An analysis of academic librarians competencies and skills for implementation of Big Data analytics in libraries: A correlational study

Abstract

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.

Full text

An analysis of academic
librarians competencies and
skills for implementation of
Big Data analytics in libraries
A correlational study
Khurshid Ahmad, Zheng JianMing and Muhammad Rafi
Department of Information Management, Nanjing University, Nanjing, China
Abstract
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.
Keywords Competencies, Academic libraries, Librarians, Skills, Analytics, Big Data
Paper type Research paper
1. Introduction
The development in this digital era, the use of information communication, Internet of Things
and cloud system technology caused an extensive data growth in almost every
area of life in this digital environment (Liu et al., 2018). In this perspective, due to the
information explosion and the extensive use and growth of data, the problem is being
faced to organize this massive unstructured growth of data. This situation provides genuine
reasons to evaluate these factors, as it is described that the growth of data on such a large
scaleisreferredtoas“Big Data”(Djafri et al., 2018). Nowadays, the Big Data is a hot topic of
discussion in the business, industry, education and government agencies globally;
the research and developmental practices are being conducted to overcome the challenges
and the opportunities of analytic use in Big Data (Cuzzocrea, 2014). It can say that Big Data is
leading pathway toward the emerging digital native economy of the world. However, in the
current literature, there is no unified definition of Big Data. Different circles have
defined Big Data in different ways. Some of the definitions mentioned as follows: “Big Data
refers to such datasets whose volume is outside the capacity of traditional software’s
(Manyika et al., 2011). Big Data comprises of those data sets which are so large and complex
that commonly used software’s are incapable of dealing with them”(Garcia and Wang 2013).
Data Technologies and
Applications
Vol. 53 No. 2, 2019
pp. 201-216
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-09-2018-0085
Received 30 September 2018
Revised 10 November 2018
Accepted 15 January 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2514-9288.htm
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Similarly, Laney (2001) first presented the concept of “Big Data”that it has some special
characteristics that differentiate it from ordinary data. These characteristics are categorized
and explained into 5Vs, that is, “huge volume, high velocity, high variety, low veracity, and
high value”(Jin et al., 2015). The size of Big Data is enormous in comparison with
regular ordinary data. There is no specified limitation for data volume and growth. The
velocity of Big Data refers to its dynamic and fast creation. The feature and variety of data
sets create the difficulties to the utilization process and organization of data. The data
that are collected systematically in a proper type by the data scientists or business
organization can be structured. However, some types of data are found in an unstructured
form that are gathered from different resources as e-mails and online collected data (Wang
et al., 2016). The development of Big Data creation, acquisition, storage and flow has come up
with some challenges and opportunities for modern libraries. The advancement in the
communication and information technology has changed the structures of organizations that
are being modified to compete the environmental and social changes in the society.
In this context, the importance of Big Data in librarianship is also being recognized in the
circles of library professionals (Zhan and Widén, 2017). Another research by Ilesanmi (2013)
described that libraries are the centers of knowledge organization, retrieval and dissemination
of information and maintaining information systems in the society to meet the requirements of
community. However, now the emergence of Big Data is forcing libraries to redesign the
patterns of their services that they usually had for carrying out their operations (Affelt, 2015).
To respond the change in this digital ere, Noh (2015) argued that the present form of libraries
can be converted into library 4.0. The library 4.0 can be defined as an intelligentlibrary, which
can analyze the massive data utilization and present the findings to their users. It means that
the unique feature of library 4.0 will be the handling of the substantial form of data. It also
shows that the emerging trend of Big Data is a helpful toward the improvement of libraries
and infrastructure development to provide the better services to the users of libraries and
community. In present situation, libraries are facing the challenge of data handling and the
lack of skills of library professionals. There is also a need for these skills to be improved to
handle the opportunities and issues that are created in this Big Data era (Gordon-Murnane,
2012). This shifting paradigm from traditional to contemporary library infrastructure and
services creates an unusual situation that is to be analyzed. This study has some important
implications of Big Data analytics for the academic libraries. The literature review of
conducted studies and the current paradigm shift helps us to know the competencies and
skills of librarians reflecting the libraries’capacity that is also necessary to address the
potential utilization of Big Data analytics in academic libraries.
1.1 Objectives
The objectives of this study are as follows:
(1) to explore the extent to which the Big Data analytics is being used in Pakistani
university libraries;
(2) to analyze the perceptions of LIS professionals toward the use of Big Data analytics; and
(3) to check the relationship between the competencies and skills of librarians for the
implementation of Big Data analytics.
2. Literature review
In previous studies, researchers have concentrated on the opportunities, merits and
demerits, required qualifications and skills, organization, and some other aspects of Big
Data analytics. In this section, the researchers review some selected studies conducted on
Big Data-related aspects. In this digital environment, the production, storage, organization
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and analysis of data have much increased the volume of data. In this scenario, the study of
Gorman (2015) focused on the new challenges and the opportunities for the libraries; he
proposed a new model for the implementation of Big Data analytics. It is also essential to
evaluate the strength and weakness of the organization to improve the services and
products. By knowing the challenges and opportunities of the organization, the
implementation of analytics helps to overcome these challenges. With this, Gunasekaran
et al. (2018) emphasized that the analytical data of large companies play an important role in
organizational flexibility by capturing and analyzing product requirements. It also
contributes to the planning, design and rapid development of supply chain networks, as well
as the production of new products. Yaqoob et al. (2016) divided the Big Data paradigm into
two phases, i.e. structuralism and functionalism. They further discussed that these are also
helpful to know the current trends in the rapid growth of Big Data. Zhuge (2015) argued that
in this era of Big Data, there is the emergence of new developing opportunities in the every
field of studies. Hence, these opportunities are also available for library and information
science (LIS) professionals to improve the services and collection of libraries.
The study results of Bedeley et al. (2018) show that the organizations are using the
analytics that focused on the application analysis because the results of the application
areeasytomeasure.Inadditiontoleveraging analytics that is used to improve the value
chain activities, many companies use different technologies to improve their
infrastructure or conduct research and development to support advanced analytics
practices. Lu et al. (2017) claimed that academic librarians are well aware of the concept of
Big Data analytics and they are forming the activities that related to data. However, there
is also a need for collaboration among the different sections of libraries for the
implementation of Big Data analytics. As the research of Triperina et al. (2018) reveals that
the used ontology in the academia provides the obviousness of classification, feminizing’s
to new solicitations, semantic web relationships and deepens of data mining. This process
leads to the implementation process of Big Data analytics in organizations. The tasks and
abilities of librarians can be the identification of data formats, acquisition, data
management and organization. In this context, librarians have been performing activities
such as data curation, visibility, interoperability of data, analysis and visualization of data
(Wang et al., 2016).
Similarly, Taylor-Sakyi (2016) discussed the Big Data phenomena by stating that
companies and organizations are thinking to reorganize the organizational process on the
growth of data for services and to enhance the product quality. For this approach, rational
database management systems are replacing the traditional systems of data management in
organizations. In 2005, the population of the internet was 10,24m but now reached 4,000m.
These numbers are showing that the opportunities for online researchers are increasing and
creating a more significant challenge for the organization of Big Data storage and its
privacy in the organizations (Schaich, 2018). The review of the previous literature reveals
that there are some studies conducted toward the implementation of Big Data analytics
in libraries. However, in the context of Pakistan as a country, there is a need to know
the understanding and to explore the competencies/skills of librarians about Big Data
analytics. The current research is an insight to add up the literature in the respective
field of studies.
2.1 Challenges of Big Data
In the process of data analytics, dealing with large data volume and growth is not a big
problem. The main issues are associated with the types of data, the velocity of data and
veracity of the data. Due to the different forms of the data, the choice and use of analytical
tools are difficult. It is a time taking task to structure the data format in the implementation
process of Big Data analytics in libraries, but sometimes the form of traditional
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unstructured data and online format are found in the random text such as voice data, videos
and images. Organizing such type of open data format is difficult. From the perspective of
the information industry, Big Data is a powerful driver of the next generation of information
technology, based primarily on a third platform, focusing on data, cloud computing, internet
the use of mobile and online social networking. The most complicated parts of Big Data
analysis are context analysis, data organization and evaluation. Therefore, in business
organizations, it is essential to define the potential of research economics and global
industrial scale at the organizational level that is also related with the limitations of data
protection and digital access models ( Jin et al., 2015). The challenges faced by libraries in
Big Data analytics focus on the lack of support from parent organizations. Concerns in this
regard include the lack of infrastructure to meet conservation needs, support for the
provision of knowledge or literacy of data and the lack of conservation initiatives in libraries
and information centers (Thomas and Urban, 2018).
2.2 Big Data analytics in libraries
According to the research of Read et al. (2015), there are always challenges associated
when starting a new library service. Tenopir et al. (2014) found that many LIS
professionals provide the data services by using the extension of traditional library
services, but some of them are more involved in helping the library users by developing
the plan of data management and organization. Read et al. (2015) also discussed that the
primary objective is to improve the efficiency and effectiveness of the institutional
approach by using data analysis to help universities address emerging issues related to
low retention rates and more extended periods. Analyzing the challenges of Big Data
analytics, Katal et al. (2013) claimed that volume, velocity, variety and veracity are the
four dimensions of Big Data. Besides this, DeVan (2016) study revealed the three more Vs
of Big Data with “variability,”the blends of constant data changing could have an
immense impact on the homogenization of data. The visualization of data in charts,
graphs and the value of organizational data are integrated challenges of analytics.
Themergingofthedataisdifficultafterfetching from the different sources. In library
(Goldberg et al., 2014), the types of data change dramatically and various volumes of
the data must be organized and supported to enable the services of the library. Due to the
digital environment, the needs of the library users will continuously grow in the future
(Showers, 2014). Librarians should have the ability to relate to the creation, management
and preservation of data (Semeler et al., 2017). The role of librarians is essential for Big
Data analytics, so there is a need to improve the skills and knowledge of librarians for the
implementations of Big Data analytics. Xie and Fox (2017) argued that for the application
of Big Data analytics, the library professionals do not have as such expertise to provide
new value-added services. In this context, the research of Atkinson (2018) reveals that
there is a need of client-centric approach for LIS professionals to be more integrated into
theacademicprocessandtounderstandandsupport different phases of research-based
needs of library users.
2.3 Competencies and skills of librarians for handling Big Data
The contemporary era of digital environment provides opportunities for librarians to
involve with data-related activities. LIS professionals should learn the skills and knowledge
about the provision of potential data services. The study of Fink et al. (2017) describes that
managers should seek to deploy strategic intelligence systems, starting with the investment
in the formation of a highly qualified and competent intelligence team, including expertise in
integration, analysis and data presentation. In this case, it is rare to find that some people do
not have simultaneous business experience in the organization. In the duties of a data
librarian, the indispensable core of his mission is to transform the different forms of data
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that are generated or can be helpful for the researchers and library users (Ahmad, 2017).
De Mauro et al. (2015) described that since companies are considering Big Data as
information assets, librarians too should bring in the practices of Big Data analytics.
Similarly, another study illustrated that the knowledge and skills of the new technology are
essential to the librarians, which can help in the provision of information service to the users
in time and high quality (Ullah and Anwar, 2013). Therefore, it is indeed to acquire the
landscape of libraries environment according to the context of Big Data analytics.
According to Oakleaf (2016) to achieve the goals and mitigate the challenges of learning
analytics, librarians should anticipate the use and learning of analysis in their institutions.
The librarians can prepare and develop their skills through collaboration with their
colleagues. Hoy (2014) also made the same point, with technological advancements,
librarians need to be familiar with the capabilities and problems inherent in large data and
use knowledge to help their customers choose the right tools. There is also the culture of
paranoid in librarianship to learn the new technological skills and emerging trends
(Braganza et al., 2017). In this perspective, there is a need to improve the skills and
competencies of LIS professionals in this competitive era of Big Data analytics.
3. Method of the study
In this study, a survey was carried out to achieve the targeted objectives. Accordingly, after a
thorough literature review, a research tool was developed to collect data from the respondents.
We developed a structured research questionnaire based on a five-point Likert scale and some
close-ended questions. 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.
We targeted the administrative librarians for data collection considering that they are the
best-informed persons about the status of technology and services in their libraries. These
professionals (academic librarians), in top management position, are involved in making
decision and implementation of new technology in central as well as in seminar libraries
of the universities. Overall, the administrative librarians are actively involved in designing
policies to all types of libraries in each university. All these universities are recognized
by higher education commission of Pakistan. These universities are functioning under the
umbrella of the higher education commission totaling 173. During the survey data were
received from 118 universities at the ratio of (68.2 percent) that were considered ultimate for
analysis. 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
association of variables. This technique was implemented by many researchers including Fan
et al. (2014) to analyze data for determining the complex relationship between values.
Following the same procedure, we applied it for explaining the quantitative variables
related to Big Data challenges in this study. In our research to test the statistical nature
and effectiveness of these measures, a pilot test was also carried out on
20 respondents. The pilot test allows the researchers to determine whether the respondents
understand the contents of the research tool entirely or not. The respondents of the pilot study
highlighted some weak points, and variables in the research tool were corrected for
respondent’s facilitation. The findings in our research showed a strong mutual correlation
po0.005 between the tested values.
4. Results of the study
The population of this study consisted of LIS professionals who varied in categories
of age, qualification and working experience in the public and private sector universities and
degree awarding institutes of Pakistan. Table I shows the demographic characteristics of
the respondents of this study. Among the 118 respondents, the number of males was
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90 (76.3 percent), and the number of female respondents was only 28 (23.7 percent).
The proportion of public sector organizations was 70 (59.3 percent), and the percentage of
private sector organizations was 48 (40.7 percent). The qualification of the respondents was as
follows: MLIS 80 (67.8 percent) and MS/MPhil was 27 (22.9 percent), whereas only 11
(9.3 percent) librarians had the PhD degree in LIS. Respondents’professional experiences were
divided into five categories. The number of LIS professionals having the experience of 11–15
years was 33 (28.0 percent), followed by 30 respondents (25.4 percent) with 5–10 years of
experience. The 21 respondents (17.7 percent) had more than 20 years of work experience in
LIS . In total, 18 (15.3 percent) of the LIS professionals working in academic institutions had
five years of work experience. The last ranking category of respondents’experience was 16–20
years, which consisted of 16 (13.6 percent) respondents. These results show that LIS
professionals working in academic institutions were having extensive work experience in the
field of LIS. The results also demonstrate that the respondents also improved their
qualifications by pursuing higher degrees in LIS. About 40 percent of the population was
having MS/MPhil and PhDs degrees in the field of LIS. It shows that LIS professionals are
concerned about their professional education.
4.1 Understanding of LIS professionals about Big Data analytics
The literature review reveals that Big Data analytics is a real source of competitive
advantage in an organization. It helps the working environment of libraries and information
centers to develop the better understand according to their working environment and
improve the collection and library services. This study evaluates the understanding level of
academic librarians about Big Data analytics. In Table II, the correlation between the
concept of Big Data analytics (1) has a significant association with “Big Data activities”(2),
with data volume (3), data forms (4) and with an increase of data (5). This analytics is
related to the practices of libraries toward the implementation process of Big Data analytics.
The results also show that librarians were familiar with all the variables of Big
Data analytics accept the different forms (4) of Big Data analytics, po0.0.005 of
librarians’views and understanding in Big Data analytics. It is also shown in Table II
that Pearson correlation was at the highest level in public and private sector academic
libraries of Pakistan.
4.2 Level of significance ( p o0.005), **represents significance, Variables 6
The results of Table II reveal that there is a significant level of understanding of the
concept, activities, data volume and data increase in the practices of academic libraries. The
librarians are performing these activities to some context in their libraries. However, due to
Measure Items Frequency Percentage
Gender Male 90 76.3
Female 28 23.7
Type of institution Public sector 70 59.3
Private sector 48 40.7
Academic qualification MLIS 80 67.8
MS/MPhil 27 22.9
PhD 11 9.3
Professional experience W5 years 18 15.3
5–10 years 30 25.4
11–15 years 33 28.0
16–20 years 16 13.6
o20 years 21 17.7
Table I.
Profile of the
respondents
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the technological barriers and Big Data Vs. (volume, velocity, variety, variability, veracity,
visualization and value), they are not able to recognize the different forms of Big Data.
In response to the first question of this study, this table validates that librarians of the
Pakistani academic libraries are well aware of the emerging trends and they are practicing
the Big Data analytics in their respective libraries. Therefore, Big Data reveals not only the
tendency of library digitization but also bring opportunity, development and challenge.
It also encourages the creation of technical solutions. The values also generated by the
proper manipulation and use of large amounts of data found helpful for libraries and
information organizations (Zhan and Widén, 2017).
The scatter plot matrix of correlation between the variables shown in Figure 1 reveals
that there is a significant correlation between all the variables of required skills of librarians
for the implementation of Big Data analytics in libraries. The Pearson correlation also
indicates the degree to which these factors have a substantial positive impact on each other.
It demonstrates that librarians have a strong understanding of the importance and
contribution toward the improvement of libraries. It also indicates that the libraries will be
able to improve their data-related services if they have these skills.
4.3 Required competencies and skills of LIS professionals for Big Data analytics
The present research identifies and addresses many critical concerns, involving Big Data
and educational analytics that are being practiced in different academic libraries and their
parent organizations. Librarians’competencies are considered an integral requirement to
participate in activities related to Big Data analytics. As the research of Federer (2018)
focused on the skills of data librarians and explained that LIS professionals as data scientist
are assorted experts in the information society with different academic and career
backgrounds to deal with different forms of work. As the emerging major trends expected
among data science librarians, there are differences in the type of work data that librarians
can have concerning specific kinds of valuable expertise and the type of jobs that have to
employ the different data librarians. In this context, two professional groups, experts and
data generalists, described here suggest that the science of databases may not play a unique
role, but rather an area that allows professionals to respond to their interests or to meet of
the need of community users. With this, the study has also examined the competencies,
practices and procedures to extract the required information and data in different formats in
a diverse and varied information system. It involves in the various forms of data,
Correlation
1* Concept 2. Activities 3. Volume 4. Forms 5. Increase
0.490
2 0.000**
−0.330 −0.552
3 0.000** 0.000**
−0.253 −0.416 0.715
4 0.006** 0.000** 0.000**
0.297 −0.374 0.532 0.583
5 0.001** 0.000** 0.000** 0.000**
−0.075 −0.120 0.321 0.331 0.409
6 0.422 0.197 0.000** 0.000** 0.000**
Notes: The activities (2) of big data are associated with the volume (3), forms (4) of data and data increase (4)
for the practices of Big Data analytics in libraries. The variable 6 is also correlated with the volume (3), forms
(4) Increase (5) of data activities in libraries. 1* is the variable “Concept”and **show the significance of the
correlation between the variables
Table II.
Correlation between
understanding
and practices of
Big Data analytics
(journal format)
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acquisition, management and organization, interoperability, data quality, metadata skills,
data curation, culture data, data preservation, data analysis, data visualization and the
policies/ethics for the practices of Big Data analytics in academic libraries. It also gives the
insight to know the role of librarians in the perspective and implementation of these skills in
their working environment and practices in libraries.
4.4 Level of significance ( p o0.005), **represents significance, Variables 12
Table III demonstrates that a significant correlation exists among all the 12 variables of
required competencies and skills. It is based on the variables that relate to the required
competencies and skills of librarians for the implementation of Big Data analytics in
libraries. In this context, the research of Lee and Kim (2018) reveals that in the field of
information and communication technologies, the visualization and analysis of the data in
an organization’s database are called ecosystem links. Leveraging and providing data to
develop the infrastructure and to improve services it is a powerful tool in the organization.
There is no doubt that without these required competencies of Big Data analytics the
strategy of implementing Big Data analytics in academic libraries cannot be applied.
The study of Johnson (2017) discussed that the collaboration among the different sections
enables librarians to demonstrate the developed value of additional professionals’expertise.
The visual analytics also lets us discover the likely or unlikely results mainly in Big Data
formats. The results of Table III also demonstrate that librarians have sufficient
competencies and skills to tackle Big Data analytics in their respective libraries and
organizations. The following details of the correlation among 12 variables of librarians
required competencies and skills are:
•The different formats (1) of Big Data analytics skills are significantly correlated with
(2) acquisition of data, (3) management and organization of data, (4) interoperability,
(5) quality, (6) metadata skills, (7) curation, (8) culture of organization, (9) preservation, (10)
Concept Activities Volume Forms Increase Issue
Concept
Activities
Volume
Forms
Increase
Issue
Figure 1.
Scatter plot matrix
for correlation
between variables
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Correlation
1* Formats 2. Acquisition 3. Management 4. Interoperability 5. Quality 6. Metadata 7. Curation 8. Culture 9. Preservation 10. Analysis 11. Visualization
2 0.553
0.000**
3 0.497 0.630
0.000** 0.000**
4 0.517 0.608 0.555
0.000** 0.000** 0.000**
5 0.492 0.559 0.595 0.562
0.000** 0.000** 0.000** 0.000**
6 0.446 0.457 0.378 0.460 0.503
0.000** 0.000** 0.000** 0.000 0.000**
7 0.460 0.474 0.469 0.481 0.455 0.623
0.000** 0.000** 0.000** 0.000** 0.000** 0.000**
8 0.400 0.544 0.445 0.553 0.473 0.482 0.704
0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000**
9 0.281 0.330 0.321 0.460 0.442 0.484 0.493 0.461
0.002** 0.000** 0.000 0.000** 0.000** 0.000 0.000** 0.000**
10 0.383 0.334 0.417 0.579 0.507 0.410 0.461 0.544 0.578
0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000**
11 0.358 0.425 0.415 0.618 0.478 0.327 0.515 0.669 0.481
0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000**
12 0.466 0.348 0.429 0.505 0.454 0.369 0.388 0.438 0.442 0.544 0.582
0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000**
Notes: The significance of variable 2 and 12 described in the results. *Described the correlation between the variables. **Significant at the 0.01 level (two-tailed)
Table III.
Correlation between
librarians’required
competencies and
skills for the
implementation of Big
Data analytics
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analysis, (11) visualization of data and (12) ethics involved for the implementation of Big
Data analytics in libraries. Thus, the required skills of librarians are significant for Big
Data analytics in libraries. The variable (2) acquisition of data also significantly correlates
with (1) different formats, (3) management and organization of data, (4) interoperability,
(5) quality, (6) metadata skills, (7) curation (8) culture of organization, (9) preservation (10)
analysis, (11) visualization of data and (12) ethics. The acquisition of data in library
practices leads to the better skill of Big Data analytics in the libraries.
•Management and organization of data (3) is strongly correlates with the (1) different
formats, (2) acquisition of data, (4) interoperability (5) quality (6) metadata skills (7)
curation (8) culture of organization, (9) preservation (10) analysis, (11) visualization of
data and (12) ethics. Thus, the organization and management of data are having a
significant impact on other competencies of Big Data analytics.
•The variable (4) Interoperability robustly correlates with other skills of Big Data
analytics in libraries, such as association with (1) different formats, (2) acquisition
of data, (3) management and organization of data, (4) interoperability, (5) quality,
(6) metadata skills, (7) curation, (8) culture of organization, (9) preservation (10)
analysis, (11) visualization of data and (12) ethic.
•Awareness about the quality of data is a very essential skill for Big Data analytics;
librarians have the competencies to know that the data quality is also significantly
correlated with (1) different formats of data, (2) acquisition of data, (3) management
and organization of data (4) interoperability of data, (6) metadata skills (7) curation,
(8) culture of organization, (9) preservation (10) analysis, (11) visualization of data
and (12) ethics.
•In libraries, LIS professional has to perform the different activities of variable
metadata (6). It includes cataloging, reference services, technical section processing,
creating library reports, digital resources utilization, etc. Thus, a significant
correlation is also found with described variables of the study, and it is helpful for the
clustering of data.
•Data curation (7) is a vital element in the digital environment. It also involves the
different scholarships of libraries and information centers. A significant correlation is
also found in this variable with all other variables of the study.
•Organizational culture (8) also has a significant correlation with other required skills
of librarians because it affects all activities of the organization.
•The role of libraries and librarians involved in the preservation of data is essential. In
this perspective, the skill of data preservation (9) also has a significant correlation
with other skills of library professionals.
•Data analysis (10) is very demanding and technical skill for the implementation of
Big Data analytics. A significant correlation is also found in data analysis with other
variables of Big Data analytics.
•As described data analysis (10) variable in our study, data visualization (11) also has
a significant correlation with all variables of the study.
•As all the described skills of Big Data analytics are necessarily important, ethics (12)
is also essential to perform any data-related activity within the organization.
On the bases of these results of the study, the technical skills toward the implementation of
BDA are essential for university librarians. Similarly, Semeler et al. (2017) narrated that in
the age of digital environment, this is a significant factor for data formats, acquisition,
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management and organization of data, interoperability, quality, metadata, data curation,
organizational culture, preservation, data analysis and visualization. In this perspective,
“ethics”found a significant correlation with the skills of Big Data analytics in libraries.
Our results associated with the study of Burton et al. (2018) highlighted that the research
librarians are a group of actors in the networks on which researchers rely in the course of
university research. The value of privacy, ethics and fair access to the information is critical
to library science professionals, making librarians a unique partner for researchers and
others. It is part of the universities’research culture support network. At the same time,
there is an international dialogue on the ethical use of data in this digital environment.
Librarians have the opportunity to find their role in supporting researchers by addressing
new ethical issues in research, in particular by providing services to the functional units that
are responsible for ethics and coordinating in universities. Libraries have to coordinate
with the parent organization to ensure the data literacy instructions and the literacy of
data ethics. There is also the need for university librarians to improve their skills and
practices by participating in different training programs and conferences, due to the
advancement of technology the services and the paradigm of library infrastructure being
changed with the passage of time. The improvement of the library structure and functions is
associated with the skills of librarians and library works. The significance of these Big data
analytical skills plays a role of interplay with the services and practices of libraries. On the
bases of these skills, the library community will serve in a better way. And the research
culture of the university can be improved by the alignment of these technologies and the
practices of data analytics in the libraries and universities.
The scatter plot matrix of correlation between the variables on the bases of Table III
shown in Figure 2 also reveals a significant relationship among all variables of research.
The results indicate that there is a strong correlation in the required skills of librarians
toward the implementation of Big Data analytics in academic libraries. These skills are
helpful to improve the competencies of librarians in the educational environment and also to
meet the research needs of library users to provide the best research-oriented services. TH
competencies on these skills help to choose the right tool for the analytics application
process. With the study of Netto et al. (2014), the university librarians can get involved in
decision making and can take the initiatives toward the implementation of new technology
in their libraries. By providing information services to the university community, they also
get involved in the process of introducing new research trends and paradigm to improve the
research culture in academia.
5. Discussion
The concept and the implementation of Big Data analytics are growing day by day.
The environment of Big Data has provided a vast opportunity for competition within the
organizations. In this scenario, the role of librarians is also changing, and now they are
working as a data scientist, data curators and digital services managers. In this perspective,
librarians are involved in data acquisition, curation, interoperability, data organization,
metadata skills, data preservation, analysis and visualization. As the traditional practices of
libraries, librarians have to follow the policies and ethics for the methods of Big Data
analytics in libraries. The data privacy and acquisition are found critical issues nowadays.
The competencies of handling Big Data vary from organization to organization. It also
depends on the format, value, volume and organizational culture. The competencies and
skills lead toward the proper implementation of Big Data analytics in libraries.
In this research, the researchers evaluated the correlation of required competencies
and skills of librarians for the implementation of Big Data in academic libraries.
As discussed in the literature review of the study, Big Data has emerged as a new,
developing and multidisciplinary field. In other words, the researchers used the
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results to say that academic libraries should be able to conduct Big Data analysis. The
variables of the study are developed by the in-depth study of the literature review. First,
Table II presents the six variables related to the understanding and basic knowledge of
Big Data analytics. Three variables are compared to explore the extent to which the Big
Data analytics are used in academic libraries. The researchers found a strong correlation
among all the variables. Only two variables had an insignificant relationship. It shows
that librarians have the concept and understandings of Big Data analytics, but due
to the rapid growth and volume of data, they are not familiar with the different formats of
Big Data. The findings also indicate that librarians are well aware of the analytics
of Big Data that are used in academic libraries. The level of analytics is practiced
to some context in their libraries. In this perspective, to know the analytics level
of Big Data in librarianship, the study of Zhan and Widén (2017) referred to the
different competencies and skills of librarians for the practices and utilization of Big
Data analytics. It also highlighted the working environment of libraries that emphases on
the relevant practices of Big Data Vs.
This research also emphasizes on the analysis of correlation in the required skills and
competencies of librarians. In this context, the researchers analyzed the association between
data formats, acquisition, data management and organization, quality of data, metadata skills
and data culture within the organization to evaluate the last objective of our study to
determine the level of skills and competencies of LIS professionals toward the Big Data
Frm Acqstn Mangnt Intpro Quality Meta Curtn Cultr Presrvn Analysis Vislsn Ethics
Frm Acqstn Mangnt Intpro Quality Meta Curtn Cultr Presrvn Analysis Vislsn Ethics
Figure 2.
Scatter plot matrix for
correlation of
variables
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analytics, see Table III. The researchers found a strong correlation among all the described
activities. The findings of Semeler et al. (2017) study indicated that in this digital era and Big
Data analytics, there should be provisions for librarians that they should perform the practices
in their libraries as data administrator, to preserve, to organize and to evaluate the data.
This study reveals that LIS professionals are working in academic libraries of Pakistan having
a strong correlation between their skills of data visualization depending on organizational
culture and keeping in view the quality of data. Because of the corporate environment, it is an
essential factor with the required competencies and skills and for the implementations of Big
Data analytics. The librarians are also well aware of the data policy and ethics for the
preservation, data analysis and visualization. With this, there is a need that librarians have to
recognize the Big Data technologies and develop their skills through participation in
accelerated courses and workshops. Extensive data analysis in the library also requires
programming language and coding. In this way the analytics of Big Data in the library can
significantly improve. These existing library services are developed by the needs of library
users to adjust the library need-based services.
Librarians were traditionally involved in the practices of acquiring, organizing,
retrieving, collecting, disseminating and hoarding of information in their libraries. Their
methods of this information organization have historically been in the form of scientific
exchanges of ideas, such as books and serials publications. In this digital environment, they
have now shifted their practices to more inclusive of all types of information, irrespective of
their form. This whole paradigm shift that has broadened an area not only focused on
primarily textual scientific publications, but also on the review of the digital footprints of
data provided by researchers and library users. However, there is a need to develop new
tools for libraries and process solutions for Big Data analysis. These developments in the
library services can adapt to the needs of library users and improve and collection
development policy. This study shows that Big Data flow is being dynamically changed
based on expectation or expected external and internal influences. The study lays the
foundation for the further development of dynamic skills of Big Data work, and based on the
strategic insights gained from the Big Data initiative, a method for the organization to
adjust and transform its capabilities is developed. Researchers of LIS professionals can
explore the causal relationship between the discovery of Big Data initiatives and changes in
the organizational requirements of libraries.
6. Conclusions
In this age of information society, academic institutions, public and private business
organizations and companies are generating a large amount of data. The capabilities of LIS
professionals based on Big Data analytics facilitate to ascertain emphases of the analytics in
academic libraries. Managing the vast and complex data is a significant challenge for
business and educational organizations. This study analyzed the awareness and
engagement of Pakistani librarians toward Big Data. The relationship between the
competencies and skills of academic librarians for the implementation of Big Data analytics
is also analyzed. The study concludes that librarians have the understanding about the
concept of Big Data analytics.
Moreover, they are primarily engaged in Big Data-related activities in their respective
libraries. The study also concludes that there exists a strong correlation between the
competencies and skills of librarians for the implementation of Big Data analytics in
libraries. The skills and competencies of librarians are fundamentally essential to offer
quality services in libraries. Emerging trends such as Big Data analytics are need of
the hour to survive in the information society. Therefore, librarians must understand
the importance and nature of Big Data technologies and develop their skills to cope
with it accordingly. They should engage themselves in Big Data-related activities.
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Librarians also need to learn and share Big Data analytics-related expertise, techniques
with their peers. Awareness about the design and implementation of Big Data analytics
should be created among LIS professionals. In order to proper implement and practice the
Big Data analytics in libraries at large, librarians of Pakistani universities need to
conduct trainings and conferences on Big Data. The computer programming languages
and coding are also helpful for the implementation of Big Data analytics in libraries.
This would result in a lot of improvement for the practices of Big Data analytics in
libraries. Studies regarding challenges associated with handling of Big Data are need to be
conducted by prospective researchers.
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Corresponding author
Khurshid Ahmad can be contacted at: khurshid.abaloch@gmail.com
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