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Abstract

Digital data management usually involves data sciences, big data, research data, open data, data curation, its utilization, distribution as well as its re-utilization. All these components need to be focused while targeting digital data management. Thus data puts a significant burden on research institutes because it is the authority that decides the responsible for the whole course of the procedure. It is of prime importance for data librarians serving in data-centric age to know regarding LIS principles, theories, and other related skills that are mandatory data services for management and support of data. This paper sums up the reviews and provided of latest and highly cited research studies through the search on Google-scholar regarding the emerging trends of digital data management. Moreover, this paper includes a diagnostic assessment of emerging data environment concerning recent advancements in data science, big data, research data, open data, data management and progress in duties of librarians, presentation of detailed data, the function of data libraries as well as librarians concerning data users. It is supposed to be an exciting era to work in a library as its role is expanding with specific new challenges. It is the need of the current period to educate librarians, library science researchers, and students regarding understanding, utility, and management of data to meet the requirements of data science librarians.
Emerging Trends of Data Management and Data Analytical
practices in Academic Libraries: A Theoretical Lens
By
Naimat Ullah Shah1
Dr. Salman Bin Naeem2
Dr. Rubina Bhatti3
1Ph.D Scholar,
Department of Library and Information Sciences,Islamia Univesity Bahawalpur
1*Corresponding author’s email: naimat784@gmail.com
2Assistant Professor,
Department of Library and Information Sciences, Islamia Univesity Bahawalpur
3Professor,
Department of Library and Information Sciences, Islamia Univesity Bahawalpur
Abstract
Digital data management usually involves data sciences, big data, research data, open
data, data curation, its utilization, distribution as well as its re-utilization. All these components
need to be focused while targeting digital data management. Thus data puts a significant burden
on research institutes because it is the authority that decides the responsible for the whole
course of the procedure. It is of prime importance for data librarians serving in data-centric age
to know regarding LIS principles, theories, and other related skills that are mandatory data
services for management and support of data. This paper sums up the reviews and provided of
latest and highly cited research studies through the search on Google-scholar regarding the
emerging trends of digital data management.
Moreover, this paper includes a diagnostic assessment of emerging data environment
concerning recent advancements in data science, big data, research data, open data, data
management and progress in duties of librarians, presentation of detailed data, the function of
data libraries as well as librarians concerning data users. It is supposed to be an exciting era to
Journal of Information and Computational Science
Volume 10 Issue 4 - 2020
ISSN: 1548-7741
www.joics.org
545
work in a library as its role is expanding with specific new challenges. It is the need of the
current period to educate librarians, library science researchers, and students regarding
understanding, utility, and management of data to meet the requirements of data science
librarians.
Keywords: Data management, Data librarians, Data Scientists, Data Science, Big Data.
1. Introduction
The emerging academic innovations, including web, technological innovation, PC based
instructive games, information gathering tools, programming and technological advancements
are altogether compelling changes in the each feature of advanced education. These innovative
changes are especially influencing the teaching learning and assessment process in advanced
research. Libraries have been an essential part of the advanced data centric and technological
innovations [1](Kumbhar 2014)(Kumbhar)[1].
Research scholars are basically working on data creation and drawing inferences form it,
however the choices they take in choosing what type of technique and tools are used to gather
and store their data, who possesses it, who approaches, whatwhich type of repository they will
use to break down it, what yields there will be from the research study, and incalculable different
exercises will have an effect further way exercised as a usual attachment of digital data-
management and electronic resource tasks. Still, the main thing is the level of awareness and
skills on how to know about domain considerate needed [2](Henty 2008)(Henty)[2]. This data-
centric era has required that academic instutions establish strategies, policies, setups, and data-
centric services for data-management through assisting the scholars in developing, gathering,
using, assessing, managing, and sharing the digital datasets [3](Pinfield, Cox et al. 2014).
The methods and skills of using data and data analytical techniques are not only
changing the library roles, but it leads library and information schools to re-evaluate established
practices and approaches to the Information centers, acquire, gather, stock, use, and disseminate
the digital data sets as essential components inside their routine services. Library circulation,
flow of data, data gathering, users accounts, circulating data, library magazines and journal
expenditure data, footfall data, bibliometric data and creation of digital data sets, collection,
accession, users as well as scholars, students, administrative officers, the citizens, e.g.
[4](Burton, Lyon et al. 2018)(Burton et al.)[4]. In the educational scenario and scientific
libraries, the term data looked all over the place, where we see about linked-data, big-data,
open-data, research-data, data-science, digital-data, etc. [5](Frederick 2016)(Frederick)[5].
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1.1What is data?
In a broader sense, the data is defined as the assortment of digits or sequence of text or a
thread of alphanumeric codes, which will not necessarily possess a firm meaning. Data itself
rarely holds a unique value [5](Frederick 2016)(Frederick)[5]. Data is an element or bit of
information which has the observed volume to be gathered and kept in a way to be recognized,
used again, and again. Data is famously divided into two types structured-data and
unstructured-data. [6](Ramkumar 2018)(Ramkumar)[6].
Data is regarded as of prime importance in research activities because of its towering worth in
information. It is the data that concerns information to possess a level of significance that
considered as critical for deciding on a particular matter. Data is supposed to have appeared in a
variety of ways, including surveys, statistical declarations, research outcomes, graphical
presentations, figures or illustrations, interviews, journal statements, etc. [7](Mikalef, Pappas et
al. 2018)(Mikalef et al.)[7]. Data is known as a vital factor for science and technology. It plays a
crucial job in the generation of hypothesis, it is assessment and making of any desired
amendments in a belief, putting forward of theories and suggesting models. It is data that has
made it possible for technology to be practiced and show applications in day to day life
[8](Oyelude 2017)(Oyelude)[8].
2. Research Problem
Presently the research studies are more data-centric, and scholars are facing the novel type of
barriers in accessing, disseminating and management of digital data, so libraries are starts to
offering data services, which includes training and instructing, management of data, planning
and data guidance, data-curation and stewardship, and data imagining [9](Federer
2018)(Federer)[9]. Science is not only the owner and creator but also the managing authority of
the most significant share of universal data. The general opinion of the public regards data to be
brought into existence by the scientist community for scientific purposes. However, if we talk
about data concerning digital stuff, it may be utilized for multiple intellectual goals
[10](Clement, Blau et al. 2017)(Clement et al.)[10]. In current data centric scenario it is the need
of the day that such type of studies are required which creats awareness with the help of research
studies regarding data practices, data services, data management and data analytics tools and
techniques used in libraries.
3. Objectives of the study
To explore the emerging trends of data practices and data management in academic libraries
with the help of related literature.
To assess the duties of librarians and function of libraries in emerging data centric age with
the help of related literature.
To provide a possible solution and recommendations based on the conclusion of the study.
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4. Methodology
This paper sums up the reviews of researchers ideas and theories regarding the emerging
trends in data centric age, like data management, and data analytical practices, dig out by latest
and highly cited research papers using Google-Scholar. A theoretical lens was drawn from
insightful articles, books and some other sources applicable to this specific issue, the core of
research, and gives a basic assessment of the previous works done in connection to the research
objectives being explored.
Figure: 1 Theoratical Lens Model
The Model shows in figure -1 that all the emerging trends are related with each others and based
on basic trend of data management and data services in academic libraries.
Emerging trends
of data
management and
data services
Data Centric
Teaching
and
Learning
Digital Data
Management
Practices
Data
Mining in
Libraries
Data
Curation in
Libraries
Data
Science
and
Libraries
Data Savvy
future of
data
science
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5. Theoretical Lens
The contribution of information centers in e-science is realized as an amplification of
data management and electronic stewardship tasks, but it is also interrogated that the technical
level of how to know and also understanding of domain is required. Information centers have
been capable of linking data management with historical and current extents of library practices,
as well as resource collection, data discovering and resource management; referral facilities,
data-literacy, and scholarly meetings; and research gathering, open-data, and digital repositories
[11](Creamer, Martin et al. 2014)(A. T. Creamer et al.)[11].
Current emerging technologies in stats and software engineering tied with a richness of
information have provided a new specialized ecosystem called data science. Data science
techniques and tools have changed trade, health science, and management, and it will also
change other areas like libraries. There is the need of Investments to enlarge the recent flow of
support in the area of data science like the world is more and more infused with data,
information professional having a critical job in the growth of the data science future. There is a
need of current scenario that information professionals inquire what data science roles can fill
out through current assets by librarians [4](Burton, Lyon et al. 2018)(Burton et al.)[4].
5.1 Data Centric Teaching and Learning Environment
There is a developing eagerness for utilizing new sources for data customizing and learning
capability and for implementation data in academic ways. The academic disciplines have been
constantly associated with creating and utilizing inventive evaluation tools and techniques
[1](Kumbhar 2014)(Kumbhar)[1]. Research associated librarys meets up the teaching and
learning process, libraries additionally serve patrons in academic institutions. Students gain
information from teaching network, likewise, they gain information from librarys needed
materials and data services. In that capacity, similar to teachers, libraries ought to be ready to
utilize library resources to gauge students learning performance. These data centric resources can
likewise be used to demonstrate librarys worth [12](Gilchrist and Oakleaf 2012)(Gilchrist and
Oakleaf)[12].
Libraries are hosting to a large number of the components of this information base action as an
empowering the online learning activities. Giving access to data sources and reposotories such as
files, print and electronic assortments and databases could teach clients further. It is an exciting
job of the library in such a learning environment [13](Chandra and Patkar 2007)(Chandra and
Patkar)[13]. Digital data management and literacy are normal and routine services in academic
libraries; however they are just two parts of research lifecycle. Data proficiency is the capacity to
read, to make and to impart information as data [14](Thomas and Urban 2018)(Thomas and
Urban)[14].
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5.2 Digital Data management practices in libraries
The history of case studies of data management in libraries started to arise in 2003 when a
particular issue published by Library Trends on digital repositories which demonstrate the
institutional struggles to include research datasets at Johns Hopkins University, Purdue
University, and the University of Minnesota in their archives [15](Macdonald and Martinez-
Uribe 2005)(Macdonald and Martinez-Uribe)[15]. The process of gathering and keeping data can
start with idealization and preparation of use and re-use. According to the educational scenario, it
will begin with the funding procedure. Researchers are hardly skilled in terms of lengthy periods
of accessing and preserving data. Presently stack holders necessitate data-management strategies,
researchers are observing for trained support at the time of proposal development. Libraries have
concerned themselves with the access of digital object and its management is the main concern
of libraries from the starting of the data-revolution [16](Heidorn 2011)(Heidorn)[16] .
Libraries get numerous demands to find data sets; to help with governmentally required data
management projects that are, clarifications of how analysts will deal with data both during and
after a research study; and to obtain complex data sets for significant level research activities.
The interest in data management benefits in libraries requires new asset areas, specialized
foundation, arrangements, and planned models [17](Johnston, Carlson et al. 2017)(Johnston et
al.)[17]. The subject of data management and library contribution is well known in current
library studies, and more libraries have created or are creating projects to get engaged with data
management practices. A significant part of the literature depicting reviews of data management
practices and projects in libraries debates studies of data curators themselves[18](Davies,
Rahman et al. 2017)(Davies et al.)[18].
5.3 Data Mining in Libraries
Data mining, also called data disclosure in databases, can be characterized as examining,
investigating enormous data repositories and finding verifiable, but possibly helpful data
[19](Han, Pei et al. 2011)(Han, Pei and Kamber)[19]. Data mining has the ability to reveal
covered up relations and to uncover ambiguous examples and patterns by burrowing into a lot of
information [20](Sumathi and Sivanandam 2006)(Sumathi and Sivanandam)[20].The practices
of data mining in China university libraries are centrally emphasized on institutional rules,
customized policies, and huge amount of data. Among them, basic services and big data related
fields will keep on being hot research areas. Currently Chinese have not conducted studies on
exploration of data mining in university libraries linked with big data and cloud storage
[21](Huancheng, Tingting et al. 2019)(Huancheng, Tingting and Rocha)[21].
The tools of data mining in libraries is a developing trend which has catch the consideration of
data users and scholars in request to behavior of library clients and staff, and usage of data assets
in library. Objectives of the study has been to encourage and facilitate the patrons or data users
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with the awareness of data mining in libraries [22](Siguenza-Guzman, Saquicela et al.
2015)(Siguenza-Guzman et al.)[22].
5.4 Data Science and Libraries
The term data science was coined by statisticians academically to point out the subject as
big data, data analysis, and broader trends, which highlights statistical foundations and the new
mathematical techniques to make sure by the richness of data [23](Gilmore 2016)(Gilmore)[23].
The emerging discipline of data known as data science which examines and interprets huge
volumes of multifaceted and formless data. Generally data science using both data and statistics.
This varies deeply on statistical power and computational science. In both computer science and
statistical research theories are demonstrated and verified with the support of data, hypothesizing
that have given stochastic data model. Further it generates the data. This discipline, by
dissimilarity, also offers emerging technologies and also resolve an alternate problem
[24](Prüfer and Prüfer 2018)(Prüfer and Prüfer)[24]. To get familiar with the emerging phase, in
any case, digital data custodians and librarians are including their conventional abilities to data
management activities [25](Troisi, D’Arco et al. 2018)(Troisi et al.)[25].
5.5 Data Savvy future of data science in libraries
There are six roles of data science managers they are: data archivist, data curator, data librarian,
data analyst, data engineer, and data journalist. These mentioned six roles are enclosed as data
science roles, some of the other roles whose derives from the commercial sector. Which define
only analysts of data type job oriented as data scientists? Though, they are a comprehensive
range of roles data-savvy those roles inside and around the world of data scientists [26](Antell,
Foote et al. 2014)(Antell et al.)[26]. Uses of data science have covered approximately the
majority of areas academic institutions and data-savvy methods are covered mostly the nature of
occupations to enhance skills and gain insights. It states that researchers and library patrons
within institutions obtain pieces of training as well as to seek these competencies and usage in
academic courses, scholarly-projects, or individuals investigations. Routine base work with the
emerging desires of the patrons, data science works as a key element in information centres, so
building information providers with data-savvy techniques enhance their skills in keeping up this
data-centred studies[4](Burton, Lyon et al. 2018)(Burton et al.)[4]. Librarians should be also be
skillful in handling and assessing technologies for future data-savvy era. Usually providing
services through technology, literature suggests that library information science may require
specific care in outgoing and gaining undesirable approaches towards technology [27](Lyon and
Mattern 2017)(Lyon and Mattern)[27].
Data savvy library institution can be categorized by [4](Burton, Lyon et al. 2018)(Burton
et al.)[4] in four categories which are described in figure:2
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Figure:2- Role of data savvy libraries
5.6 Big Data in Libraries
Usage of Data-science methods permits us to control the explaining the growth of data
not ever seen earlier, known as big data. Which is in touch with these features: the bulk (size)
of data which is generated in each second; the velocity (speediness) through fresh data are
produced, acquired, and assessed; the diversity (kinds) by which data being used; the veracity of
data, so their value and reliability; and their complication. These are the factors of big data to
need superior types of storage-systems, accessing, and processability. Analyzing the bulk of data
are required multiple systems and calculation-units, which is known as high-performance
computing, like Hadoop clusters and Spark-Streaming or equivalent virtual networks. Current
tactics with highly performing computing to big data systems seem as continuously need for the
Brainy grouping of complex-data tools with new data mining techniques to analyses of the huge
amount of data [4](Burton, Lyon et al. 2018)(Burton et al.)[4].
Big data has also created new challenges for information professionals, which change
their nature of duty quickly. There is a need to use big data to meet the challenge of a rapidly
growing flood of information and provide the services through online sources. The current digital
scenario also changes the nature of the job of information professional from managing and
disseminate the traditional resources to provide access to digital resources on their desktop in this
online world. Information professional should provide creative and innovative services to meet
the challenges of digital civilizations in instructional ways [28](Pun, Pradhananga et al.
2015)(Pun et al.)[28].
1-Routine assortment and
vital use of quantitative
proof
2-Powerful correspondence
and information driven by
data centric studies
3-Built up proficient
training projects to spread
and grow data savvy skills
4-Understanding of the Worth
and advantages of science and
stewardship professions and
responsibilities
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5.7Data Curation in Libraries
Managers of the academic libraries who provides data services faced most of the difficult
situation related to the digital data sets management that what type of users need to done locally
and what type need to done nationally and internationally. The nonappearance of data curation
does not mean that there is no provision. Mostly areas and subjects are fulfilled by efficient data
sets management services provided by data managers in local, national and international data
centres, which reflect modifications in the research culture by depositing and re-using of datasets
[29](Haynes, Mazumdar et al. 2019)(Haynes et al.)[29].
While the current library services can establish an important involvement in conducting research
information curation on the organizational guide, even libraries with well-created information
retrievals are probably going to find that they need extra abilities to give huge information
curation capacity locally. Starting to different areas of data to help the data curation, access, and
the copyright is the needs of the day by the research scholars [30](Lafferty-Hess, Rudder et al.
2018)(Lafferty-Hess et al.)[30].
There is a job scenario for library chiefs in recognizing the aptitudes hole and working in
institutions with library and data science schools to grow advancement assets to fill it. Only one
out of every academic library will need or need to be dynamic around data. Yet, there is a sense
among numerous college library executives that expert practice has changed quicker of the LIS
schools providing a new concept to the digital workforce [31](Creamer, Morales et al. 2012)(A.
Creamer et al.)[31].
6. Conclusion
Digital data management practices and data oriented services are in its developing stage
in academic libraries. Parents organizations mandates are obviously in the emerging phase,
and little bit academic libraries have built up solid ways to deal with help researchers by
creating data management strategies and figuring the data services in libraries for patrons
benefits. The above-mentioned studies reveal that data management, data curation, data mining,
data science, data savvy and big data projects typically have an objective to identify
relationships and causal associations, classify and predict events, identify patterns and
anomalies, and infer probabilities, interest, and sentiment. Data savvy librarians are familiar
with the data, learn practical procedures and methods, and also learn multi-disciplinary
languages which are permitting them to provide more closely and accurate services to the
researchers or the society. As well as a data management is a critical element of all of these
roles. The majority of studies mentioned that data-science managers and data- curators are
aware of the institutional orders, and the majority of the data managers are actively making to
perform data management activities by learning and teaching inventive procedures in libraries.
It is the need of the current era to educate librarians, library science researchers, and
students regarding understanding, utility, and management of data. Science is not only the owner
and creator but also the managing authority of the most significant share of universal data.
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There is a need for the current scenario that information professionals inquire about
what data science roles can fill out through existing assets by librarians. Currently the libraries
are in the position of to contribute to providing the necessary infrastructure needed for support
of data centric services, particularly admittance to data, utilization, re-use, storage, and
preservation of data. It is supposed to be engaging in the data centric era to work in a library as
its role is expanding with specific new challenges.
Recommendations
Librarians should be skilful in handling and assessing technologies for the future data-
centric age.
Information professional should provide creative and innovative services to meet the
challenges of digital data era in instructional ways.
Information professional should curate, manage, preserve the data for reuse and proide
open access to institutional data repositories.
Innovative information management curriculum should be made more data-centric
concerning the current needy scenario of research and social needs.
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... Therefore, success will be achieved if various stakeholders perform their roles per policies and strategies with various influencing factors in perspective. This study is in line with Shah et al. (2020), which revealed that ''data management, data curation, data mining, data science, data-savvy, and big data purpose is to identify relationships and causal associations, classify and predict events, identify patterns and anomalies, and infer probabilities, interest, and sentiment.'' ...
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