Data-Driven Culture Requires Overcoming Data Governance and Data
Literacy Challenges
Carlos Alberto Bassi
1 a
, Jacqueline David-Planas
2 b
, Solange Nice Alves-Souza
2 c
and Luiz Sergio de Souza
3 d
1
Information System, Escola de Artes, Ci
ˆ
encias e Humanidades, Universidade de S
˜
ao Paulo, S
˜
ao Paulo, Brazil
2
Departamento de Engenharia de Computac¸
˜
ao e Sistemas Digitais, Escola Polit
´
ecnica,
Universidade de S
˜
ao Paulo, S
˜
ao Paulo, Brazil
3
Faculdade de Tecnologia de S
˜
ao Paulo, S
˜
ao Paulo, Brazil
Keywords:
Data Governance, Data Literacy, Data-Driven Culture, Data-Driven Organization, Artificial Intelligence,
Scoping Review.
Abstract:
Organizations are progressively working towards becoming data driven. To achieve this, they need to cultivate
a comprehensive culture grounded in behaviors, practices, and training, while also ensuring governance in
data sharing, collection, policies, tools, and processes. Data Governance (DG) and Data Literacy (DL) offer
essential resources to support this cultural shift. However, the implementation of DG and DL faces a variety
of organizational challenges. This study aims to identify and analyze these challenges, with a focus on their
intersections and implications for building a sustainable Data-Driven Culture (DDC). The challenges for this
research were derived from a scoping review of the literature of case studies on the implementation of DG
and DL, complemented by data collected through a completed web form and interviews with professionals
involved in the DG initiatives. The analysis revealed a significant overlap in the challenges of DG and DL,
highlighting the importance of integrated strategies. Organizations that prioritize and address these shared
challenges will significantly accelerate the development of a robust DDC and enhance the value derived from
data.
1 INTRODUCTION
Data-Driven Culture (DDC) is understood as a pattern
of behaviors and practices among a group of people
who share the conviction that the availability, com-
prehension, and use of data and information are crit-
ical to their organization’s success (Chaudhuri et al.,
2024).
Becoming data-driven involves developing capa-
bilities, tools and, crucially, a culture of acting based
on data (Anderson, 2015).
For the past decade, becoming data-driven has
been consistently identified as a top priority for or-
ganizations. Empirical evidence indicates significant
benefits: data-driven companies demonstrate, on av-
erage, 5% higher productivity and 6% greater prof-
a
https://orcid.org/0009-0001-2524-7478
b
https://orcid.org/0000-0001-7661-0401
c
https://orcid.org/0000-0002-6112-3536
d
https://orcid.org/0000-0002-7855-0235
itability compared to their competitors (Storm and
Borgman, 2020).
The findings suggest that Data Governance (DG)
and Data Literacy (DL) precede DDC. Consequently,
the research offers a significant theoretical contribu-
tion by providing empirical evidence and elucidating
the role of DG as a precursor to DDC. Interestingly,
despite the recognized importance of DG in manag-
ing data relevant to organizational decision-making,
current literature lacks a clear understanding of the
nomological network linking DG constructs and other
analytical capabilities (Fattah, 2024).
DG encompasses the exercise of authority, con-
trol, and shared decision-making (planning, monitor-
ing, and execution) in the management of data assets
(Data Management Association, 2017). DG estab-
lishes policies, standards, processes and frameworks
with the definition of roles and responsibilities that
define and enforce the rules of engagement, decision
rights, and accountability for the effective manage-
ment of information assets (Putro et al., 2016; Al-
Bassi, C. A., David-Planas, J., Alves-Souza, S. N. and Sergio de Souza, L.
Data-Driven Culture Requires Overcoming Data Governance and Data Literacy Challenges.
DOI: 10.5220/0013670200004000
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2025) - Volume 2: KEOD and KMIS, pages
349-356
ISBN: 978-989-758-769-6; ISSN: 2184-3228
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
349

Dossari and Sumaili, 2021). According to Jim and
Chang, the DG functions as a guided framework that
should address organizational objectives and business
processes (decision-making, culture and values), le-
gal and compliance obligations (accountability), risk
management (privacy and security), data management
(data quality and metadata), and designated roles of
individuals (data stewards and data owners) (Jim and
Chang, 2018).
DL characterizes the ability to collect, manage,
evaluate, and apply data critically (Ridsdale et al.,
2015) and is often defined as the ability to transform
information into actionable knowledge and practices
through the collection, analysis, and interpretation
of diverse data types (Koltay, 2015). DL improves
purpose-specific data handling skills, encompassing
the ability to select, clean, analyze, visualize, critique,
interpret, and communicate data narratives, enabling
effective data use (Wolff et al., 2016) as a founda-
tion for organizational value creation (Ansyari et al.,
2022).
Cultural challenges, including a lack of perceived
data value and insufficient understanding and train-
ing in relevant concepts, technologies, and best prac-
tices, significantly impact the implementation of DG
in organizations across different market segments and
countries (Bassi and Alves-Souza, 2023).
Many organizations establish one initiative for the
implementation of DG and another for building DL
to implement DDC. However, many of the challenges
faced by each of these initiatives are common and,
when identified and addressed, can significantly re-
duce efforts and accelerate the achievement of the
goal of becoming a data-driven organization.
This study addresses the following research ques-
tions:
• How do DG and DL relate?
• What are the challenges in implementing DG and
DL?
• Are there shared challenges between DG and DL?
• DG and DL contribute to creating DDC?
2 CHALLENGES
DATA-DRIVEN CULTURE - Organizational cul-
ture profoundly influences all aspects of action,
shaping decisions related to products, personnel,
customers, measurements, and resource allocation.
While certain leaders attempt to incorporate cultural
norms through advanced technology, others may op-
pose cultural transformation. Furthermore, a change
in the executive mindset requires promoting a DDC
by establishing governance strategies and mecha-
nisms, as addressed by DG, and by fostering analyti-
cal skills, as supported by DL (Fattah, 2024).
Study conducted identified that there are two en-
abling factors of a DDC: (i) mandatory prerequisites
include an established DG and access to high-quality
data, as their absence diminishes trust in analytically
derived business insights and obstructs the adoption
of a DDC and (ii) active involvement of top-level
management is critical to develop a strategic approach
to establish a DDC (Berndtsson et al., 2018).
The complexity of DDC, as it is built on organiza-
tional cultures, lies in the challenge of achieving con-
sistency with decision-making principles (Holsapple
et al., 2014).
DDC encompasses established norms, values, and
behavioral patterns of an organization, leading to
a structured methodology to create, collect, con-
solidate, and analyze data. This involves mak-
ing data available to relevant stakeholders, expand-
ing their application for business development and
decision-making, and facilitating management anal-
ysis, the acceptance of learning, and knowledge shar-
ing. Moreover, DDC reflects an inclination towards
the adaptation and improvement of work methodolo-
gies and decision-making informed by data (Duan
et al., 2020).
DATA GOVERNANCE - Organizations face signif-
icant challenges in implementing comprehensive and
efficient DG programs. Frequently, professionals in-
volved in DG implementation projects lack adequate
knowledge on the necessary activities, assignment of
responsibilities, interdependencies between these ac-
tivities, and the repercussions of their inadequate ex-
ecution (Bassi and Alves-Souza, 2023).
According to Gartner’s projections, by 2025, a
significant majority (80%) of organizations that seek
to scale up their digital business are likely to fail with-
out the adoption of a contemporary strategy for DG
and analytics (Fattah, 2024).
The complex nature of DG implementation, which
requires long-term commitment and continuous en-
gagement, typically leads organizations to develop a
series of actions to realize these objectives (Zhang
et al., 2022).
Organizations have faced practical challenges in
mobilizing for the adoption of DG. Data inventory re-
mains a cumbersome process, the potential for value
creation is often seen as abstract, and the necessity of
DG investment is generally acknowledged only after
significant regulatory pressure or data breaches (Ben-
feldt et al., 2020).
A comprehensive analysis of the scientific and
KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems
350

practice-based literature indicates a fundamental lack
of understanding about the activities essential for
the establishment of a DG program (Alhassan et al.,
2019).
DATA LITERACY - DL represents an approach that
strengthens goal-oriented data handling skills, which
encompasses not only technical competencies such as
data preparation and analysis, but also the ability to
communicate insights and apply data meaningfully in
decision making (David-Planas et al., 2023). These
practical competencies are embedded within broader
personal, professional, and social contexts and must
take into account ethical issues, subjectivity, and bias.
Connecting data with value involves a personal
journey that requires changes in behavior and thought
processes, contextual awareness, clarity of pur-
pose, critical reasoning, and motivation (Smolnikova,
2022). Extrinsic factors such as collaboration, ef-
fective communication, and engagement with change
management are also essential to ensure consistency
of this transformation. Lacking the ability to contex-
tualize, data analysis efforts are often misdirected or
unfocused (Matthews, 2016).
Despite increasing recognition of the importance
of DL, its practical development and evolution still
face significant challenges. For instance, many pro-
fessionals and organizations still lack the necessary
competencies to extract value from data, thus limiting
their potential to generate significant benefits (Frank
and Walker, 2016). Furthermore, the absence of a
well-established DDC within organizations, coupled
with a lack of commitment from their leaders in this
endeavor, contributes to the difficulty in implement-
ing efficient data-based decision-making practices.
The Global Data Literacy Benchmarking 2023
(Data to the People, 2023) reveals that despite in-
creased awareness of the topic, few professionals pos-
sess the skills to teach or assist others regarding the
importance of a data-driven approach. This scenario
underscores the urgency of promoting DL as a funda-
mental organizational competency that can be instru-
mental in creating competitive advantages and maxi-
mizing the value extracted from available data (Smol-
nikova, 2022).
3 RESEARCH
DATA GOVERNANCE - This stage involved con-
ducting a Scoping Review (SR) of case studies to
identify the main challenges facing organizations in
the implementation of DG projects. The entire pro-
cedure followed, the analysis of the identified chal-
lenges, and the main findings is detailed in the article
’Challenges to Implementing Effective Data Gover-
nance: A Literature Review’ (Bassi and Alves-Souza,
2023). A supplementary SR conducted, focusing on
theses and dissertations that also addressed case stud-
ies of the implementation of the DG project, employ-
ing the same systematization adopted. Fifty-eight
challenges were identified and subsequently catego-
rized into 11 groups, displayed in Table 1.
Table 1: Challenges in implementing DG and DL.
Scoping Profes-
Review sionals
Category Challenge
DG DL DG DL
(58) (35) (36) (26)
Data
o Data Silos X X X X
o Data Quality X X X X
o Data Sanitization X
o Data Standardization X
o Data Volume X X X X
o Life Cycle X X
o Metadata X
o Master Data X
o Data Collection X
o Traceability X
o Data Integration X X
o Data Inventory X
o Assignment of Responsibility / interest X X X
o Existing / Updated Documentation X X
o Data Diversity X X
o Established Quality Criteria X
o Identification of Relevant Data X X
Security
o Consent X X
o Confidentiality / Privacy X X
o Sharing X X
Policies
o Data Governance X X
o Data privacy and security X X
o Data quality management X
o Conflicts of interest X
o Established / Updated Policies X X
o Well-defined / Clear Policies X X
o Existing / Updated Documentation X
Process
o Data quality control and monitoring X X
o Degree of harmonization X X
o Variations in processes X X
o Established / Updated Processes X X
o Existing / Updated Documentation X
o Well-defined / Clear Processes X X
Infrastructure
o Compatibility across platforms and stan-
dards
X X
o Use of external infrastructure X
o Alignment with application architecture X
o Fragmented architecture with legacy sys-
tems
X
o Standardized Big Data systems X
Technologies
o Existing / Updated Documentation X X X
o Diversity of Technologies / Environ-
ments / Systems
X
o Tool Support X X X
o Obsolete / Inadequate Infrastructure /
Systems
X
o Implementation of Adjustments in In-
frastructure / Systems
X
Organizational
o Existence of a Responsible Area / De-
partment
X X X X
o Employee Turnover X X
o Organization Size X X
o Decentralized Structure X X
o Growth speed X
o Competitive edge X X
o Expectation of short-term results X X
o Alignment / collaboration between orga-
nizational units
X X
o Local practices X X
o Compliance with Laws / Regulations X X
o Management Support X X
o Well-defined / Clear Strategy X X
o Constant Organizational Changes X X
o Financial Investment X X
o Human Resources X X
Data-Driven Culture Requires Overcoming Data Governance and Data Literacy Challenges
351

Table 1: Challenges in implementing DG and DL (cont.).
Scoping Profes-
Review sionals
Category Challenge DG DL DG DL
Cultural
o Perception of Value / Benefits X X X X
o Understanding / Cultute / Knowledge /
Training in Concepts and Technologies in-
volved
X X X X
o Understanding / Training in Security and
Privacy
X X
o Knowledge of frameworks and best prac-
tices
X X
o Collaboration / information sharing with
communities / third parties
X X
o Resistance X X
o Employee Engagement X X
o Experience X X
o Alignment / Communication X X
Frameworks
o Adapted to the needs of the organization X X
o Capacity to promote data sharing X
Project
o Organization-wide approach X X
o Existing and active Management Com-
mittee
X
o Engagement / commitment and/or resis-
tance to the project / changes by those in-
volved
X X
o Experience / knowledge of the resources
involved
X X
o Understanding the activities required for
a Data Governance Program
X
o Perception of the benefits of conducting
the project
X X
o Limited resources and deadlines X X
o Planning / Prioritization X X
Regulations
o Policies / regulations on data processing X X
o Policies / rules of control / regulation of
the segment in which the organization op-
erates
X X
o Antitrust X
o Public interest in data X
o Transparency in the use of data X X
o Privacy and Data Security X X
External
Environment
o Political and/or institutional instability X X
o Political support X X
End of Table 1
Subsequently, within the scope of research to
specify a guide with practical and coordinated actions
that help organizations implement DG, data were
collected through a web-based questionnaire admin-
istered to professionals from organizations that in-
tend to implement or are currently conducting DG
projects. The aim was to identify the main challenges
that prevent organizations from conducting DG ac-
tions and the main challenges faced by organizations
when conducting DG actions.
It was assumed that the research population
should encompass organizations in different stages
of DG implementation (initial stage, advanced stage,
and planning), of varying sizes (small, medium, and
large), and from diverse market segments.
To ensure comprehensive representativeness, a
minimum population of 81 participants was esti-
mated. This estimate accounted for at least 3 orga-
nizations for each of the 3 different sizes, each of
the 3 different implementation stages, and each of the
3 different market segments. According to the Na-
tional Education Association, the necessary sample
size for research should be 82% of the defined pop-
ulation (Krejcie, 1970).
There was effective participation of 67 profession-
als, with the following profile (Bassi et al., 2024):
• More than 75% of the professionals have more
than 3 years of experience in DG.
• 85% work in medium to large-sized organizations.
• Professionals work in organizations across a wide
range of market segments.
Thirty-six challenges were identified from inter-
views with professionals and subsequently grouped
into 7 categories, as presented in Table 1.
DATA LITERACY - DL involves critical thinking
and the development of multifaceted competencies
that integrate data and contextual understanding, both
at individual and collective levels, with the goal of
generating meaningful impact in organizations (Kris-
tiana et al., 2023; Prado and
´
Angel Marzal, 2013). A
value-oriented approach to DL goes beyond technical
or business skills by emphasizing the human dimen-
sion of data use.
(David-Planas et al., 2023) present preliminary
findings from an ongoing scoping literature review
aimed at exploring the challenges and critical suc-
cess factors related to the promotion of DL. As part of
this research, the authors posit that the connection be-
tween data and value necessitates a multifaceted ap-
proach involving, for example, behavioral and mind-
set shifts, continuous motivation, as well as the de-
velopment of technical skills and contextual under-
standing of data use. Furthermore, extrinsic factors,
such as collaboration, effective communication, re-
sources, institutional sponsorship, and change man-
agement, are equally essential to ensure consistent
and sustainable organizational transformation (DiLab
et al., 2022; McCosker et al., 2022; Ansyari et al.,
2022; Mandinach and Jimerson, 2016). These find-
ings were further analyzed considering the challenges
of DG outlined in Table 1, highlighting the interde-
pendence between effective DL initiatives and robust
DG practices.
4 ANALYSIS
The successful implementation of a DG program will
create a DDC and, consequently, make the organiza-
tion data-driven (Bassi et al., 2024).
Evaluating solely based on the quantity of DG and
DL challenges identified from the SR that will impact
the construction of DDC, 60.24% of the DG chal-
lenges are also DL challenges, as illustrated in Figure
1. When analyzing the challenges based on the val-
idation of professionals, this percentage increases to
72.22%, as illustrated in Figure 2.
Figure 3 shows how frequently each challenge re-
lated to DG implementation was mentioned in the
KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems
352

Figure 1: DDC Challenges (SR) [quantitative].
Figure 2: DDC Challenges (Professionals) [quantitative].
case studies analyzed in the DG scoping review. For
clarity, only challenges cited more than once are in-
cluded. Perception of the value of data as an asset,
Understanding/Training in Concepts and Technolo-
gies involved in DG and Engagement/commitment
and/or resistance to the project/changes by those in-
volved constitute key challenges and are directly as-
sociated with DL and, consequently, also with data
culture.
Figure 3: References of DG challenges from SR.
Figure 4 presents the number of references to
the challenges in the implementation of DG ac-
cording to the survey among the DG profession-
als. For improved visual clarity, only the chal-
lenges referenced more than ten times are included.
According to the evaluation of DG professionals,
the challenges directly associated with culture such
as Culture/Knowledge/Training, Employee Engage-
ment, Perception of Value/Benefits, Resistance and
Alignment / Communication, tend to have greater rel-
evance due to the direct experience gained during DG
implementation.
Figure 4: Reference of DG challenges from Professionals.
Considering the number of references to the chal-
lenges, 77, 27% of the challenges of DG are also chal-
lenges of DL in SR case studies, as illustrated in Fig-
ure 5, and this percentage increases to 85,61% as il-
lustrated according to the survey of DG professionals
in Figure 6.
Figure 5: DDC Challenges (SR) [reference].
Figure 6: DDC Challenges (Professionals) [reference].
Data-Driven Culture Requires Overcoming Data Governance and Data Literacy Challenges
353

5 DISCUSSION
One solution identified in the literature for the suc-
cessful implementation of DG is to train and improve
DL for all personnel across organizations involved in
the project (Kawtrakul et al., 2021)
The successful implementation of a DG program
in organizations is significantly dependent on cul-
tural aspects. These aspects comprise a framework
of values, beliefs, and behaviors that dictate the or-
ganization’s business practices and its relationships
with customers and partners (Bassi and Alves-Souza,
2023).
A DDC should be developed through the partici-
pation of the entire organization, fostering employee
interest and motivation. Furthermore, DDC must be
implemented in an integrated way throughout the or-
ganization, rather than isolated silos (Anton et al.,
2023).
Strong, top-down data leadership is essential for
data-driven organizations. This leadership should in-
spire and promote a data-driven culture, actively driv-
ing and supporting all aspects of the analytics value
chain, from data collection to data-driven decision-
making and institutional learning (Anderson, 2015).
DG and DL are vital to encouraging and enabling
data analysis within DDC. DG establishes decision-
making rights and accountability to ensure appropri-
ate behavior in managing organizational data, analyt-
ics, and information assets. Integrating DG with the
overall business strategy and aligning it with data and
analytical assets is critical for stakeholders. In the era
of Big Data (BD), DG involves setting up and adher-
ing to structures, rules, policies, and controls for data
analysis activities (Fattah, 2024; Downes, 2023; Ifen-
thaler et al., 2021; Mandinach and Jimerson, 2016).
Human-centered and business value-driven DL
aim to shift mindsets, transform organizational cul-
ture, and pave the way for the establishment of DG
(Oliver et al., 2024; DiLab et al., 2022; Kristiana
et al., 2023; Dangol and Dasgupta, 2023).
The significant interrelation between the chal-
lenges encountered in the implementation of DG and
DL raises the question of which methods or technolo-
gies can facilitate their management and accelerate
the establishment of a DDC.
In this scenario of rapid change and the growing
need for agile data-driven decision-making, Artificial
Intelligence (AI) has emerged as a potentially effec-
tive solution. The following examples illustrate their
practical application:
• Traditional DG methodologies often depend
on manual processes that are inherently time-
consuming and susceptible to errors. In contrast,
AI-powered solutions automate a substantial por-
tion of these tasks, thus enhancing efficiency and
minimizing human fallibility. The capacity for
real-time data analysis and continuous monitor-
ing facilitates quicker responses to anomalies and
security threats, a capability often unattainable to
the same degree with conventional approaches.
Moreover, AI’s ability to process large volumes
of data in real time and discern intricate patterns
offers a distinct advantage over traditional meth-
ods (Azeroual, 2024).
• Generative models facilitate metadata capture,
data lineage tracing, and enforcement of business
rules involved in DG. In addition, they offer rec-
ommendations for data classification, access con-
trol mechanisms, and privacy compliance proto-
cols, ensuring the adherence to corporate policies
and the fulfillment of legal requirements (Sug-
ureddy, 2023).
• The use of AI improves the agility, adaptability,
and intelligence of DG frameworks, enabling or-
ganizations to proactively address the complexi-
ties and challenges inherent in contemporary data
environments. AI automating processes such as
quality inspections, metadata management, and
compliance monitoring, thereby enhancing effi-
ciency and consistency. Through advanced func-
tionalities, including anomaly detection, deci-
sion support, and Natural Language Processing
(NLP), AI algorithms improve an organization’s
data classification, tagging, and monitoring capa-
bilities (Yandrapalli, 2024).
• The technological influence on data-driven
decision-making will intensify as Machine Learn-
ing (ML) and AI intelligence systems provide
recommendations derived from big data, includ-
ing student keystrokes, progress, and learning
outcomes recorded in educational platforms
(Henderson and Corry, 2021).
• The integration of AI into Education enables the
maximization of data utilization across various
applications, such as data classification, chatbot
deployment, academic performance assessment,
dropout prediction, and learning personalization.
This development signifies a substantial effort
to improve efficiency, inform decision-making,
and generate value within the educational sector
(Dewi et al., 2024).
• The use of AI-driven tools not only sup-
ports inquiry-based learning by guiding learners
through complex topics in ways that are cus-
tomized to their cognitive pathways, thereby pro-
moting the development of DL and making the
KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems
354

learning process more engaging (Picasso et al.,
2024).
• AI enhances decision-making by extracting value
from large datasets to identify trends, predict mar-
ket movements, and optimize investment strate-
gies, particularly in financial contexts (Picasso
et al., 2024). It enables automated trading, im-
proves credit risk assessments, and increases op-
erational efficiency, reducing human error, and
supporting more accurate data-driven decisions.
The successful deployment of AI systems requires
a workforce that is both competent and adequately
trained. Consequently, organizations must consis-
tently invest in the continuous education of their staff
to equip them with the required technical skills and
knowledge (Azeroual, 2024).
6 CONCLUSION
The contribution of this research was to present the
main challenges that organizations faced in conduct-
ing DG and DL implementation projects and for aca-
demic research, and to the extent to which these two
disciplines are directly linked in the formation of a
DDC.
Orienting and prioritizing the shared challenges of
DG and DL will significantly contribute to the cre-
ation of a DDC. To achieve this, it will be necessary
to coordinate actions and resources for the implemen-
tation of DG and DL, thereby realizing the benefits of
becoming a data-driven organization.
The use of AI-based solutions will address many
of the shared challenges identified in DG and DL,
thereby facilitating the development of DDC.
One limitation in conducting this research was the
absence of validation of DL challenges with experi-
enced professionals from organizations that intend to
implement or are currently conducting DL projects.
A significant opportunity for future research is to per-
form a validation with specialized DL professionals.
Potential future work would involve investigating
the effective utilization of AI in mitigating the chal-
lenges associated with DG and DL in practical con-
texts and assessing its contribution to the establish-
ment of a DDC.
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