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Recent developments in big data have heightened the need for data governance in any organization. There has been a growing interest and recognition of the importance of data governance in higher education. While multiple research efforts focus on a literature‐based approach to conceptualize data governance, evidence‐based research on this topic can scarcely be found. Higher education institutes are facing similar challenges in aligning their information technology (IT) efforts with business processes to meet organizational goals. This study aims to address such a research gap and investigates the status quo of data governance practice across tier one universities in the United States. Using Web content analysis, this paper sought to obtain empirical evidence of data governance initiatives, the extent of data governance, and its relationship with IT governance and information governance across a sample of 30 tier one university websites. Results of this study revealed that most of the universities created a new data governance unit with different labels (e.g., data governance, institutional research, or data management/analytics), while some universities extended IT governance or information governance to data governance. These findings shed light on the potential directions of developing data governance initiatives in higher education.
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ASIS&T Annual Meeting 2018 198 Papers
The Current State of Data Governance in Higher Education
Cary K. Jim
University of North Texas, USA. Hsia-Ching Chang
University of North Texas, USA.
Recent developments in big data have heightened the need for data governance in any organization. There has
been a growing interest and recognition of the importance of data governance in higher education. While multiple
research efforts focus on a literature-based approach to conceptualize data governance, evidence-based re-
search on this topic can scarcely be found. Higher education institutes are facing similar challenges in aligning
their information technology (IT) efforts with business processes to meet organizational goals. This study aims
to address such a research gap and investigates the status quo of data governance practice across tier one
universities in the United States. Using Web content analysis, this paper sought to obtain empirical evidence of
data governance initiatives, the extent of data governance, and its relationship with IT governance and infor-
mation governance across a sample of 30 tier one university websites. Results of this study revealed that most
of the universities created a new data governance unit with different labels (e.g., data governance, institutional
research, or data management/analytics), while some universities extended IT governance or information gov-
ernance to data governance. These findings shed light on the potential directions of developing data governance
initiatives in higher education.
Data governance, tier one universities, higher education, web content analysis, decision-making.
The change from paper-based records to digital data opened a lot of opportunities and challenges for many organizations.
During the last fifty years, computer software and hardware development continued to advance. Today, any person who inter-
acts with technology locally or network through “wired” or “wireless”, is generating data that are trackable and could be stored
over time. The early use of digital technology in American higher education has been researched and documented since the
1960s (Picciano, 2012). Schools and universities have a long history of maintaining academic records and other operational
documents. Students, faculty, and staff all contribute to the diverse sources of data in universities regardless of their awareness
of how the data are used and for what purpose. From an organizational standpoint, the data stored in their information systems
are valuable for decision-making, compliance, assessment and performance evaluation.
Using digital technologies for record management may appear to free up physical space, but this requires an on-going invest-
ment to keep up with the advancing hardware and software requirement. “Infusions of technology infrastructure, large-scale
databases, and demands for timely data to support decision making have seeped into all levels of college leadership and oper-
ations” (Picciano, 2012, p. 10). It is not a simple task to maintain information technology (IT), and business processes while
improving teaching and learning efforts for students, staff, and the community (Broad, 2014; Duhaney, 2005; Hora, Bouwma-
Gearhart, & Park, 2017; Picciano, 2012). The blending of academia and business processes in higher education offer opportu-
nities for information professionals to connect research to practice. For many universities, their business and instructional
processes supported by information technology have become indivisible. The digitalized components within the universities
require an inclusive structure to manage data and information for the benefit of the organizations and their stakeholders.
Even though information systems and data techniques continue to advance, the intent of using data to inform decisions is
fundamental. The on-going cycle of data analytics provides information for performance, accountability, and other educational
objectives such as students learning outcomes. Data-driven decision-making, then, became a widely adopted approach to ad-
dress these concerns (Hora et al., 2017; Picciano, 2012). Administrative records, financial records, enrollment data, student
information, and faculty records are examples of internal data maintained and processed in-house, usually in a form of data
warehousing. External data are sourced from third-party applications such as a learning management system, curriculum and
instructional tools, digital library resources, and social media data. These are just some examples of data shared and hosted by
vendors or in-house. The third type of data that falls between internal and external is research data. Research data management
is usually handled based on the institution’s data management policy as well as on the funding agency's expectations. Therefore,
universities do not lack data but rather direction in terms of how to utilize data and information for their benefit and how to
protect data as an asset. Hence, the awareness and understanding of data as their asset is the first step. Often “third parties
outside of the university may be the first to recognize data opportunities. Governance mechanisms to assure protection of
privacy, academic freedom, intellectual property, information security and compliance” in universities is imperative (Borgman,
2018, p. 5). Data governance is needed to guide and facilitate information technology, data processes, and decision-making to
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support an organization in reaching its goals. Among library and information professionals, data management or data curation
are not new concepts (Koltay, 2016). Library and information science professionals can enrich collaboration among organiza-
tions in data and information related activities, such as data governance.
Data governance exists in different industries and the interpretations of data governance differ. According to Nielsen’s (2017)
literature review on data governance practices between 2007 and 2017, 32% of published papers came from computer science
disciplines, another 32% from information systems, and only 5% were from education (i.e. higher-education education institu-
tions and learning). This provided us a sense of how data governance has been associated with certain disciplines such as
computer science and information technology from a technical and system perspective. The inception of data governance typ-
ically involves IT, business processes, privacy and security measures. Information technology (IT) governance is used by or-
ganizations to guide their data related processes and has a longer history in the business domain. Weill and Ross (2014) defined
“IT governance as specifying the decision rights and accountability framework to encourage desirable behavior in using IT”
(p. 2). However, data are being collected constantly behind the scenes and it takes some conscious effort to appraise their value
to an organization. Weber, Otto, and Österle (2009) explained that data governance includes both business processes and IT to
provide organization-wide guidelines to ensure data quality and accountability. Several scholars suggested that when data are
viewed as an asset in an organization, they should be monitored and managed (Brous, Janssen & Vilminko-Heikkinen, 2016;
Khatri & Brown, 2010; Koltay, 2016; Otto, 2011). Culture and values of an organization are also important and will shape the
behavior of individuals and organizations (Putro, Surendro, & Herbert, 2016). Therefore, a systematic approach in the devel-
opment of data governance will allow an organization to utilize data effectively to achieve their goals. The increased concern
over privacy and security due to data breaches is another facet of data governance. "Three major universities and one school
district became victims of cyber is not just identifiable information. It is also information about the students and
their performance itself...the mental processes of students as they are working through equations" (How Data Mining, 2014, p.
2). The joint U.S. congressional hearing examined privacy concerns in education, especially, "the sharing of student information
with educational software and cloud service vendors and the laws and guidelines that govern them" (How Data Mining, 2014,
p. 3). Thus, the need to examine data sharing and how third parties are using identifiable data should be addressed by data
governance in educational institutions. Information governance is another concept presented in relation to data governance in
which both are important for organizations to achieve their desired outcomes (Bennett, 2017; Hulme, 2012). Li, Zhou, and Yu
(2016) suggested seven core-criteria (based on information governance guidelines) for big data: organization, metadata, pri-
vacy, data quality, business process integration, master data integration, and information lifecycle requirements. Khatri and
Brown (2010) suggested a data governance framework with five decision domains: data principles, data quality, metadata, data
access, and data lifecycle. The Data Governance Institute (2017a) provided a data governance framework with three main
branches: Rules and rules of engagement, people and organizational bodies, and processes. Each main branch from the frame-
work contains components that guide an organization to develop data governance that will fit their needs. Brous et al. (2016)
provided a systematic review of data governance principles and identified 27 related key concepts including: accountability,
decision rights, compliance, privacy, security, and metadata management. The 27 related key concepts are arranged under four
major principles: organization, alignment, compliance, and common understanding. In summary, data governance serves as the
guiding framework that should address organizational goals and business processes (decision-making, culture, and values),
legal obligations and compliance (accountability), risk management (privacy and security), data management (data quality and
metadata) and the roles humans play (data stewards and data owners). Data governance should continue to mature and adapt
for the organization’s changing needs and processes.
We believe that good data governance can only be meaningful when it aligns with the institutional goals and values in a sus-
tainable manner. Based on the limited amount of research on data governance in education (Nielsen, 2017), there is a need to
explore this topic for a better understanding. This paper is the first attempt to address the lack of research of data governance
among higher education institutions by exploring the current practice of data governance among the tier one research universi-
ties in the United States. This study focuses on the following research questions:
1. What is the current status quo of data governance among tier one research universities in the United States?
2. What type of information is available from the universities that present their data governance initiatives in their web-
A Web content analysis is used to collect and analyze available information publicly released by the universities on their
websites and webpages. The identification of tier one research universities in the U.S. is based on the rankings from the Car-
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negie Classification of Institutions of Higher Education (Indiana Univ. Center for Postsecondary Research, 2017). It is a rec-
ognized ranking system to classify higher education institutes in the U.S. The Carnegie Classification evaluates for-profit and
non-profit higher education institutes and assigns ranking based on the type of degree granting program and the level of research
activity at each. The R1 ranking is commonly referred to as tier one or top-tier which is the highest ranking in the Carnegie
Classification. Tier one indicates a doctoral degree granting institute with the highest research activities. In 2017, there were
115 research universities in the U.S. listed in the R1 (tier one) criteria.
Thirty tier one research universities were selected for the initial sampling of this study based on their geographic location and
business structure: for-profit vs. non-profit. We anticipated to identify data governance practices among the leading universities
(tier one) in U.S. to inform future studies of data governance model and framework for higher education and related environ-
ments. Each sampled university website was searched to identify information regarding their data governance practices. Using
existing documents and web content as a source of data are often referred as nonreactive measures and this type of data can be
a better representation of the phenomenon comparing to data collected through self-reporting mechanism (Wildemuth, 2017,
p. 165). The presence of online information released by each university was used as an indicator of their progress or awareness
of data governance practices. The first part of the evaluation process is to review whether an existing data governance program
is established, how the program is represented within the organization and what approach is taken to govern data as an asset.
Furthermore, the primary goal of this paper is to examine the current data governance practices among the sampled universities
instead of comparing the level of completion of their data governance efforts. In order to collect relevant data that are mean-
ingful for analysis, a Data Governance Checklist was developed. It is derived from key literature on data governance with seven
major criteria identified that would fit for the context of higher education (Brous, et al., 2016; Khatri & Brown, 2010; Koltay,
2016; Li, et al., 2016; Otto, 2011). A coding schema was developed to examine and collect evidence of the status quo of their
data governance practices.
The seven criteria and definition of the Data Governance Checklist is presented in Table 1. Each criterion is designed to mini-
mize overlapping ideas and to allow analysis of existing online documents and information from the sampled universities.
Data Governance Checklist Criteria
Data Governance Body
A group of stakeholders who formalize the data governance practice at
their organization
Data Quality
Guidance on the accuracy, availability, integrity, data standards, and the
intended use
Data Access or Restriction
Specific access or restriction policy on the data
Data Security
Guidance on system security to protect data and privacy issues of sensi-
tive data
Data Stewardship, Ownership, and Roles
Roles and responsibility of those who interact with the data at various lev-
els within an organization
Metadata Documentation and Organization
Structure and methodology to document and maintain data: e.g. data dic-
tionary, metadata scheme
Business Process Integration
Core business process that is parallel to the data process, and manage-
ment of both
Table 1. Data Governance Component Checklist
The Data Governance Body is a group of stakeholders who are interested in the development and oversight of the data govern-
ance program. Membership in this group can be formed internally but can also include other experts outside of an organization.
When reviewing the institutional webpages, we looked for an official group or unit that hosts the data governance program
information as an indication of their web presence. The data governance body is different from the other roles supporting the
process. Other frequently discussed roles in data governance are data stewards, data owners, and data committees (Koltay,
2016; Otto, 2011; Rosenbaum, 2010). Data Stewardship, Ownership, and Roles indicated positions of those who have a vested
interest and actively engage in the process of data governance within their institution. We explored to determine if the univer-
sities presented these roles in their webpages and how these roles are explained in their web content.
Data Quality is the foundation of the data-driven decision-making process. If the data are not genuine and trustworthy, the
output will be misleading and ineffective. If the data collected are not relevant to the organizational goals, they will not be
informative to decision making process. For this criterion, we looked for any description or statement in relation to data quality
from the webpages.
Data Access and Restriction are important to regulate access rights and put limitations on the use of data within an organization.
For example, staff who handle the institution’s data should be given certain access or limitations to avoid misuse of the data. It
also implies proper procedures to grant access for users who should have access to the data. This component is determined by
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identifying specific policies and procedures on the webpages addressing user access or restrictions. Sometimes, Data Security
can be confused with access and restriction. Access and restriction are established for rights and limitations as for how users
are retrieving and interacting with the data. Security is about safeguarding the data and valuable information of the organization.
We use the term security to cover related concepts, such as privacy, confidentiality and data breach protection. Each institution
may have its own definition, covering similar or different elements in its webpages.
Metadata Documentation and Organization are intended to provide structure and information about the data stored in the vari-
ous systems within an organization. It is a common understanding among information professionals that metadata is meant to
support organization and provide a description of the data for users. Some common forms of metadata documentation are data
dictionary, metadata standards, thesauri, etc. The form of metadata implementation was assessed by reviewing the universities
data management mechanism from their web content.
The last criterion is the indication of business process alignment in the web content of each university. Business Process Inte-
gration is the backbone of any data governance process in an organization. Several scholars discussed the significance of busi-
ness process integration with data governance to support the organization’s overarching goals and mission (Brous, et al., 2016;
Khatri & Brown, 2010; Otto, 2011). When reviewing the institutional data governance webpages, we looked for identifiable
information, or business strategies, or processes integration in their data governance practices. Other information collected
depends on its availability, such as department or unit that oversees the data governance practices, data governance documents,
or policy in their webpages.
Every effort has been made to discover publicly available information from the universities’ websites. If the data governance
information is hosted in a restricted web domain, that information will not be accessible to us and therefore won’t be included
in this initial data collection.
The preliminary result includes information collected from web content including online documents and hyperlinks from the
30 tier one universities data governance webpages. There are a variety of terms and concepts used by each university to describe
their efforts in data governance. The use of the Data Governance Checklist helped target searches and organize identified
information from existing key components of data governance in their organization. First, we used the Google search engine
to locate the institution website by entering the full university name and the keyword “data governance”. Then, we compared
the Google search result with an internal search of the university website with the keyword “data governance”. During data
collection, we recognized the different organizational structure and naming mechanism they used among information technol-
ogy, data related services and information related services.
Table 2 presents the offices or units which oversee data governance initiatives or data related services at their institution. The
result indicated most of the data governance activities are supported by a designated group of members in the form of a com-
mittee, council, or working group in the universities. The second and third largest groups of staff who support and supervise
data governance processes are the Information Technology departments as well as Institutional Research. Some institutions
have their own single unit of data management and analytics that were not associated or under another department within the
organization. An interesting fact during this initial data collection is two of the universities have two or more joint office/units
that support both data governance and IT governance, shown in the last row of Table 2.
Office or Unit Represented
Data Governance Council, Working Group or Committee
Information Technology
Institutional Research
Data Management or Analytics Unit
Other units such as security or administrative
No indication of data governance practice
Two or more joint office/units governing data practices
Table 2. Offices or unit within the university where information of data governance is hosted
The Data Governance Checklist is used to distinguish each specific component within the universities’ data governance prac-
tice. The brief definition of each criterion can be found in Table 1. Each count is discrete in the following data analysis. One
or zero was assigned to the checklist to present an indication of yes or no based on the content of their webpages (Table 3).
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Data Governance Checklist Criteria
Data Governance Body
Data Quality
Data Access or Restriction
Data Security
Data Stewardship, Ownership, and Roles
Metadata Documentation and Organization
Business Process Integration
Table 3. Findings of each data governance criterion by count and percentage
The initial analysis of the webpages with the Data Governance Checklist presented the majority (93%) of universities have
policies and procedures of data access or restriction at their institution. About three quarters (77%) of the universities showed
a data governance body in the form of a committee, working group, or council that directs and supports data governance
practices at their institution. Furthermore, more than half of the universities appeared to exercise the other key principles of
data governance at their institution in data quality, data stewardship/ownership, and metadata documentation and organization.
While examining the type of data governance initiative
or program at each institution, we recognized that terms
such as information, data, or IT governance are used
similarly and differently to represent their practice. For
example: IT governance, information governance, or
data management are used together to describe their cur-
rent practice of data governance. Figure 1 displays the
count of the term data governance, IT governance, in-
formation governance, business governance, security
and other types of governance found on the webpages.
Note, some universities displayed more than one type of
governance, for example, data and IT governance is the
most common combination among the 30 universities.
A further investigation is needed discover why there is
such a disparity in their data governance practices.
Part of the Web content analysis is to examine infor-
mation provided by the universities’ webpages about
their view on data governance. We were able to locate
14 university webpages that provide their direct statement on data governance. As stated before, it was impossible to mutually
exclude data governance from IT governance because the perception of each depends upon the university. Therefore, we in-
cluded only statements that address data governance directly in Table 4. The direct statements retrieved from the university
websites represented the variety of perspectives and approaches. Some universities consider data governance as a process or
underlying principles, while others construe it as a part of best practices to support business operation and system integration.
The majority of the statements also cover overlapping key concepts presented in our Data Governance Checklist. Therefore,
we organized the statements according to basic sentence structure in which “data governance” is the subject and the predicate
is what and how the institution describes data governance.
Governance Type
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Examples of Statement by Universities
Data Governance
The [Data Stewardship] Council addresses issues concerning data management including usage,
sharing, integration, access, security, privacy, quality, and compliance” (The University of Chicago,
2018, para 3).
Data Quality
“Data governance adds value to our administrative and academic data systems by the establish-
ment of standards that that promote data integrity and enables strategic integrations of information
systems” (Vanderbilt University, 2018, para 1).
Data Access or Re-
“Data Governance is the process of establishing and managing information about the data collected
and used by the University…This ensures a common and consistent understanding of what data
exists, term definitions, availability, sensitivity classification and access restrictions” (University of
Norte Dame, 2018, para 1)
Data Security
“Data governance includes all the policies and practices that ensure UDW+ [online information sys-
tem and dashboard] provides the university community with accurate and consistent information
while protecting data security and confidentiality” (New York University, n.d., para 1).
Data Steward-
and Roles
“[Data Governance Stewardship] determine formal roles for those in charge of data. This does not
mean that everyone on campus is not responsible despite formal roles.” (University of Wisconsin at
Madison, 2018).
Metadata Docu-
mentation and Or-
This program [Data Governance program] will result in a sustainable and efficient set of controls,
data standards and data policies for UTA, with the potential to extend its scope over time to other
areas with UTA” (The University of Texas at Arlington, 2018).
Table 4: Direct statements that support each component of the Data Governance Checklist
According to a survey by Childers (2017) for the Higher Educa-
tion Data Warehousing Forum, data governance is the most fre-
quent topic among 57% of their surveyed universities for the sec-
ond year (2016 and 2017). The latest result of the Top 10 survey
by Childers and Walz (2018) again showed data governance as a
priority topic and became a priority category in 2018. It is worth
mentioning that several components in the data governance from
their 2018 Top 10 survey also reflected similar observations as
our study, such as administration of data governance, metadata
and data definitions, data quality and role-based access.
Through the examination of data governance structure and pro-
cesses, the sampled universities seem to have different overarch-
ing governance structures. For example, some universities cre-
ated new units to lead the data governance initiatives; other uni-
versities began with their IT department and extended their work
to address data governance. There are also a minority of univer-
sities working toward an integration of information and data gov-
ernance in which IT plays a supporting role. Figure 2 presents a
Venn diagram which depicts the relationships between data gov-
ernance and two other types of governance, IT governance and
information governance. The intersecting areas denote the interplay between different governance structures that work together
with data governance initiatives, while non-intersecting areas illustrate the standalone governance structure in the sampled
universities. Note, the number in parentheses reflects the count displayed in Figure 1.
Interestingly, two universities strategically extended their information governance to integrate data governance and/or IT gov-
ernance. This approach can support interconnections as information governance refers to a broader concept that entails data
governance and IT governance (Bennett, 2017; Smallwood, 2014). In addition, information governance is often considered
within the scope of IT to address the importance of information within a modern organization (Faria & Simpson, 2013). As a
result, many universities thus far do not seem to have embraced information governance as a relevant part of their framework
to inform the creation of their data governance. As for the representation purpose, interplay between data governance and IT
governance, ten universities (about one-third) have data governance embedded within the IT governance structure, and only
one university demonstrated its inclusion of information governance, IT governance and data governance on the webpages.
Figure 2. The interplay between data governance, IT gov-
ernance, and information governance found on the univer-
sities webpages.
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Based on the evidence collected from the 30 tier one research universities, it has shown that most institutions have awareness
of data governance and are in the process of forming policies and procedures for their institution. The mixture of information
available from their webpages provides information of their current state of data governance. Yet, due to the design of this
exploratory study and the collection of web content from the sampled universities, our study has limitations. First, the initial
collection of web content for this study can only serve as a starting point to inform future research in this topic. The varied
organizational structures and concept terms found in universities’ data governance policies require further inquiry with each
institution. The use of surveys or interviews with university officials within their IT, data or information units can provide
valuable insight into their strategy. Institutional culture and values also have an impact on their views and approach to data
related mechanism and processes. We recognized that organizational culture and values are important to the development and
success of data governance policies and programs in higher education. However, culture and values are not simple variables
that we can examine through the web content analysis in this study and should be further researched in light of their connection
to data governance at the universities. Further comprehensive research is needed to establish a uniform model or framework
for data governance in higher education.
As information professionals, we believe our profession can contribute to an organization’s data governance initiative due to
our expertise in information related work and services for the various settings. There is no one-size-fits-all solution to data
governance. Our study does not propose a single solution approach to data governance, rather, we surveyed the data governance
initiatives and practices based on the university's web presences to assess its status quo. We observed consistent themes across
the samples in which a governing body with defined roles and access are important constituents within an organization. We
also observed the use of software solutions to address their data governance need in many of the universities. To support data-
driven decision making, most universities strive for business intelligence (BI) advancement utilizing a variety of analytics tools
along with information technology (IT). BI and data analytics are all about using data for decision making and support. In order
to acquire quality data for effective data analytics, a data governance or data management framework has to be in place to guide
the process. It is evident that BI/analytics efforts co-evolve with data governance. More than 50% of sampled universities focus
on BI betterment by indicating the advanced analytics tools in their webpages, such as SAS, Qlikview, Tableau, Cognos, SAP
HANA, and OLAP cubes. One university specifically adopted a data governance platform, Collibra, to break down the data
silos. This implies that using technology tools or software solutions could aid in navigating the complex data governance
process. It is essential to further research on the interrelationship between an organizational structure of data governance and
the digitized processes of data governance. Can the adopted solutions provide the one-stop-shop for a unique institution such
as higher education? What are the elements in data governance that cannot be addressed by technical solutions but through the
integration of human resources or supports?
The next step for our study is to complete the data collection of all tier one universities and collect other evidence to verify our
observation from the web content such as interviews or surveys. Another aspect to explore is to identify models or frameworks
used by the universities and if these draw from theoretical or conceptual work of data governance from other disciplines. Which
type of governance should serve as the foundation of data governance for higher education and how it should it vary based on
the institutional view on data and information? As Koltay (2016) suggested, “librarianship as well as library and information
science also should pay attention to DG [data governance], albeit it attracted attention mainly in the business sector” (p. 304).
It is our attempt to examine data governance in higher education from an information science perspective in hopes of under-
standing the significance of the interplay of data governance, IT governance, and information governance. The current land-
scapes of data governance among tier one universities are diverse and how they evolve will reflect the institutions’ business
approach and management as well as their academic vision and mission.
Data and information are both valuable assets for higher education. The need for information and data to support the dual roles
as an academic institution and a business organization needs a systematic way to manage data and information. The goal of
data governance in higher education should be strategic to address the needs and interests of stakeholders from students, staff,
faculty, administrators to the larger research community. This paper conducts an evidenced-based study using Web content
analysis that substantiates the current state of data governance among higher education. This exploratory approach provides
information on one aspect of this phenomenon. Further data collection such as interviews or surveys can provide more in-depth
insights in regards of data governance in higher education institutes and to address any information not released in their public
domain webpages.
The findings highlight the different approaches undertaken by higher education on data governance. While information gov-
ernance oversees people activities, technology, and processes (Information Governance Initiative, 2018), data governance re-
volves around managing information-related processes (The Data Governance Institute, 2017b). This perspective leads us to
ongoing discussions regarding rethinking the role of information governance in data governance initiatives. Although the re-
sults from this study are not conclusive due to limited sample size, they are certainly worth investigating in depth with wider
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populations in tier one universities. It is our plan to further explore the rest of the tier one universities (85 out of 115) to get a
better view of data governance among all the tier one universities in the U.S.
For publicly funded education entities, such as public universities, there are distinct features in comparison to a for-profit
organization. Accountability, compliance, and transparency are the basis of a public domain that demonstrates equality, acces-
sibility, and stewardship of public resources. Therefore, with the vast amount of data within an education institution, data
governance should be in place to protect data assets and to guide the data processes to meet organizational goals. This paper is
the first study to collect evidence of data governance across a sample of tier one research universities, thus contributing to the
understanding of current state of data governance among leading research universities. Several organizations and universities
have taken the lead in forming data governance to guide their decision making and control of data (Borgman & Wada, 2016;
Chapple, 2013; Data Quality Campaign, 2018; Reeves & Bowen, 2012), but more work is needed to examine the maturity of
data governance implementation. Other types of growing data such as social media and communication channels should also
be considered in the data governance framework. If a higher education institute is to invest in software solutions and information
architecture to support its business processes and services, a comprehensive data governance framework will help inform de-
cisions in technology investment. The direction taken by the organization on data governance and how it evolves to keep up
with the growing big data trend will be one of the determining factors of success in the decades to come.
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81st Annual Meeting of the Association for Information Science & Technology | Vancouver, Canada | 10 14 November 2018
Author(s) Retain Copyright
... Taking into account a significant increase in the amounts of data collected, stored, and used in HEIs, the concept of Data Governance has been introduced into higher education [28,29] and was discussed in relation to LA [30]. As was mentioned in a comprehensive review [31], it aims at implementing a corporate-wide data agenda, maximizing the value of data assets in an organization and managing data-related risks. ...
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Citation: Kustitskaya, T.A.; Esin, R.V.; Kytmanov, A.A.; Zykova, T.V. Designing an Education Database in a Higher Education Institution for the Data-Driven Management of the Educational Process. Educ. Sci. 2023, 13, 947. https://doi. Abstract: During the past two decades, higher education institutions have been experiencing challenges in transforming the traditional way of in-class teaching into blended learning formats with the support of e-learning technologies that make possible the collection and storing of considerable amounts of data on students. These data have considerable potential to bring digital technologies in education to a new level of personalized learning and data-driven management of the educational process. However, the way data are collected and stored in a typical university makes it difficult to achieve the mentioned goals, with limited examples of data being used for the purposes of learning analytics. In this work, based on the analysis of existing information systems and databases at Siberian Federal University, we propose principles of design for a university database architecture that allow for the development and implementation of a data-driven management approach. We consider various levels of detail of education data, describe the database organization and structure , and provide examples of learning analytics tools that can benefit from the proposed approach. Furthermore, we discuss various aspects of its implementation and associated questions.
... And at a personal level, students have no agency over the practices of data extraction and use. In other words, there is no coherent and meaningful oversight, control, and data governance mechanism that ensures accountable, transparent, and ethical collection and processing of education data (Jim and Chang 2019). While general data protection laws provide a baseline for making data privacy impact assessments, these are seen as insufficient (Mantelero 2022) and within the education context -chaotic and limited (Hillman 2022b). ...
The pandemic affected more than 1.5 billion students and youth, and the most vulnerable learners were hit hardest, making digital inequality in educational settings impossible to overlook. Given this reality, we, all educators, came together to find ways to understand and address some of these inequalities. As a product of this collaboration, we propose a methodological toolkit: a theoretical kaleidoscope to examine and critique the constitutive elements and dimensions of digital inequalities. We argue that such a tool is helpful when a critical attitude to examine ‘the ideology of digitalism’, its concomitant inequalities, and the huge losses it entails for human flourishing seems urgent. In the paper, we describe different theoretical approaches that can be used for the kaleidoscope. We give relevant examples of each theory. We argue that the postdigital does not mean that the digital is over, rather that it has mutated into new power structures that are less evident but no less insidious as they continue to govern socio-technical infrastructures, geopolitics, and markets. In this sense, it is vital to find tools that allow us to shed light on such invisible and pervasive power structures and the consequences in the daily lives of so many.KeywordsTheoretical kaleidoscopeToolkitMethodologyDigital inequalitiesPostdigitalCollaborative writing
... To the best of our knowledge, research on data security and compliance in connection with collaboration for innovation may be scarce as it was impossible to find any relevant literature related to India. The explanation might be associated with the growing interest in data regulation in academia aligned with the rise of big data (Jim and Chang, 2018;Zhang, 2018). For example, Hina and Domenic's 10.1163/21971927-bja10039 | triple helix (2023) 1-66 (2016) study on compliance practices among Malaysian academia found that despite their dependence on technological solutions and security policies to protect the information, information security compliance practices were scarce, resulting in non-compliant behaviours and security breaches with loss of sensitive and valued information. ...
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The increased complexity in the current business environment connected with the globalisation of economies and rapid technological developments makes firms depend on innovation and, in the process, develop dense networks of relationships, making collaboration an essential requisite for innovation. Thus, collaboration develops based on complex social networks from which innovation emerges. From this perspective, collaboration takes a systematic approach, where social relationships are crucial. This article describes the innovation behaviour of firms operating in India and introduces collaboration as a system drawing from the systems theory and triple helix innovation model. The results of the mixed methods study conducted pointed toward a fragile collaboration framework. Triangulation was employed to provide a deeper understanding. Furthermore, limited understanding of collaboration as a social system has constrained social interactions, leading to limited knowledge production, application, and knowledge sharing, with technological development and innovation delays. The article lists crucial factors from the perspectives of industry and academia to foster a collaboration framework.
... The Data governance dimension maps the level of adherence to data governance practices and methodologies to ensure the quality and accuracy of the data in the BIA solution. According to [49], there has been a growing interest and recognition of the importance of data governance in HE. Data governance refers to the exercise of authority and control over the management of data [50]. ...
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Business Intelligence and Analytics (BIA) systems play an essential role in organizations, providing actionable insights that enable business users to make more informed, data-driven decisions. However, many Higher Education (HE) institutions do not have accessible and usable models to guide them through the incremental development of BIA solutions to realize the full potential value of BIA. The situation is becoming ever more acute as HE operates today in a complex and dynamic environment brought forward by globalization and the rapid development of information technologies. This paper proposes a domain-specific BIA maturity model (MM) for HE–the HE-BIA Maturity Model. Following a design science approach, this paper details the design, development, and evaluation of two artifacts: the MM and the maturity assessment method. The evaluation phase comprised three case studies with universities from different countries and two workshops with practitioners from more than ten countries. HE institutions reported that the assessment with the HE-BIA model was (i) useful and adequate for their needs; (ii) and contributed to a better understanding of the current status of their BIA landscape, making it explicit that a BIA program is a technology endeavor as well as an organizational development.
... And at a personal level, students have no agency over the practices of data extraction and use. In other words, there is no coherent and meaningful oversight, control and data governance framework that ensures accountable, transparent and ethical collection and processing of education data (Day 2021;Jim and Chang 2019). ...
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The need for a comprehensive education data governance – the regulation of who collects what data, how it is used and why – continues to grow. Technologically, data can be collected by third parties, rendering schools unable to control their use. Legal frameworks partially achieve data governance as businesses continue to exploit existing loopholes. Education data use practices undergo no prior ethical reviews. And at a personal level, students have no agency over these practices. In other words, there is no coherent and meaningful oversight and data governance framework that ensures accountable data use in an increasingly digitalised education sector. In this article, I contextualise the issues arising from education data transactions in one school district in the United States. The case study helps to contextualise what needs governance, who may access education data and how the district governs data use and transactions, emphasising the need for a coherent education data governance but also the limitations of such isolated efforts.
... The status of data governance implementation across tier-one Universities in the United States was investigated by Cary and Hsa in 2018 [12]. They examine the status of data governance and its relationship with IT governance across 30 tier-one university websites. ...
Nowadays, data has become important and influences the decision-making process on government and business sectors. Data governance strategy should not be underestimated because it increases the value of data and minimize data-related cost and risk. The data governance concept promotes the accomplishment of organizational objectives by developing and implementing an appropriate strategy for processing data in perfect and secure manner. This study aims to assess the maturity of data governance for Saudi sectors by design a framework and using it to measure whether the data governance have been applied or not. To do so, we have designed a questionnaire based on five criteria for assessing the current state of data governance implementation which are: policies and standards of data management, data quality, risk of poor data quality, cost of data correction, and data security. The questionnaire was then distributed to the employees in the IT department or who are related to data management or data security in Saudi sectors either government or private. The results show that approximately 48% of the respondents stated that they have a data governance committee in the sectors in which they work. Also, 55% of the respondents indicated that there are legislation and regulations for data governance in the sectors, as well as for making data available. Moreover, 42% from the respondents stated that their organizations have policies and procedures to enforce data management
As education grows its dependency on digital technologies, their business owners have the capacity to claim control over not only the distribution of knowledge but also over individuals with regards to their future prospects and life chances. Algorithmic systems are increasingly used to infer, predict, and steer the education experience of children. Such systems are data-intensive, often proprietary in nature, and near impossible for their end-users to understand how they work or even resist. These systems therefore build their privilege in education to acquire pedagogic power and authority over the educational process and over one’s future life chances. But concerns grow as to whom this authority benefits the most and how to prevent their possible negative impact. For that, this chapter calls for more and systematic postdigital research to identify the needs of governance, scrutiny, and oversight of these advancing data-intensive systems and their owners to ensure that they are held accountable in their influence and impact on education.KeywordsDataficationEdTechChildrenEducationPedagogyPostdigitalResearch
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The purpose of this review paper is to understand why organisations choose to implement data governance (DG) programmes. An understanding of these motivations will facilitate assessing the effectiveness of DG programmes. A search of the literature returned 628 publications for examination; of these 50 were deemed to be relevant to the research, and were selected for analysis and coding using a grounded theory approach. Our analysis found 115 organisational motivations for implementing DG, grouped into 23 categories. We use the Khatri and Brown framework to organise these categories across their five decision domains. The motivations are predominantly associated with operations and technology. This presents a challenge for organisations, where an over-focus on technology could lessen the business imperative. DG needs to be much more than an operational plan for managing the data asset. DG requires a holistic approach to succeed which suggests that all decision domains are considered adequately.
As institutions seek to shift into more advanced analytics and data‐based decision‐support, many institutional research offices face the challenge of meeting the office's current demands while taking on more intricate and specialized work to support decision‐making. Given the great need organizations have for information that supports real‐time strategic decision‐making, institutions must advance beyond traditional static data reporting offices to modern offices with regular predictive analytics use. The authors believe that institutional research offices should actively engage in contemporary analytical approaches and provide leadership in this area. The following chapter focuses on (a) why higher education should embrace analytics, (b) discusses areas where analytical advancements have occurred, (c) discusses areas where analytical growth is lacking, and (d) provides guidance on addressing cultural changes concerning institutional data use, policies, and practices.
Conference Paper
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Data is a vital asset in a business enterprise in achieving organizational goals. Data and information affect the decision-making process on the various activities of an organization. Data problems include validity, quality, duplication, control over data, and the difficulty of data availability. Data Governance is the way the company / institution manages its data assets. Data Governance covers the rules, policies, procedures, roles and responsibilities, and performance indicators that direct the overall management of data assets. Studies on governance data or information aplenty recommend the importance of cultural factors in the governance of research data. Among the organization’s leadership culture has a very close relationship, and there are two concepts turn, namely: Culture created by leaders, leaders created by culture. Based on the above, this study exposure to the theme “Leadership and Culture Of Data Governance For The Achievement Of Higher Education Goals (Case Study: Indonesia University Of Education)”. Culture and Leadership Model Development of on Higher Education in Indonesia would be made by comparing several models of data governance, organizational culture, and organizational leadership on previous studies based on the advantages and disadvantages of each model to the existing organizational business. Results of data governance model development is shown in the organizational culture FPMIPA Indonesia University Of Education today is the cultural market and desired culture is a culture of clan. Organizational leadership today is Individualism Index (IDV) (83.72%), and situational leadership on selling position.
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More and more data is becoming available and is being combined which results in a need for data governance - the exercise of authority, control, and shared decision making over the management of data assets. Data governance provides organizations with the ability to ensure that data and information are managed appropriately, providing the right people with the right information at the right time. Despite its importance for achieving data quality, data governance has received scant attention by the scientific community. Research has focused on data governance structures and there has been only limited attention given to the underlying principles. This paper fills this gap and advances the knowledge base of data governance through a systematic review of literature and derives four principles for data governance that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance strategy and approach.
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Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is already being felt and should not be ignored. While big data and analytics are not panaceas for addressing all of the issues and decisions faced by higher education administrators, they can become part of the solutions integrated into administrative and instructional functions. The purpose of this article is to examine the evolving world of big data and analytics in American higher education. Specifically, it will look at the nature of these concepts, provide basic definitions, consider possible applications, and last but not least, identify concerns about their implementation and growth.
There has been a great deal of confusion around the term information governance (IG), and how it is distinct from other similar industry terms such as information technology (IT) governance and data governance. This chapter discusses the differences and includes examples in hopes of clarifying what the meaning of each is, and how they are related. It delves into more detailed definitions and a comparison of the three. Data governance is a newer, hybrid quality control discipline that includes elements of data quality, data management, IG policy development, business process improvement, and compliance and risk management. Data governance can be seen as part of IT governance, which is also a part of a broader program of information governance. There are several IT governance frameworks that can be used as a guide to implementing an IT governance program. IT governance seeks to align business objectives with IT strategy to deliver business value.
Discussed in this chapter will be information governance (IG) and how an information governance framework (IGF) can be helpful in bridging the gap between business and IT by clarifying the factors that must be considered by organizations in order to successfully implement an IG strategy. The practical relevance of the factors considered on the proposed IGF is illustrated in the context of the banking industry experience. To understand the current situation of IG inside banks and the possibilities of an IGF, 16 executives of 13 banks in Brazil, Hong Kong, and the United States were interviewed.
As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.
Data governance and data literacy are two important building blocks in the knowledge base of information professionals, involved in supporting dataintensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data.
There is a growing realization that information is an organization’s third capital asset and should be given the same level of focus as cash and human resources. Information Governance is the discipline of treating information as a strategic corporate asset. Information Governance is about maximizing the business value of information and reducing the total cost of ownership of the information management landscape, whilst ensuring compliance, managing risk and protecting security. The organizational world is at a tipping point in terms of the wide-spread adoption of Information Governance – at present there are very few organizations that have a comprehensive, coherent and mature Information Governance programme in place – although most Chief Information Officers (CIOs) are beginning to understand how essential this is. This article covers the business and IT issues that Information Governance addresses. It demonstrates the opportunities that exist if we think about information in new and innovative ways – and how these desired outcomes require a focus on good governance. It shows how IBM has been a pioneer in the area of Information Governance for the past seven years and it examines the five critical competencies that top performers exhibit for good governance. The article shows how realistic entry points to get started on Information Governance can be identified.