlation with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecedent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.
In the past few years, the field of big data has multiplied, with more academic papers written about it. This bibliometric research was done to look at and understand the trends regarding countries, organizations, authors, and keywords that are creating the most publications and citations in big data. This study was done to understand the current state of scientific publications in the field. The research used Web of Science (WoS) database information from 1993 to 2021. The study of 32,085 papers showed that, on average, each document has 14.7 citations and 3.46 citations per year. According to the results, the United States, China, and the United Kingdom have the most scholarly publications about big data. The Chinese Academy of Science, Harvard University, and Stanford University were the three most productive groups. When researching big data, most writers work together, and most terms are related to big data analytics, machine learning, and cloud computing.
Big data and artificial intelligence (AI) technologies have changed how we live, how we work, and how we organize businesses. Thus, it is no surprise that it is also changing how we manage human resources (HR). For HR leaders, digital transformation is a very hot topic, having the potential to create high value for businesses. First, HR can transform all functions, processes, and systems by leveraging digital platforms and applications. Second, HR can lead business digitalization, enabling a compelling employee experience where a digital culture, a digital workplace, and digital management are welcomed. To provide a more pragmatic perspective, this chapter discusses digitalization of HR with big data and artificial intelligence (AI) technologies and identifies key digital HR strategies and roles needed to sustain the digital transformation. Also, this chapter presents the advantages of digital HR and the basic pitfalls HR faces in the digital transformation of HR.
There is a rapid evolution in the purpose and value of higher education brought about by technological advancement and data ubiquity. Data mining and advanced predictive analytics are increasingly being used in higher education institutions around the world to perform tasks, ranging from student recruitment, enrolment, predicting student behaviour, and developing personalised learning schemes. This chapter evaluates and assesses the impact of big data and cloud computing in higher education. The authors adopt systematic literature research approach that employs qualitative content analysis to establish their position with regards to the impact, benefits, challenges, and opportunities of integrating big data and cloud computing to facilitate teaching and learning.
Information communication technology is growing at a faster speed and diversified way. Cloud computing, internet of things, 5G, and such technologies are gearing-up and resulting in proliferation of data. Data is a raw information and it has created a big data. To handle such data, big data techniques are emerging. Library and information science is a big-way service profession that mediates between the data/information and the users, letting them be students, researchers, technocrats. Big data, mostly digital data, is being generated through multiple on-line surveys and repositories. Digital and social media is the main source of generating such data. Analyzing such data according to user needs is a huge task. This is the challenge now a days to organize the data explosion, specially the volume and variety of data. Big data analytics proves to be a major help in organizing and fetching data sets pertaining to user query. The authors, in this chapter, deal with four major services of libraries, wherein time efficiency can be achieved through big data analytics. Authors have focused on thrust areas of library and information science and indicate the benefits of big data analytics for service efficiency.
Information and operations management in libraries presents a unique opportunity to provide insights for the sharing economy. Libraries correspond to a special type of sharing goods, named common-pool resources. Such resources have two characteristics: they are non-exclusive, but rival to each other. Service operations in libraries involve thousands of operations every year, making them a perfect context for the use of big data analytics capabilities (BDAC) to provide real-world evidence on the potential existing challenges in the sharing economy. Employing a novel dataset related to 723,798 library transactions, made by 16,232 individual users during a 10-year period (2006–2015), we estimate peer effects among users via regression analysis, considering the number of books each user borrows. Our main results suggest that a rise in the number of loans among a user’s peer group correlates with her own loans, an evidence of positive peer effects. However, a closer look at the data suggests a high degree of heterogeneity, in terms of behavioral patterns. First, we suggest that peer effects do not occur in the case of users who are not subject to monetary fines. Second, peer effects vary according to users’ category (student or non-student), and area of study (management, accounting, economics, and other courses). Third, there is evidence of different magnitudes of peer effects according to time in school, which suggests the existence of learning effects in a library setting. The results reported in this paper highlight the important role of big data analytics capabilities to uncover new challenges of the sharing economy, having important implications, both in theoretical and practical terms.
This study aims to explore multiple factors that are associated with social media use by public librarians for marketing purposes. Based on the technology acceptance model and theory of planned behavior, the effects of five factors—usefulness, ease of use, attitude, subjective norms, and behavioral control—on social media use intention were examined. A survey was conducted, and 462 valid responses were collected from public librarians across the United States. The findings revealed that all five factors have a significant impact on librarians’ intention to engage in social media activities for library marketing. Perceived behavioral control factors were the most influential on social media use intention. Both practical and theoretical implications are discussed based on the findings of this study.
The availability of big data is increasing in various sectors of the economy worldwide. Consequently, opportunities for data services are expanding in special, academic and public libraries. Librarians’ cataloging, archiving, research, and reference skills remain especially suited for the provision of big data services. In this article the authors examine the literature on big data and libraries to identify major themes as well as highlight possibilities and problems with the management of large datasets. Librarians can utilize this information to develop or improve data services in their institutions’ libraries.
Purpose
The purpose of this paper is to define big data and draw its deep understanding. Moreover, trends of big data research in the field of library and information management are explored. With the purpose to explore the research trends, papers indexed in Thomson Reuters’ ISI Web of Knowledge were analyzed.
Design/methodology/approach
It is a literature-based and scientometric paper. A formal definition is constructed through a review of literature. Moreover, scientometric analysis of papers indexed in Thomson Reuters’ ISI Web of Knowledge has been done to explore the research trends associated with big data in the field of library and information science, using Vosviewer software.
Findings
The findings of the study indicate the reshaped definition of big data. The findings further indicate major research trends associated with big data. The analysis indicated “Risk”, “Industry”, “Market”, “Creditworthiness” and “Big Data Analytics”, the most repeated research trends associated with big data.
Practical implications
The paper sums up the learnings required to be a successful data-literate manager. First, the study defines big data. Second, the study describes current research trends associated with big data. Third, on the basis of the explored trends, data managers and library and information management professionals are guided about the learnings they require to be a successful data manager. Where thousands of data-literate managers are predicted to require in future, the present study is a guide to trends associated with big data.
Originality/value
It is a first study of its type which provides a reshaped definition of big data. It portrays its landscape and associated research trends in the field of information and library management (ILM).
Currently, data are stored in an always-on condition, and can be globally accessed at any point, by any user. Data librarianship has its origins in the social sciences. In particular, the creation of data services and data archives, in the United Kingdom (Data Archives Services) and in the United States and Canada (Data Library Services), is a key factor for the emergence of data librarianship. The focus of data librarianship nowadays is on the creation of new library services. Data librarians are concerned with the proposition of services for data management and curation in academic libraries and other research organizations. The purpose of this paper is to understand how the complexity of the data can serve as the basis for identifying the technical skills required by data librarians. This essay is systematically divided, first introducing the concepts of data and research data in data librarianship, followed by an overview of data science as a theory, method, and technology to assess data. Next, the identification of the competencies and skills required by data scientists and data librarians are discussed. Our final remarks highlight that data librarians should understand that the complexity and novelty associated with data science praxis. Data science provides new methods and practices for data librarianship. A data librarian need not become a programmer, statistician, or database manager, but should be interested in learning about the languages and programming logic of computers, databases, and information retrieval tools. We believe that numerous kinds of scientific data research provide opportunities for a data librarian to engage with data science.
Social life increasingly occurs in digital environments and continues to be mediated by digital systems. Big data represents the data being generated by the digitization of social life, which we break down into three domains: digital life, digital traces, and digitalized life. We argue that there is enormous potential in using big data to study a variety of phenomena that remain difficult to observe. However, there are some recurring vulnerabilities that should be addressed. We also outline the role institutions must play in clarifying the ethical rules of the road. Finally, we conclude by pointing to a few trends that are not yet common in research using big data but will play an increasing role in it. Expected final online publication date for the Annual Review of Sociology Volume 43 is July 30, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
This paper examines some conceptual issues for library and information science (LIS), with a focus on how they have been treated in the scholarship of Rafael Capurro, based on a selective literature analysis. Three topics are examined. First, the concept of information is considered, with particular reference on the value of theoretical approaches for LIS, and with emphasis on a comparison of Capurro’s approach with those of Popper and of Floridi. Second, the nature of the information-centric disciplines is considered, with particular reference to Capurro’s conception of a conjoined LIS discipline, rooted in the humanities. Third, Capurro’s ideas of digital ontology and digital hermeneutics are outlined, with emphasis on their value in providing a theoretical background for studying the new generation of immersive multisensory documents. It is concluded that the kind of rigorous study of foundational issues which characterises Capurro’s work will be of even greater importance for the LIS discipline in the future.
Academic libraries enable a wide range of digital scholarship activities, increasingly as a partner rather than as a service provider. Communicating that shift in role is challenging, not least as digital scholarship is a new field with many players whose activities on campus can be disjointed. The library's actual and potential contributions need to be broadcast to a diverse range of internal and external constituencies, primarily academic staff, university management, library colleagues and related project teams, often with different perspectives. Libraries have significant contributions to offer and a focused communications strategy is needed to embed libraries in digital scholarship and to create new perceptions of their role as enabling partners.
Academic libraries acquire and steward numeric datasets as well as large collections of image files, audio archives, digital texts, and other non-numeric resources. This article considers how a sample of libraries in the United States, Canada, and the United Kingdom present these collections and make them available for discovery. And, importantly, the article examines whether these non-numeric collections are positioned on the library website as research datasets, rather than as artifacts of limited local and/or historical interest. Findings from this analysis may help collection managers and web designers as libraries define their role in connecting users to research data.
Smartphone usage reframes our daily life activities to support ease, convenience, multitasking, and always connect with others wherever we are. The excessive use of a smartphone can generate a large amount of data. Recently, the term of big data is popularly used to describe data that is high volume, high velocity, and high variety and the exponential expansion and accessibility of data, both structured and unstructured. A smartphone with Internet produces a huge amount of data that will enhance users' experience through volume, value, variety and velocity. This study was done through inductive approach by distributing questionnaires in Brunei Darussalam (Brunei) to understand the smartphone habits of users in Brunei. The analysis had identified the concerns that become the focal point of a study on the habitual using of smartphones in daily activities. The study was conducted in specific context, yet the methods and findings can be used into broader contents and contexts. The majority of respondents use smartphones to access Internet excessively. Since, they depend on smartphones then they deserve to get better value added and services. The paper proposes the findings relating to the big data concept.
This presentation will set out the eScience agenda by explaining the current scientific data deluge and the case for a “Fourth Paradigm” for scientific exploration. Examples of data intensive science will be used to illustrate the explosion of data and the associated new challenges for data capture, curation, analysis, and sharing. The role of cloud computing, collaboration services, and research repositories will be discussed.
It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as data-intensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing.
Scientists need to ensure that their results will be managed for the long haul. Maintaining data takes big organization, says Clifford Lynch.
Big-data computing: Creating revolutionary breakthroughs in commerce, science, and society
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Big data, new epistemologies and paradigm shifts
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Big data for educational service management
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