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Data librarianship, or data-driven librarianship, is the combination of information science, data science and e-science fields and is gaining gradual importance in the library and information science (LIS) profession. Hence, this study investigates the data librarianship core concepts (motivational factors, challenges, skills and appropriate training platforms) to learn and successfully launch data librarianship services.

A survey method was used and the data were collected through online questionnaire. Purposive sampling method was applied and 132 responses were received with 76 respondents from the public and 56 from the private sector universities of Pakistan. The statistical package for social sciences (SPSS version 25) was used, and descriptive and inferential statistics were applied to analyzed the data.

LIS professionals understand the importance of data-driven library services and perceive that such services are helpful in evolving the image of the library, helping with the establishment of institutional data repositories/data banks, developing data resources and services for library patrons and especially researchers, and receiving appreciation and acknowledgment from the higher authorities. The major challenges that emerged from the data were: missing data policies, limited training opportunities for data librarianship roles, no additional financial benefits, lack of infrastructure and systems, lack of organizational support for the initiation of data-driven services, and lack of skills, knowledge and expertise. Data librarianship is in its early stages in Pakistan, and consequently, the LIS professionals are lacking basic, advanced and technical data-driven skills.

The policy, theoretical and practical implications describe an immediate need for framing data policies. Such policies will help the libraries or any other relevant entities to store the data and assign metadata and documentation in such a way that it is easy to retrieve and reusable for others.

This is the first study in Pakistan to investigate the perceptions of LIS professionals about data librarianship core concepts: motivational factors, challenges, skills and appropriate training platforms to grasp data-driven skills and successfully launch library services.

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.

This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.

The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.

These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.

This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.

The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.

This study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.

The resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.

Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.

At a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

Collection development in a post-subject librarian age needs to be done differently; utilising data, metadata, analytical tools and automation more fully may offer new possibilities. The purpose of this paper is to report and evaluate an exploratory project into new techniques for collection development at the University of Manchester Library.

The project employed a cross-team approach where a relatively large number of staff tried some innovative and experimental approaches to individual aspects of a large and complex task in a large, research-intensive university library. The overriding aim was to exploit data to support decision making and to push automation as far as possible.

The quality of (meta)data remains a huge hindrance to data-driven approaches. A proper understanding of usage data is an urgent but intractable issue. Human input and relationships are still important. Data are nothing without analysis, and many librarians currently lack the data fluency to work confidently in a world of dynamic content curation.

Librarians need both to re-skill and to change their self-identification and the philosophy that underlies it if they are to achieve confident, data fluency.

The University of Manchester Library was one of the first libraries in the UK to make a thoroughgoing structural change from subject-based to functional teams. This paper will be of value to other libraries moving in this direction, and to those looking to make more use of data-driven decision making in collections management.

This paper will provide an overview of the contemporary surveillance environment in the age of Big Data and an insight into the complexities and overlap between security, bodily and informational surveillance as well as the subsequent impacts on privacy and democracy. These impacts include the ethical dilemmas facing librarians and information scientists as they endeavour to uphold principles of equality of access to information, and the support of intellectual freedom in private in an increasingly politicised informational environment. If we accept that privacy is integral to the notion of learning, free thought and intellectual exploration and a crucial element in the separation of the state and the individual in democratic society, then the emergence of the data age and the all-encompassing surveillance and exposure of once private acts will undoubtedly lead to the reimagining of the social and political elements of society.

Data literacy empowers college students, equipping them with essential skills necessary for their personal lives and careers in today’s data-driven world. This study aims to explore how community college students evaluate their data literacy and further examine demographic and educational/career advancement disparities in their self-assessed data literacy levels.

An online survey presenting a data literacy self-assessment scale was distributed and completed by 570 students at four community colleges. Statistical tests were performed between the data literacy factor scores and students’ demographic and educational/career advancement variables.

Male students rated their data literacy skills higher than females. The 18–19 age group has relatively lower confidence in their data literacy scores than other age groups. High school graduates do not feel proficient in data literacy to the level required for college and the workplace. Full-time employed students demonstrate more confidence in their data literacy than part-time and nonemployed students.

Given the lack of research on community college students’ data literacy, the findings of this study can be valuable in designing and implementing data literacy training programs for different groups of community college students.

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 describe how a medium‐sized university library implemented activity‐based costing (ABC) and other broad‐based decision‐making strategies to make the necessary budgetary cuts and refocus library services accordingly.

The paper uses ABC to quantify the cost‐drive of services in the library.

Given the current budgetary cuts academic libraries are facing, a rational, data‐driven, ABC approach can lead to constructive organizational decision making and outcomes.

The paper demonstrates the value of activity‐based costing in assessing services and addressing issues on how libraries can do more with less.

The author shares information about the American Library Association’s 133rd annual conference in Las Vegas. This conference served as an open forum for participants on incredible range of topics, namely, e-books, digital content, innovation and community engagement, library instruction, copyright, outreach, privacy, library advocacy, literacy, data-driven decision making, intellectual freedom, teen services, gaming, leadership and best practices on a range of library-related concerns.

This report is a brief outline of ALA 2014 annual conference in Las Vegas with focus on international programs. It summarizes selective events, including author’s presentation on e-books advocacy in India, International Librarians’ Reception and paper session organized by IRRT.

ALA annual conference at Las Vegas was one of the best professional development opportunities for librarians, with a large variety of programs, activities and exhibition. The event attracted librarians from all sectors and work environments from many countries and highlighted issues in librarianship and latest technology development; its use in libraries will be of interest to this journal’s readership, especially information professionals from all over the world.

ALA Annual conference in June 26-July 1, 2014 was the largest conference of librarians touched all topics in librarianship. It is difficult to describe the entire conference events even briefly in an article. The author’s experiences during the international events are highlighted.

In particular, it is worth mentoring new and more efficient solutions that can meet the increasingly specific needs of each company, especially in food management. A business intelligence (BI) solution can help your food company better understand and manage business processes more effectively. Management information is essential for all levels of an organisation to make quick and correct decisions. However, what exactly is BI, and what can it mean for a food company?

The PRISMA stands for (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and content analysis strategy used the SLR (systematic literature review) methodology to examine 151 papers published in peer-reviewed academic journals and industry reports between 2016 and 2023.

The findings show that artificial intelligence and digitalisation are linked to the UN 2030 Agenda. BI management ranks first (66%), followed by crop and land mapping systems (40%), agricultural machinery monitoring tools (39%) and decision support systems (31%). The road to digital transformation remains extended, with the main impediments being more compatibility between enterprise systems and a shortage of expertise.

The section relating to methodological perspective adopts the PRISMA methodology for systematic review. Interoperability is easily managed by assigning qualified teams to projects. The added value of a consulting firm with extensive project management experience in the food industry is closely related to the results achieved.

BI: What exactly is it, and why a data-driven culture is essential in the food and beverage industry?