Data Governance in Education: Addressing Challenges and Unlocking

Opportunities for Effective Data Management

Thiago Medeiros

, Andr

e Ara

ujo

, Jos

e Silva

and Alenilton Silva

Computing Institute, Federal University of Alagoas, Av. Lourival Melo Mota, S/N - Cidade Universit

aria, Macei

o, Brazil

Keywords:

Data Governance, Data Quality, Data Security, Data Interoperability, Data Integration, Educational

Ecosystems.

Abstract:

This study investigates the pivotal role of data governance in driving digital transformation within the educa-

tion sector. It highlights the importance of improving data quality, security, interoperability, and integration

to establish efﬁcient and transparent educational data ecosystems. The analysis reveals signiﬁcant challenges,

including fragmented adoption of governance practices, the absence of tailored public policies, and a lack of

standardized metrics to measure governance impacts. Additional barriers, such as compatibility issues with

legacy systems and insufﬁcient technical training, hinder the effective implementation of data governance

strategies. This research emphasizes the need for collaborative and interdisciplinary efforts to address these

challenges, advocating for developing practical, scalable, and context-speciﬁc solutions. By tackling these

issues, data governance can be ﬁrmly established as a cornerstone for innovation, improved decision-making,

and enhanced transparency and equity in the education sector, ultimately supporting its digital transformation

and long-term sustainability.

1 INTRODUCTION

The education sector is undergoing profound trans-

formations driven by the increasing digitization of

processes and data (Kshirsagar, 2024). Educational

institutions generate, collect, and store growing vol-

umes of information on students, teachers, curricula,

and public policies, highlighting the importance of ro-

bust management practices and responsible data use

(Curry et al., 2022). In this context, data governance

emerges as a strategic pillar for ensuring data quality,

integration, and security (International, 2017).

Reliable and integrated data are essential for

evidence-based decisions, formulating public poli-

cies, and personalizing pedagogical practices. How-

ever, challenges such as data fragmentation, redun-

dancy, and the absence of clear interoperability stan-

dards persist (Casta

neda and Gourlay, 2023). Without

adequate governance practices, institutions struggle

to maintain accessible, accurate, and ethically used

data (Bazaluk et al., 2024).

https://orcid.org/0009-0006-5145-7854

https://orcid.org/0000-0001-8321-2268

https://orcid.org/0009-0001-0225-2696

https://orcid.org/0009-0008-2989-3996

Implementing data governance strategies offers

signiﬁcant beneﬁts, including operational efﬁciency,

improved teaching quality, and greater transparency

in public management (Hendrawan et al., 2023). Ad-

ditionally, it facilitates interoperability between edu-

cational systems, promoting the exchange of informa-

tion and enabling a holistic view of academic perfor-

mance at local, regional, and national levels.

Despite its importance, data governance frame-

works tailored speciﬁcally for education are limited

(Casta

neda and Gourlay, 2023). Many existing mod-

els were designed for sectors like healthcare or ﬁ-

nance, failing to address the speciﬁcities of academic

institutions. This gap in contextualized tools and

practices hampers the widespread adoption of data

governance strategies in education, limiting their po-

tential impact (Volkov et al., 2023).

This article explores the importance of data gover-

nance in education, focusing on its challenges, oppor-

tunities, and best practices to guide effective imple-

mentation. It aims to advance the discussion and pro-

pose solutions to improve data integration and quality,

enhance educational management, and support digital

transformation. A central question addressed is how

existing data governance frameworks can be adapted

Medeiros, T., Araújo, A., Silva, J. and Silva, A.

Data Governance in Education: Addressing Challenges and Unlocking Opportunities for Effective Data Management.

DOI: 10.5220/0013468300003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 1, pages 367-374

ISBN: 978-989-758-749-8; ISSN: 2184-4992

367

to the education sector to overcome data fragmenta-

tion and system integration issues.

The article is structured as follows: Section 2

presents key concepts and data governance frame-

works. Section 3 offers a literature review on edu-

cational data governance, while Section 4 discusses

the major challenges identiﬁed. Section 5 contextual-

izes these challenges within the Brazilian educational

ecosystem, and Section 6 concludes with recommen-

dations for future research.

2 DATA GOVERNANCE

This section deﬁnes data governance and presents key

frameworks that ensure data quality, security, and eth-

ical use. It highlights ﬁve prominent frameworks of-

fering structured methodologies for managing data

governance applicable across sectors, including edu-

cation.

2.1 Basic Concepts

Data governance is the framework of processes, poli-

cies, standards, and technologies designed to man-

age an organization’s data assets responsibly (Inter-

national, 2017; Henninger, 2022). It spans the en-

tire data lifecycle, ensuring accuracy, security, ac-

cessibility, and regulatory compliance (International,

2017). In education, data governance aids institutions

in managing complex datasets from students, teach-

ers, and policymakers.

Effective data governance is guided by core prin-

ciples: data quality ensures accuracy and reliabil-

ity; data security protects sensitive information; and

data transparency clariﬁes how data is collected, pro-

cessed, and used (International, 2017). Additionally,

data accountability assigns responsibility for integrity

and compliance, while data stewardship manages data

throughout its lifecycle. Data standardization ensures

consistent formats and deﬁnitions, promoting interop-

erability and reducing redundancy. Data privacy safe-

guards personal information, ensuring ethical han-

dling under regulatory frameworks.

Data accessibility allows authorized users to re-

trieve and utilize data, supporting timely decision-

making. Data interoperability facilitates seamless

exchange across systems (Hendrawan et al., 2023),

and data integration uniﬁes information from multiple

sources, breaking silos and improving insights. To-

gether, these principles build trust, foster data-driven

decision-making, and support strategic goals.

2.2 Frameworks

This section presents ﬁve key data governance frame-

works relevant across various industries, including

education. The DAMA-DMBOK framework pro-

vides a comprehensive guide covering governance,

quality, and architecture, aligning data management

with organizational goals and addressing integration

challenges in education (International, 2017).

COBIT, developed by ISACA, integrates data

governance within IT governance, aligning technol-

ogy practices with business objectives. It offers clear

policies, roles, and performance measures, enhancing

decision-making through accountability and trans-

parency (ISACA, 2018). Similarly, the IBM Data

Governance Framework focuses on policies, roles,

and technologies, improving efﬁciency and managing

sensitive data, particularly useful in large educational

environments (Soares, 2010).

The DCAM model assesses and improves data

management capabilities, including a maturity assess-

ment to identify governance gaps. Originally used in

ﬁnance, it is adaptable to education (Chu and Wang,

2023). Lastly, the FAIR Principles focus on data us-

ability and interoperability, promoting standardized

metadata and open data practices, fostering collabora-

tion and innovation in academic contexts (Kush et al.,

2020).

3 LITERATURE REVIEW

This section is structured as follows: it starts with an

overview of the research methodology, followed by

an analysis of the state of the art, which explores re-

cent advancements and trends in the ﬁeld. Lastly, the

challenges identiﬁed in the reviewed studies are ad-

dressed, with a focus on the issues that remain to be

tackled in order to advance data governance, integra-

tion, and interoperability.

3.1 Methodology

This study conducted a structured literature review,

employing rigorous methods to identify, select, and

evaluate studies on data governance, interoperability,

and integration. The search strategy, detailed in Fig-

ure 1, combined relevant terms with Boolean oper-

ators. Searches were performed in Science Direct,

PubMed, Springer, ACM, IEEE, and Scopus, focus-

ing on publications from 2018 to 2024 to capture re-

cent advancements. As a result, 1512 studies were

retrieved based on titles and keywords.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

368

Figure 1: Search Strategy.

In the initial analysis, inclusion and exclusion cri-

teria were applied, selecting only full, open-access

articles directly related to the study topics, while

excluding duplicates, unrelated publications, book

chapters, and other formats. The retrieved studies

were organized using specialized tools, and a ﬁrst ﬁl-

tering, based on abstracts and conclusions, identiﬁed

59 potential articles for detailed review. In the second

ﬁltering, thorough reading reduced the selection to 18

articles, which were analyzed for their contributions

and compatibility with the research theme. These arti-

cles informed the state-of-the-art review, highlighting

intersections, common points, and existing research

gaps, as illustrated in Figure 2.

3.2 State of the Art Analysis

The analysis of selected studies highlights advance-

ments in data governance and digital transformation

in education, emphasizing the role of cross-sector so-

lutions. Key studies, such as (Miao et al., 2019), com-

pare open data initiatives in China and the UK, show-

ing discrepancies in governance practices. The UK’s

success in implementing standardized platforms con-

trasts with China’s data disorganization, underscoring

the need for structured governance.

Studies like (Otoo-Arthur and van Zyl, 2020) and

(Feng et al., 2021) introduce technological solutions

like Big Data, IoT, and cloud computing to enhance

scalability, security, and data integration in educa-

tion. Additionally, blockchain technology, explored

in (Hillman and Ganesh, 2019) and (Rani et al.,

2023), offers solutions for ensuring data integrity, pri-

vacy, and transparency, highlighting its signiﬁcance in

addressing ethical challenges within educational sys-

tems.

The integration of educational data is explored by

(Huang, 2023), which advocates for the iPaaS plat-

form to enhance university efﬁciency and improve

data literacy. Similarly, (Williamson, 2018) discusses

the ”datiﬁcation of higher education,” focusing on

how data infrastructures shape decision-making pro-

cesses in alignment with social and economic goals.

In the broader context of data governance, (Wang and

Huang, 2021) explores data integration to improve ef-

ﬁciency in Chinese universities, while (Mojun and

Tingmin, 2022) suggests governance algorithms to

optimize systems and teaching processes. Addition-

ally, (Astuti et al., 2024) highlights cultural and polit-

ical barriers to data governance in Indonesia, empha-

sizing the importance of aligning practices with local

contexts.

These studies reveal uneven progress in data gov-

ernance and digital transformation in education, with

notable trends in technology integration, scalability,

and interoperability. However, challenges related to

institutional, cultural, and political factors persist. To

overcome these barriers, future research must focus

on tailoring technological solutions to local contexts,

promoting operational efﬁciency, and ensuring equi-

table access. Examining practices from other sectors

with proven security and system integration method-

ologies is critical for improving educational data man-

agement.

In agriculture, (Kawtrakul et al., 2021) empha-

sizes the importance of interoperability and collab-

oration for advancing sustainability, showcasing the

BIO-AGRI-WATCH model for optimizing resource

use and supporting strategic decisions. This model

aligns with other sectors’ efforts to integrate data into

distributed systems, highlighting the universal bene-

ﬁts of interoperable solutions for complex challenges.

In manufacturing, (Dur

ao et al., 2024) and (Stahl

et al., 2023) explore how Digital Twins and data-

driven business models enhance operational efﬁ-

ciency and competitiveness. Their work, which

demonstrates the potential for real-time data integra-

tion to revolutionize industrial processes, offers in-

sights that could also beneﬁt sectors like healthcare

and public management. In health, (Kush et al.,

2020), (Almeida and Oliveira, 2024), and (Li and

Quinn, 2024) highlight the role of interoperability

and security standards such as the FAIR principles

and GDPR, addressing privacy and data portability

challenges. Similarly, in public management, stud-

ies by (Haneem et al., 2019) and (Zanti et al., 2022)

show how Integrated Data Systems (IDS) can mod-

ernize administration and enhance social policies, un-

derscoring the value of collaboration across sectors to

overcome organizational and technical barriers.

These studies highlight that data governance is

essential for efﬁciency, innovation, and sustainabil-

ity across sectors. By aligning practices, standards,

and technologies, organizations can improve interop-

erability and create integrated ecosystems to address

global digital transformation challenges. This align-

ment fosters collaboration and data sharing, driving

progress in various ﬁelds while meeting the demands

of a connected, data-driven world.

After reviewing the state of the art, the eighteen

Data Governance in Education: Addressing Challenges and Unlocking Opportunities for Effective Data Management

369

Figure 2: Methodological Approach.

selected articles were categorized according to their

primary focus, as shown in Table 1. To provide a more

comprehensive evaluation, data integration and inter-

operability were treated as distinct categories, even

though they are subﬁelds of data governance. This

approach facilitated a deeper exploration of their in-

dividual contributions and challenges. The resulting

categories are:

• Data Integration (DI): studies that explore meth-

ods and best practices for integrating data effec-

tively.

• Data Interoperability (DINT): articles dealing

with standards and protocols to ensure interaction

between different systems.

• Data Governance (DG): works dealing with

policies, processes, and frameworks for data man-

agement, control, and security.

• Practical Solutions (PS): studies that present

practical solutions to any of the previous cate-

gories, in simulated or real contexts.

3.3 Discussion

As shown in Table 1, of the 18 selected articles, eight

focus on practical experiments, nine explore data

governance technologies and architectures, ﬁve ad-

dress data integration technologies, and seven exam-

ine data interoperability technologies. Many articles

span multiple categories, reﬂecting the interconnected

nature of these areas, though each primarily empha-

sizes one focus. Only one study, by (Almeida and

Oliveira, 2024), comprehensively covers all mapped

areas, providing a holistic view of data governance,

integration, and interoperability.

The reviewed studies explore various aspects of

data governance, such as organizational structures

and frameworks, but practical examples are scarce.

For instance, (Miao et al., 2019), (Zanti et al., 2022),

and (Dur

ao et al., 2024) rely on simulated data, lack-

ing real-world validation. This highlights the need for

more empirical research to bridge theory and practice.

Additionally, studies on big data technologies often

overlook limitations and feasibility, reducing their ap-

plicability and hindering progress.

Studies such as (Miao et al., 2019), (Almeida

and Oliveira, 2024), (Feng et al., 2021), (Williamson,

2018), (Kawtrakul et al., 2021), (Hillman and Ganesh,

2019), (Kush et al., 2020), and (Li and Quinn, 2024)

focus on interoperability, emphasizing the logistics of

information access. While these studies validate pro-

cesses within speciﬁc scenarios, they often struggle

to replicate results in different contexts. Research on

integration and interoperability, such as (Rani et al.,

2023), (Otoo-Arthur and van Zyl, 2020), and (Mojun

and Tingmin, 2022), faces difﬁculties in establishing

consistent, universally applicable standards, compli-

cating efforts to achieve scalable and adaptable solu-

tions.

In the ﬁeld of interoperability, authors like (Li

and Quinn, 2024), (Rani et al., 2023), and (Hillman

and Ganesh, 2019) highlight blockchain technology

for its strengths in security, traceability, and atom-

icity. However, its effective application is hindered

by challenges such as the handling of data outside

the cryptographic environment and a lack of regula-

tions on data portability. These limitations restrict

blockchain’s broader adoption, emphasizing the need

for both technological advancements and regulatory

frameworks to address these gaps.

In general, studies like (Wang and Huang, 2021),

(Feng et al., 2021), (Williamson, 2018), and (Stahl

et al., 2023) explore data governance in data-driven

organizations and its broader impacts. Meanwhile,

works such as (Kawtrakul et al., 2021), (Almeida and

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

370

Table 1: Comparative Analysis of Related Works, Focus Areas, and Domains.

Related Work

Focus Area Domain

DI DINT DG PS Education Others

(Miao et al., 2019) ◦ • ◦ ◦ • ◦

(Otoo-Arthur and van Zyl, 2020) ◦ ◦ • • • ◦

(Mojun and Tingmin, 2022) ◦ ◦ ◦ ◦ • ◦

(Rani et al., 2023) ◦ • • ◦ • ◦

(Hillman and Ganesh, 2019) • ◦ • • • ◦

(Feng et al., 2021) • • • ◦ • ◦

(Wang and Huang, 2021) • ◦ ◦ • • ◦

(Kawtrakul et al., 2021) ◦ • ◦ • ◦ •

(Huang, 2023) ◦ ◦ • • • ◦

(Williamson, 2018) ◦ • ◦ ◦ • ◦

(Stahl et al., 2023) ◦ ◦ ◦ ◦ ◦ •

(Haneem et al., 2019) ◦ ◦ • • ◦ •

(Zanti et al., 2022) • ◦ ◦ ◦ ◦ •

(Dur

ao et al., 2024) • • ◦ ◦ ◦ •

(Kush et al., 2020) ◦ • ◦ ◦ ◦ •

(Astuti et al., 2024) ◦ ◦ • • • ◦

(Almeida and Oliveira, 2024) • • • • ◦ •

(Li and Quinn, 2024) ◦ ◦ • ◦ ◦ •

Oliveira, 2024), (Zanti et al., 2022), (Dur

ao et al.,

2024), (Astuti et al., 2024), (Huang, 2023), and (Ha-

neem et al., 2019) detail implementations in spe-

ciﬁc contexts, though their results remain constrained.

The lack of comprehensive quantitative and qualita-

tive analyses in these studies raises concerns about the

scalability and effectiveness of the proposed applica-

tions. Figure 3 outlines the key gaps and challenges

identiﬁed, highlighting four key areas for further in-

vestigation.

4 CHALLENGES IDENTIFIED

The literature analysis identiﬁed several challenges

hindering the implementation of data governance, in-

teroperability, and integration strategies across sec-

tors, including education. These challenges encom-

pass technical, regulatory, cultural, and institutional

barriers that complicate the adoption and scalability

of solutions. This section highlights key obstacles and

their implications for future research and practical ap-

plications. Addressing these issues can improve the

effectiveness and impact of data-driven initiatives.

Collaboration among stakeholders is vital for inte-

gration yet is rarely explored in-depth regarding pro-

cedures, challenges, and successes. Studies by (Miao

et al., 2019) and (Astuti et al., 2024) discuss govern-

ment open data initiatives, emphasizing the signiﬁ-

cant role of policies and non-technical stakeholders

in the success of these efforts, pointing to the need for

more comprehensive analysis of stakeholder collabo-

ration in data integration.

In addition to the previously identiﬁed gaps, the

lack of speciﬁc public policies for educational data

governance is a signiﬁcant barrier to progress. While

some government initiatives have promoted open data

policies, they often lack clear guidelines for managing

educational data, particularly in areas like interoper-

ability, integration, and data protection. This under-

scores the need for closer collaboration among edu-

cational institutions, governments, and stakeholders

to develop regulations and standards that can guide

effective data management, promote innovation, and

ensure ethical and secure practices.

Another critical issue is the lack of standardized

methodologies for measuring and evaluating the im-

pacts of data governance in education. While gen-

eral indicators exist in sectors like healthcare and

Data Governance in Education: Addressing Challenges and Unlocking Opportunities for Effective Data Management

371

Figure 3: Gaps and Challenges of Data Governance in Education.

business, the education sector requires tailored met-

rics that reﬂect its unique characteristics, such as im-

pacts on teaching quality, administrative efﬁciency,

and data protection for students and educators. Devel-

oping speciﬁc indicators would enable effective eval-

uation of current initiatives, facilitate comparisons,

and identify areas for improvement. These metrics

are essential for guiding policy development and driv-

ing continuous improvement in educational data gov-

ernance.

In the ﬁeld of interoperability, the adoption of

international standards must be strengthened to im-

prove connectivity and efﬁciency within education.

While these standards are well-established in other

sectors, their implementation in education remains

fragmented, facing challenges like compatibility with

legacy systems and lack of technical training. To ad-

dress these issues, it is crucial to promote consistent

use of these standards and invest in technical support

and capacity-building initiatives. These efforts will

help create a more integrated educational ecosystem,

enabling seamless data exchange and better collabo-

ration among institutions.

Additionally, awareness of ethics and security in

data governance must be raised. As educational in-

stitutions deal with increasing volumes of data, issues

such as privacy, consent, and responsible use become

even more critical. Awareness campaigns and the

inclusion of ethical guidelines in governance frame-

works can help mitigate risks and promote an organi-

zational culture focused on protecting sensitive data.

Emerging technologies like artiﬁcial intelligence

(AI) and blockchain present an opportunity to im-

prove educational data governance, with AI opti-

mizing data management and blockchain ensuring

security and transparency. However, their adop-

tion requires thorough feasibility studies and the de-

velopment of guidelines tailored to the education

sector’s needs, aligning with educational objectives,

ethics, and regulations. These challenges and oppor-

tunities emphasize the need for a focused research

agenda, involving collaboration among policymakers,

researchers, technologists, and educators, to establish

data governance as a key driver of digital transforma-

tion and innovation in education.

5 CHALLENGES FACING THE

BRAZILIAN EDUCATION

SYSTEM

In addition to analyzing the state of the art, this study

investigated whether the challenges identiﬁed in the

literature are present in the daily operations of the

Brazilian education system, focusing on data gover-

nance. An in-depth analysis of public policy docu-

ments from the Ministry of Education and oversight

reports on government policies and student ﬁnancial

incentives aimed to bridge the gap between theoreti-

cal challenges and real-world implications. Serving

over 47 million students across more than 178,500

schools in 5,570 municipalities (Inep, 2023b; Inep,

2023a), the Brazilian education system is one of the

largest and most complex globally. It is highly de-

centralized, with responsibilities shared among fed-

eral, state, and municipal levels, and its diverse ed-

ucational landscape spans early childhood, elemen-

tary, and high school institutions of varying sizes, re-

sources, and infrastructure.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

372

Figure 4: Overview of Basic Education in Brazil. Fonte:

(Inep, 2023b).

The diversity of the Brazilian education system

poses signiﬁcant challenges for the Ministry of Ed-

ucation (MEC) in formulating and implementing ef-

fective public policies. A major issue is the hetero-

geneity of data collection methods, reporting formats,

and digital infrastructure, leading to fragmented and

inconsistent datasets that hinder monitoring of indica-

tors such as attendance, performance, and resource al-

location. Disparities in technological capacity further

complicate data management, especially in rural and

underserved schools lacking reliable digital systems.

These challenges underscore the urgent need for a ro-

bust data governance framework to ensure data qual-

ity, interoperability, and integration, supporting equi-

table and evidence-based public policies.

• Data Quality. A major concern is the inconsis-

tency and unreliability of data, such as incomplete

records and outdated information, which hinder

decision-making and evidence-based policies.

• Interoperability. The lack of interoperability

leads to fragmented datasets across agencies, ob-

structing integrated analysis and efﬁcient resource

allocation.

• Traceability. The absence of traceability mech-

anisms complicates the monitoring of resources

and the veriﬁcation of policy objectives, making

accountability challenging.

The evidence from the state of the practice empha-

sizes the need for further research and practical so-

lutions to help the education sector implement effec-

tive data governance strategies. Challenges like frag-

mented data systems and inconsistent quality high-

light the importance of tailored frameworks for edu-

cation. Addressing these issues requires both theoreti-

cal advancements and actionable solutions that can be

scaled across diverse contexts, ultimately improving

transparency, accountability, and efﬁciency in man-

aging educational data.

6 CONCLUSIONS

This study explores the transformative potential of

data governance in driving digital transformation in

the education sector. Despite progress, several gaps

remain, including the lack of tailored public policies,

standardized metrics to evaluate impacts, and insuf-

ﬁcient technical training for implementing interoper-

ability standards. These challenges, compounded by

legacy systems, fragmented data sources, and regula-

tory inconsistencies, hinder the effective implementa-

tion of comprehensive data governance strategies.

Focusing on the Brazilian educational scenario,

the study identiﬁes challenges such as data hetero-

geneity, technological disparities, and a lack of trace-

ability, which obstruct integration, transparency, and

accountability. Addressing these requires context-

speciﬁc policies, frameworks, and a collaborative, in-

terdisciplinary approach involving policymakers, ed-

ucators, researchers, and technologists to develop

scalable and effective solutions.

The study emphasizes the importance of adapt-

ing data governance frameworks to the unique char-

acteristics of the education sector, promoting interop-

erability, and building technical capacity. Emerging

technologies like AI and blockchain can enhance data

quality, security, and transparency, further advancing

digital transformation. Ultimately, robust data gov-

ernance is essential for fostering equitable, efﬁcient,

and transparent educational ecosystems.

REFERENCES

Almeida, J. R. and Oliveira, J. L. (2024). Montra2: a

web platform for proﬁling distributed databases in the

health domain. Informatics in Medicine Unlocked,

45:101447.

Astuti, H. M., Wibowo, R. P., and Herdiyanti, A. (2024).

Towards the national higher education database in in-

donesia: Challenges to data governance implementa-

tion from the perspective of a public university. Pro-

cedia Computer Science, 234:1322–1331.

Bazaluk, O., Pavlychenko, A., Yavorska, O., Nesterova,

O., Tsopa, V., Cheberiachko, S., Deryugin, O., and

Data Governance in Education: Addressing Challenges and Unlocking Opportunities for Effective Data Management

373

Lozynskyi, V. (2024). Improving the risk management

process in quality management systems of higher ed-

ucation. Dental science reports.

Casta

neda, L. and Gourlay, L. (2023). Data ecosystems in

education: opportunities and challenges.

Chu, S. and Wang, Y. (2023). Application of data man-

agement capability maturity assessment model in the

automotive industry.

Curry, E., Auer, S., Berre, A. J., Metzger, A., Perez, M. S.,

and Zillner, S. (2022). Technologies and applications

for big data value. In Technologies and Applications

for Big Data Value, pages 1–15. Springer.

Dur

ao, L. F. C., Zancul, E., and Sch

utzer, K. (2024). Digi-

tal twin data architecture for product-service systems.

Procedia CIRP, 121:79–84.

Feng, Y., Chen, W., Zhang, Y., Yu, G., Liu, Y., and Xu, H.

(2021). Research on the architecture of smart cam-

pus system based on data middleground in univer-

sity. In 2021 IEEE 3rd International Conference on

Computer Science and Educational Informatization

(CSEI), pages 18–21. IEEE.

Haneem, F., Kama, N., Taskin, N., Pauleen, D., and Bakar,

N. A. A. (2019). Determinants of master data manage-

ment adoption by local government organizations: An

empirical study. International Journal of Information

Management, 45:25–43.

Hendrawan, F. R., Kusumasari, T. F., and Praditya, D.

(2023). (2) a comprehensive framework of role data

governance in ensuring data quality: Literature re-

view.

Henninger, A. (2022). Data Governance. Enablers, In-

hibitors, Practices, and Outcomes. PhD thesis,

Toronto Metropolitan University.

Hillman, V. and Ganesh, V. (2019). Kratos: A secure, au-

thenticated and publicly veriﬁable system for educa-

tional data using the blockchain. In 2019 IEEE Inter-

national Conference on Big Data (Big Data), pages

5754–5762. IEEE.

Huang, R. (2023). Research on technical path and practice

of digital transformation of colleges based on ipaas.

In Second International Conference on Digital Society

and Intelligent Systems (DSInS 2022), volume 12599,

pages 269–275. SPIE.

Inep (2023a). Censo escolar da educac¸

ao b

asica 2023: Re-

sumo t

ecnico. Accessed on January 20, 2025.

Inep (2023b). Mec e inep divulgam resultados do censo

escolar 2023. Accessed on January 20, 2025.

International, D. (2017). DAMA-DMBOK: Data manage-

ment body of knowledge. Technics Publications, LLC.

ISACA (2018). COBIT® 2019 Framework: Introduction

and Methodology. ISACA.

Kawtrakul, A., Chanlekha, H., Waiyamai, K., Kangka-

chit, T., d’Orazio, L., Kotzinos, D., Laurent, D., and

Spyratos, N. (2021). Towards data-and-innovation

driven sustainable and productive agriculture: Bio-

agri-watch as a use case study. In 2021 IEEE Inter-

national Conference on Big Data (Big Data), pages

3407–3415. IEEE.

Kshirsagar, O. R. (2024). Big data analytics: Future of

database. International Journal for Research in Ap-

plied Science and Engineering Technology.

Kush, R. D., Warzel, D., Kush, M. A., Sherman, A.,

Navarro, E. A., Fitzmartin, R., P

etavy, F., Galvez, J.,

Becnel, L. B., Zhou, F., et al. (2020). Fair data sharing:

the roles of common data elements and harmoniza-

tion. Journal of biomedical informatics, 107:103421.

Li, W. and Quinn, P. (2024). The european health data

space: An expanded right to data portability? Com-

puter Law & Security Review, 52:105913.

Miao, Z., Zhai, J., Lin, Y., and Yuan, C. (2019). A compar-

ative study of open education data between china and

uk. In 2019 16th International Conference on Service

Systems and Service Management (ICSSSM), pages 1–

5. IEEE.

Mojun, X. and Tingmin, L. (2022). Architecture optimiza-

tion and core code de-redundancy of educational man-

agement information system in higher vocational col-

leges under the background of data governance algo-

rithm. In 2022 4th International Conference on Inven-

tive Research in Computing Applications (ICIRCA),

pages 532–535. IEEE.

Otoo-Arthur, D. and van Zyl, T. L. (2020). A scalable

heterogeneous big data framework for e-learning sys-

tems. In 2020 international conference on artiﬁcial

intelligence, big data, computing and data communi-

cation systems (icABCD), pages 1–15. IEEE.

Rani, P. S., Janani, J., Krithika, C., Keerthika, S. L., and

Joshika, J. (2023). Blockchain-based ﬁle tracking

and data management system for education sector. In

2023 International Conference on Advances in Com-

puting, Communication and Applied Informatics (AC-

CAI), pages 1–6. IEEE.

Soares, S. (2010). The IBM data governance uniﬁed pro-

cess: driving business value with IBM software and

best practices. MC Press, LLC.

Stahl, B., H

ackel, B., Leuthe, D., and Ritter, C. (2023).

Data or business ﬁrst?—manufacturers’ transforma-

tion toward data-driven business models. Schmalen-

bach Journal of Business Research, 75(3):303–343.

Volkov, A., Otbetkina, T., and Vidmanova, A. (2023). Im-

proving the effectiveness of data-driven learning man-

agement. International journal of social science and

human research.

Wang, Z. and Huang, R. (2021). Research on the implemen-

tation path and practice of data driven university gov-

ernance modernization—taking shandong youth col-

lege of political science as an example. In 2021 11th

International Conference on Information Technology

in Medicine and Education (ITME), pages 500–504.

IEEE.

Williamson, B. (2018). The hidden architecture of higher

education: Building a big data infrastructure for the

‘smarter university’. International Journal of Educa-

tional Technology in Higher Education, 15:1–26.

Zanti, S., Berkowitz, E., Katz, M., Nelson, A. H., Burnett,

T., Culhane, D., and Zhou, Y. (2022). Leveraging in-

tegrated data for program evaluation: Recommenda-

tions from the ﬁeld. Evaluation and Program Plan-

ning, 95:102093.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

374