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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. caryjim@my.unt.edu Hsia-Ching Chang
University of North Texas, USA. Hsia-ching.Chang@unt.edu
ABSTRACT
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.
KEYWORDS
Data governance, tier one universities, higher education, web content analysis, decision-making.
INTRODUCTION
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
ASIS&T Annual Meeting 2018 199 Papers
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.
RELATED WORK
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 breaches...it 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.
PURPOSE OF THE STUDY
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-
sites?
METHODOLOGY
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-
ASIS&T Annual Meeting 2018 200 Papers
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
Definition
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
ASIS&T Annual Meeting 2018 201 Papers
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.
FINDINGS – PRELIMINARY RESULTS
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
Count
Percentage
Data Governance Council, Working Group or Committee
10
33%
Information Technology
7
23%
Institutional Research
6
20%
Data Management or Analytics Unit
4
13%
Other units such as security or administrative
3
10%
No indication of data governance practice
2
6%
Two or more joint office/units governing data practices
2
6%
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).
ASIS&T Annual Meeting 2018 202 Papers
Data Governance Checklist Criteria
Count
Percentage
Data Governance Body
23
77%
Data Quality
16
53%
Data Access or Restriction
28
93%
Data Security
23
77%
Data Stewardship, Ownership, and Roles
22
73%
Metadata Documentation and Organization
19
63%
Business Process Integration
23
77%
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.
0
2
4
6
8
10
12
14
16
18
20
Governance Type
Figure 1. Types of governance found on the universities webpages
ASIS&T Annual Meeting 2018 203 Papers
Criteria
Examples of Statement by Universities
Data Governance
Body
“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-
striction
“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-
ship/Ownership,
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-
ganization
“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
DISCUSSION
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.
ASIS&T Annual Meeting 2018 204 Papers
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.
CONCLUSION
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
ASIS&T Annual Meeting 2018 205 Papers
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
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